**Configuring R2Client**

I can use `R2Client` for configuration, and it seems I can customize the bucket names using its config options. I could create `R2Client(bucket_type='source')` and `R2Client(bucket_type='production')`. If the goal is to differentiate 'test' from '1-cleaned-data', this would work seamlessly as long as environment variables are set. The script could also use `load_dotenv` to load from a `.env` file.

Additionally, I'll provide flags for `--source-bucket`, with a default of `test`, and maybe `--source-prefix` starting at `'podcasts/'`, but I'm not entirely sure about that. The user mentioned they pushed raw audio files into the test bucket, so I want to keep things flexible, perhaps allowing for pattern detection. Yet, auto-detecting common prefixes adds some complexity I need to consider.


**Supporting command-line options**

I'm thinking of keeping things simple while still providing support for various features. For example, I could have a `--source-ext` list that defaults to common formats but allows users to specify none for flexibility. The `--dest-bucket` will default to `1-cleaned-data`, and maybe the `--dest-prefix` could default to an empty string or use an environment variable for greater customization.

It might be useful to have a `--dest-ext` list, defaulting to `.tar`, and an optional `--id-regex`. Also, I want the output file path for missing IDs with an option for extra output.

I'll introduce a `--format` and possibly a `--limit` for quicker tests, while `--no-validate-id` will skip filtering by YouTube ID patterns. I'll plan to list keys in each bucket using a paginator, parsing IDs, checking extensions, counting the scanned keys and extracted IDs, then compute the difference and write to a file.


**Estimating operation costs**

I think we should print out the estimated operation cost and time for the user. I can calculate approximate list operations based on the number of scanned keys (pages), although I'm unsure about the exact object count. Each paginator page probably includes 'KeyCount', and I can count the pages.

While I can show the approximate cost based on the pricing, I want to be cautious. Since Cloudflare R2 charges for Class A operations, listing a bucket falls under that, but the cost is minimal. I could provide a formula for calculating it, like `(pages_source + pages_dest) / 1_000_000 * $0.36`, and reassure the user that the costs are low.

Given they might have a high object count, I think including an explanation with an optional script would make sense since their query sounds more conceptual.


**Checking for guidelines**

I need to keep my final answer concise, ideally under ten lines. If I implement the script, I'll want to include the path and command to run it, including arguments.

First, I should check if the repository has any existing `scripts` for auditing; there might be guidelines or a style to follow. I want to search for the `AGENTS.md` file in the repo's root for additional instructions. It looks like the user provided some guidelines from that file, but it only covers skills and doesn't mention code style.

I think it’s worth checking for any other `AGENTS.md` files in the `scripts` or `src` directories, so I’ll search for those.


**Determining minimal updates**

I think we’re okay with the requirements already in place. I might consider updating the README or documentation, but I’m not sure it’s necessary. I want to identify a minimal patch, such as adding `scripts/diff_r2_buckets.py` for usage. We could also update `src/r2_client.py` to include `iter_keys` with pagination, but maybe it’s not needed since the script could build boto3 clients directly. Using `R2Client` seems simpler, but we should accept bucket names explicitly for clarity.


Output:
Exit code: 0
Wall time: 1.1 seconds
Output:


Summary: auto

## Skills
A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.
### Available skills
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
### How to use skills
- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.


**Planning R2 Script**

I’m considering a plan with 3-4 steps: inspecting the current R2 utilities, implementing a diff script, and then running a quick lint check. But do we really need to change the code? Maybe not, but if we do, running `python -m py_compile` or using `--help` to check for errors is a good idea, and it's easy to do without the network.

I’ll draft a script to compute missing video IDs between two R2 buckets, providing necessary defaults and usage examples.


**Deciding on Prefixes**

I’m thinking about which default prefixes to use in the script. They mentioned pushing all raw audio files into the test bucket, maybe at the root level. However, the code uses 'podcasts/', so I might default to an empty prefix to ensure a scan of the whole bucket, but that could take time.

For efficiency, using 'podcasts/' helps avoid irrelevant objects, yet it risks missing files in the root.

I could allow multiple prefixes, defaulting to '' and including common ones like 'podcasts/' and 'videos/'. Alternatively, I could read the environment variables `R2_SOURCE_PREFIXES` or `R2_SOURCE_PREFIX` to detect them. They have a function to handle this in `download.py`, so I might consider allowing `--source-prefixes` as a mandatory option or provide defaults, though users may find that complex.


**Designing the Script for Video ID Extraction**

I think I can create a script that scans all keys and extracts video IDs while ignoring non-matching ones, which simplifies things. Setting the default `source_prefix` and `dest_prefix` to empty strings will allow for safe video ID validation.

Sure, scanning all 536k objects could be a lot, but it should still work. While there might be non-video objects, that’s probably rare. To enhance the process, I’ll also incorporate R2Client prefix handling, so if `R2_PREFIX` is set, it can still find keys under that prefix. Providing optional `--source-prefix` and `--dest-prefix` will give users flexibility if they want to narrow down scanning.


**Adding Extension Filters**

I want to include `--source-extensions` and `--dest-extensions` options for filtering by file types. In the raw bucket, there might be various extensions like `.webm` or `.mp4`, but we could encounter `.json` metadata files that might match during video ID extraction, which could lead to errors.

To minimize false positives, I think it’s best to filter by known extensions, requiring an extension from an allowed list. If multiple types are present, I can allow users to input a list. I could even use regex to ignore certain extensions, like `.json`, but it might not be strictly necessary. In cases with unusual key formats such as `<video_id>_audio.wav`, the base name extraction might not yield accurate results.


**Creating Video ID Extraction Logic**

I need to ensure that our video ID extraction logic aligns with their naming conventions. Since they mentioned pushing raw audio files, I can keep things simple. However, to be more robust, I want the extraction to find the YouTube ID pattern anywhere in the key, not just the base name. Using regex could help catch this pattern effectively.

For file keys like `podcasts/VIDEO.webm`, I can extract `VIDEO`, and for `raw/VIDEO_original.mp3`, I might need to use regex to capture the first 11-character ID pattern. This way, I can manage any embedded IDs or file suffixes while controlling potential matches by enforcing strict boundaries.

To implement this, I’ll establish a function to strip directory parts from the names and identify valid IDs based on the allowed character set. Ultimately, I need to ensure I match the correct ID after validating the naming scheme accurately, considering separators like underscores or dashes while still returning valid video IDs.


**Defining Video ID Extraction and Bucket Handling**

I'm thinking about how to define separators for video IDs in filenames, especially when dealing with characters like hyphens and underscores. Since YouTube IDs might include these characters, I want to ensure my regex captures valid patterns.

I'd prefer to check the base stem for clear matches, treating keys with hyphens or underscores carefully. I’m also considering providing a simple way to differentiate bucket types and names in our script.

To streamline things, I'll implement a function to resolve bucket names and set appropriate defaults for source and destination buckets, as well as extension filters. This should help avoid confusion while accommodating various file types effectively!


**Implementing Video ID Extraction and Bucket Diffing**

I'm considering the need for file extensions when handling video IDs, especially since audio and video formats can vary. It's safer to set defaults that encompass common YouTube file extensions, including `.wav` and `.flac`, while allowing regex for ID extraction to be independent of extensions.

I’m thinking about how to effectively manage bucket prefixes, enabling users to specify multiple prefixes. I want to implement flexible options for extracting YouTube IDs while keeping the overall logic straightforward.

Additionally, I’ll include default configurations for the script to ensure everything runs smoothly, focusing on clarity in the command-line interface.


**Considering Output Options and Progress Tracking**

I'm thinking the user may want to see a list of missing IDs, but with a large volume like 20k, it's probably better to write them to a file. I can also include an option to print the first N IDs for convenience.

Tracking progress is important too, and while using a `tqdm` progress bar sounds nice, I want to keep dependencies minimal. I'll aim to implement basic progress prints every 100k keys and consider falling back to `tqdm` if it's available. I should check the requirements to ensure `tqdm` is listed.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
41:tqdm


Summary: auto


**Evaluating S3 region settings**

I’m thinking about how the region might be set when using boto3 with S3. If `region_name` is `None`, it could default to 'us-east-1'. For custom endpoints like R2, maybe it should use 'auto'. I’m not fully certain about that. Cloudflare might expect 'auto', but the existing code works, so I'll go with it. Now, I need to list keys using a paginator function, which ensures we can loop through multiple pages efficiently.


**Defining extension normalization**

I’m creating a function to normalize file extensions by processing a list of raw strings. I want to split them by commas and ensure they start with a dot, converting everything to lowercase for consistency. Next, I also need to handle prefixes, interpreting an empty string as a request to scan the entire bucket and allowing for comma-separated values.

For each key, if the extensions aren’t empty, I'll check the file name for its extension, ensuring to consider special cases like `.tar.gz`. But for the destination bucket, I'll only keep `.tar`.


**Extracting IDs from keys**

I'm working on a function to extract IDs from key strings. I start by trimming any trailing slashes and splitting the key to get the file name. If the name exists, I look for the ID pattern using a regex, including a fallback option to grab the first 11 characters if necessary.

I also consider if the filename follows certain patterns with extensions, and while matching, some formats like `<id>-something.wav` won't work but should be okay as long as they appear at the beginning.

Next, I'll create a set to collect these IDs while iterating through keys.


**Avoiding duplicate counts**

I'm considering how to ensure I don't count duplicates when scanning multiple prefixes, especially if they overlap. So, I’ll need to deduplicate those prefixes. Introducing a `limit` can help test quickly without scanning too many keys, though the user might not need it.

I want to track the operation count, noting how many list requests the paginator executes. Each page is a list operation, so I can count those as they come in.

Then I’ll compute the differences between source and destination IDs and prepare to write those results into a file, ideally in a `reports` directory with a structured naming convention. I think I’ll allow an output directory to be specified as well.


**Implementing output paths and summaries**

I'm setting up output paths: one for missing items in the destination and another for extras in the destination. If the user doesn’t provide a path, I'll default to a sanitized filename for the reports. I’ll also consider creating a JSON summary file, although it isn’t strictly necessary.

While summarizing the operation, I need to address potential pitfalls, like the significant size of the destination bucket. The cost of operations is minor, so I’ll estimate it for transparency. However, I will clarify pricing carefully in case it changes.

Overall, I want to mention alternative methods for checking object counts too, while keeping things straightforward and efficient. Lastly, I’ll implement the script and check its functionality. Adding this will be helpful for the user and ensure I track my progress effectively.


Output: Plan updated


**Moving through steps**

I'm currently on step 1, where I'm inspecting existing utilities. I've already checked out the r2_client and scripts, and it looks like that's all completed for this step. Now I can mark step 1 as done and move on to step 2. I'll be focusing on what's next in the process! It’s interesting to see how these steps unfold and contribute to the overall task.


Output: Plan updated


**Implementing S3 Scan Function**

I'm outlining the implementation for a function called `scan_bucket_for_ids(s3, bucket, prefixes, exts, id_fallback=True, limit_keys=0, include_sample_keys=0)`. The plan is to keep things simple while ensuring we count pages correctly.

I’ll create a generator `iter_bucket_keys()` to yield bucket keys. In `scan_bucket_for_ids()`, I'll accumulate IDs into a set while checking for specified extensions and respecting limits across prefixes. Plus, I plan to incorporate progress feedback, possibly using `tqdm` for page iteration.


**Implementing Progress Reporting**

I’m considering how to handle page counts while implementing the `scan_bucket_for_ids()` function. Since we don't know the total pages ahead of time, I'll print progress every 100,000 keys. I think it would be good to keep the script lightweight, possibly using `tqdm` as an optional feature but defaulting it to off.

The plan is to report scanned keys at each 100k threshold. Also, if the prefix list is empty, we need to avoid scanning the entire bucket to prevent double counting. Finally, allowing for intermediate ID lists should be a useful feature for further analysis.


**Outputting ID Lists for Debugging**

I’m considering whether to output both ID lists for debugging purposes. We could add an option like `--dump-ids`, but to keep it simple, I’ll just focus on outputting missing IDs. However, users may want to see missing IDs in a file, so I’ll implement options like `--write-source-ids` and `--write-dest-ids`, even if they're not strictly necessary.

For the output file, I'll create a default path that includes a timestamp. I think it would make sense to generate filenames that explicitly label the reports, such as missing IDs and additional IDs. I’ll include both files by default but will ensure users are aware they can clean them up if necessary.


**Setting Up Output Options**

I'm thinking about enhancing the output functionality by allowing options like `--out` and `--out-extra`. If these aren't provided, I'll make sure to create both with a timestamp. The plan is to set a base path if `--out` isn’t specified.

I considered adding a `--skip-extra` option, but I’m leaning against that. Once the script runs and creates the necessary files, I’ll mark step 2 as completed and step 3 as in progress, and then apply the patch.


Input:
*** Begin Patch
*** Add File: scripts/diff_r2_video_ids.py
+#!/usr/bin/env python3
+"""
+Fast diff of video IDs between two Cloudflare R2 buckets.
+
+This script LISTs keys in each bucket (paginated; up to 1000 keys per request),
+extracts YouTube-style video IDs, and computes set differences locally.
+
+Typical use case:
+  - Source bucket (raw/original): test
+  - Output bucket (cleaned):      1-cleaned-data
+
+Examples:
+  # Basic (scan whole buckets, extract IDs from filenames/paths)
+  python scripts/diff_r2_video_ids.py
+
+  # Faster: restrict to known prefixes
+  python scripts/diff_r2_video_ids.py --source-prefix podcasts/ --dest-prefix cleaned/trail/
+
+  # If your source files have a known extension (optional filter)
+  python scripts/diff_r2_video_ids.py --source-ext webm,mp4,mkv,m4a,mp3,opus --dest-ext tar
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import re
+import sys
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from typing import Iterable, List, Optional, Sequence, Set, Tuple
+
+import boto3
+from botocore.config import Config as BotoConfig
+from dotenv import load_dotenv
+
+
+YT_ID_FULL_RE = re.compile(r"^[A-Za-z0-9_-]{11}$")
+# Common key forms:
+# - podcasts/<id>.webm
+# - <id>.tar
+# - <id>_original.flac
+YT_ID_IN_KEY_RE = re.compile(r"(?:^|/)([A-Za-z0-9_-]{11})(?=\\.|_|$)")
+
+
+def _split_csv(values: Sequence[str]) -> List[str]:
+    out: List[str] = []
+    for v in values:
+        for part in str(v).split(","):
+            part = part.strip()
+            if part:
+                out.append(part)
+    return out
+
+
+def _normalize_prefixes(prefix_args: Sequence[str]) -> List[str]:
+    prefixes = _split_csv(prefix_args)
+    if not prefixes:
+        return [""]
+    # If any prefix is empty, it covers the full bucket anyway.
+    if any(p.strip() == "" for p in prefixes):
+        return [""]
+
+    norm: List[str] = []
+    seen = set()
+    for p in prefixes:
+        p = p.strip().lstrip("/")  # S3 keys never start with '/'
+        if p and not p.endswith("/"):
+            p = p + "/"
+        if p not in seen:
+            seen.add(p)
+            norm.append(p)
+    return norm or [""]
+
+
+def _normalize_exts(ext_args: Sequence[str]) -> Set[str]:
+    exts = _split_csv(ext_args)
+    out: Set[str] = set()
+    for e in exts:
+        e = e.strip()
+        if not e:
+            continue
+        if e == "*":
+            return set()
+        if not e.startswith("."):
+            e = "." + e
+        out.add(e.lower())
+    return out
+
+
+def _key_ext(key: str) -> str:
+    name = (key.rsplit("/", 1)[-1] if key else "").strip()
+    if "." not in name:
+        return ""
+    return "." + name.rsplit(".", 1)[-1].lower()
+
+
+def extract_video_id(key: str, *, allow_prefix_fallback: bool = True) -> Optional[str]:
+    key = (key or "").strip().lstrip("/")
+    if not key or key.endswith("/"):
+        return None
+
+    name = key.rsplit("/", 1)[-1]
+    stem = name.rsplit(".", 1)[0] if "." in name else name
+    if YT_ID_FULL_RE.match(stem):
+        return stem
+
+    m = YT_ID_IN_KEY_RE.search(key)
+    if m:
+        return m.group(1)
+
+    # Optional fallback: treat first 11 chars of the filename stem as the ID
+    # if they look like a YouTube ID.
+    if allow_prefix_fallback and len(stem) >= 11:
+        candidate = stem[:11]
+        if YT_ID_FULL_RE.match(candidate):
+            return candidate
+
+    return None
+
+
+def build_s3_client() -> "boto3.client":
+    endpoint_url = os.environ.get("R2_ENDPOINT_URL") or os.environ.get("S3_API") or ""
+    access_key_id = os.environ.get("R2_ACCESS_KEY_ID") or os.environ.get("AWS_ACCESS_KEY_ID") or ""
+    secret_access_key = os.environ.get("R2_SECRET_ACCESS_KEY") or os.environ.get("AWS_SECRET_ACCESS_KEY") or ""
+
+    if not endpoint_url:
+        raise RuntimeError("Missing R2_ENDPOINT_URL (or S3_API) in environment.")
+    if not access_key_id:
+        raise RuntimeError("Missing R2_ACCESS_KEY_ID (or AWS_ACCESS_KEY_ID) in environment.")
+    if not secret_access_key:
+        raise RuntimeError("Missing R2_SECRET_ACCESS_KEY (or AWS_SECRET_ACCESS_KEY) in environment.")
+
+    max_pool = int(os.environ.get("R2_MAX_POOL_CONNECTIONS", "128") or 128)
+    connect_timeout = int(os.environ.get("R2_CONNECT_TIMEOUT_SEC", "10") or 10)
+    read_timeout = int(os.environ.get("R2_READ_TIMEOUT_SEC", "60") or 60)
+
+    return boto3.client(
+        "s3",
+        endpoint_url=endpoint_url,
+        aws_access_key_id=access_key_id,
+        aws_secret_access_key=secret_access_key,
+        config=BotoConfig(
+            signature_version="s3v4",
+            retries={"max_attempts": 5, "mode": "adaptive"},
+            max_pool_connections=max_pool,
+            connect_timeout=connect_timeout,
+            read_timeout=read_timeout,
+        ),
+    )
+
+
+@dataclass
+class ScanStats:
+    keys_scanned: int = 0
+    pages: int = 0
+
+
+def scan_bucket_ids(
+    s3,
+    *,
+    bucket: str,
+    prefixes: Sequence[str],
+    exts: Set[str],
+    allow_prefix_fallback: bool,
+    limit_keys: int,
+    progress_every: int,
+) -> Tuple[Set[str], ScanStats]:
+    ids: Set[str] = set()
+    stats = ScanStats()
+    last_report_at = 0
+
+    paginator = s3.get_paginator("list_objects_v2")
+
+    for prefix in prefixes:
+        kwargs = {"Bucket": bucket}
+        if prefix:
+            kwargs["Prefix"] = prefix
+
+        for page in paginator.paginate(**kwargs):
+            stats.pages += 1
+            for obj in page.get("Contents", []) or []:
+                key = obj.get("Key") or ""
+                stats.keys_scanned += 1
+
+                if exts and _key_ext(key) not in exts:
+                    continue
+                vid = extract_video_id(key, allow_prefix_fallback=allow_prefix_fallback)
+                if vid:
+                    ids.add(vid)
+
+                if limit_keys and stats.keys_scanned >= limit_keys:
+                    return ids, stats
+
+                if progress_every and (stats.keys_scanned - last_report_at) >= progress_every:
+                    print(f"[{bucket}] scanned {stats.keys_scanned:,} keys → {len(ids):,} ids")
+                    last_report_at = stats.keys_scanned
+
+    return ids, stats
+
+
+def _safe_slug(s: str) -> str:
+    return re.sub(r"[^A-Za-z0-9_.-]+", "_", s.strip()) or "bucket"
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser(description="Diff YouTube video IDs between two R2 buckets via LIST.")
+    ap.add_argument("--source-bucket", default="test", help="Source bucket name (default: test)")
+    ap.add_argument("--dest-bucket", default="1-cleaned-data", help="Destination bucket name (default: 1-cleaned-data)")
+    ap.add_argument(
+        "--source-prefix",
+        action="append",
+        default=[],
+        help="Source key prefix to scan (repeatable, comma-separated ok). Default: entire bucket.",
+    )
+    ap.add_argument(
+        "--dest-prefix",
+        action="append",
+        default=[],
+        help="Destination key prefix to scan (repeatable, comma-separated ok). Default: entire bucket.",
+    )
+    ap.add_argument(
+        "--source-ext",
+        action="append",
+        default=[],
+        help="Optional source extensions filter (repeatable, comma-separated ok), e.g. webm,mp4,opus",
+    )
+    ap.add_argument(
+        "--dest-ext",
+        action="append",
+        default=["tar"],
+        help="Optional destination extensions filter (repeatable, comma-separated ok). Default: tar",
+    )
+    ap.add_argument(
+        "--no-fallback",
+        action="store_true",
+        help="Disable filename-stem prefix fallback (only accept exact 11-char stems / common patterns).",
+    )
+    ap.add_argument("--limit-keys", type=int, default=0, help="Scan at most N keys per bucket (debug).")
+    ap.add_argument("--progress-every", type=int, default=100_000, help="Print progress every N keys (0 disables).")
+    ap.add_argument("--out-missing", default="", help="Output path for IDs in source but missing in dest.")
+    ap.add_argument("--out-extra", default="", help="Output path for IDs in dest but missing in source.")
+    ap.add_argument("--print-samples", type=int, default=25, help="Print first N IDs from each diff (0 disables).")
+    args = ap.parse_args()
+
+    load_dotenv(dotenv_path=os.environ.get("DOTENV_PATH") or ".env", override=False)
+
+    source_bucket = (args.source_bucket or "").strip()
+    dest_bucket = (args.dest_bucket or "").strip()
+    if not source_bucket or not dest_bucket:
+        raise SystemExit("Both --source-bucket and --dest-bucket must be non-empty.")
+
+    source_prefixes = _normalize_prefixes(args.source_prefix)
+    dest_prefixes = _normalize_prefixes(args.dest_prefix)
+    source_exts = _normalize_exts(args.source_ext)
+    dest_exts = _normalize_exts(args.dest_ext)
+    allow_prefix_fallback = not bool(args.no_fallback)
+
+    s3 = build_s3_client()
+
+    print(f"source: bucket={source_bucket} prefixes={source_prefixes} exts={sorted(source_exts) or ['*']}")
+    source_ids, source_stats = scan_bucket_ids(
+        s3,
+        bucket=source_bucket,
+        prefixes=source_prefixes,
+        exts=source_exts,
+        allow_prefix_fallback=allow_prefix_fallback,
+        limit_keys=int(args.limit_keys or 0),
+        progress_every=int(args.progress_every or 0),
+    )
+    print(
+        f"source: scanned {source_stats.keys_scanned:,} keys in {source_stats.pages:,} LIST pages → {len(source_ids):,} ids"
+    )
+
+    print(f"dest:   bucket={dest_bucket} prefixes={dest_prefixes} exts={sorted(dest_exts) or ['*']}")
+    dest_ids, dest_stats = scan_bucket_ids(
+        s3,
+        bucket=dest_bucket,
+        prefixes=dest_prefixes,
+        exts=dest_exts,
+        allow_prefix_fallback=allow_prefix_fallback,
+        limit_keys=int(args.limit_keys or 0),
+        progress_every=int(args.progress_every or 0),
+    )
+    print(f"dest:   scanned {dest_stats.keys_scanned:,} keys in {dest_stats.pages:,} LIST pages → {len(dest_ids):,} ids")
+
+    missing_in_dest = sorted(source_ids - dest_ids)
+    extra_in_dest = sorted(dest_ids - source_ids)
+
+    ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
+    os.makedirs("reports", exist_ok=True)
+
+    out_missing = args.out_missing.strip()
+    if not out_missing:
+        out_missing = os.path.join("reports", f"r2_missing_in_{_safe_slug(dest_bucket)}_{ts}.txt")
+    out_extra = args.out_extra.strip()
+    if not out_extra:
+        out_extra = os.path.join("reports", f"r2_only_in_{_safe_slug(dest_bucket)}_{ts}.txt")
+
+    with open(out_missing, "w", encoding="utf-8") as f:
+        for vid in missing_in_dest:
+            f.write(vid + "\n")
+
+    with open(out_extra, "w", encoding="utf-8") as f:
+        for vid in extra_in_dest:
+            f.write(vid + "\n")
+
+    print(f"\nmissing in dest (present in source, absent in dest): {len(missing_in_dest):,} → {out_missing}")
+    print(f"only in dest (present in dest, absent in source):    {len(extra_in_dest):,} → {out_extra}")
+
+    n = int(args.print_samples or 0)
+    if n > 0:
+        if missing_in_dest:
+            print(f"\nmissing sample (first {min(n, len(missing_in_dest))}):")
+            for vid in missing_in_dest[:n]:
+                print(f"- {vid}")
+        if extra_in_dest:
+            print(f"\nextra sample (first {min(n, len(extra_in_dest))}):")
+            for vid in extra_in_dest[:n]:
+                print(f"- {vid}")
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
+
*** End Patch


Output: {"output":"Success. Updated the following files:\nA scripts/diff_r2_video_ids.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Output: Plan updated


**Validating script functionality**

I'm focusing on validating the script runs by using the `--help` flag and checking with `py_compile`. First, I’ll run `python scripts/diff_r2_video_ids.py --help`, which shouldn’t need credentials since it uses dotenv but doesn’t build the client yet. It seems the client builds after parsing the arguments, so using `--help` should be safe. I'll also run `python -m py_compile scripts/diff_r2_video_ids.py` to confirm the syntax. I might do both to be thorough!


python scripts/diff_r2_video_ids.py --help | head


python -m py_compile scripts/diff_r2_video_ids.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
/bin/bash: line 1: python: command not found


Output:
Exit code: 127
Wall time: 0 seconds
Output:
/bin/bash: line 1: python: command not found


**Running Python command**

In this environment, I’m considering that the Python command might be `python3`. So, it seems like I should go ahead and use `python3`. I think the plan is to rerun whatever I was working on with that command. It feels like a straightforward step, but I want to make sure I get it right. Let's proceed and see if it works!


python3 scripts/diff_r2_video_ids.py --help | head


python3 -m py_compile scripts/diff_r2_video_ids.py


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
usage: diff_r2_video_ids.py [-h] [--source-bucket SOURCE_BUCKET]
                            [--dest-bucket DEST_BUCKET]
                            [--source-prefix SOURCE_PREFIX]
                            [--dest-prefix DEST_PREFIX]
                            [--source-ext SOURCE_EXT] [--dest-ext DEST_EXT]
                            [--no-fallback] [--limit-keys LIMIT_KEYS]
                            [--progress-every PROGRESS_EVERY]
                            [--out-missing OUT_MISSING]
                            [--out-extra OUT_EXTRA]
                            [--print-samples PRINT_SAMPLES]


Output:
Exit code: 0
Wall time: 0 seconds
Output:


Summary: auto


Output: Plan updated


**Clarifying operations in S3 and R2**

I want to avoid referring to "classA searches" as "Class A ops." It's crucial to understand that each list operation indeed counts as one class A request. If I'm listing 1000 keys in a call, that means I'm actually executing 1000 class A operations.

While listing 500k objects translates to about 500 list requests, I should clarify that the S3 API's `list_objects_v2` retrieves up to 1000 keys. So, confirming that each page shows about 1000 keys is essential for accuracy. Additionally, I consider how the Cloudflare R2 dashboard may trigger a list operation with scrolls or filters, but using the API for full buckets is more efficient.


**Crafting the final message**

I need to put together a final message while remembering the guideline to avoid emojis. Here's a draft:

For the **Fastest/Cheapest** method, we can dump key lists using the S3 LIST operation, which processes about 1000 objects per request. This way, we can extract video IDs without needing per-object metadata or HEAD requests.

I've created a script, `diff_r2_video_ids.py`, to handle this and output missing ID lists to the reports folder. When running the script, you can specify source and destination prefixes.

If you're unsure about the destination prefix, you can simplify it by omitting it, but the script generates two output files and counts. It’s worth noting that you might need to check if Python 3 is installed!


**Finalizing the response**

Alright, I realize the script requires Python 3, which should be available in the user’s environment, though it might not be in every container. Good to note that while my local setup shows Python 3 available, others might just have Python.

Now, the user asked for the fastest and cheapest way to handle listing and comparing. I’ll confirm it’s minimal in cost. If users truly can’t afford listing, an inventory method can be suggested, but it's optional.

Let's create clear bullet points in the final response, and I’ll make sure to include file references like `scripts/diff_r2_video_ids.py:1` and the `reports/` directory, while keeping it concise.


- You don’t need “searching metadata” object-by-object: the fastest/cheapest way is to do 1 full `LIST` of each bucket (S3 `list_objects_v2` returns up to 1000 keys per request), extract the video IDs from the keys, then diff locally. For ~500k objects, that’s only ~500 LIST requests per bucket.
- I added `scripts/diff_r2_video_ids.py:1` to do exactly this and write the missing IDs into `reports/`.

Run (add prefixes if you know them, e.g. `podcasts/`):
- `python3 scripts/diff_r2_video_ids.py --source-bucket test --dest-bucket 1-cleaned-data --source-prefix podcasts/ --print-samples 0`


<environment_context>
  <approval_policy>never</approval_policy>
  <sandbox_mode>danger-full-access</sandbox_mode>
  <network_access>enabled</network_access>
  <shell>bash</shell>
</environment_context>


# Context from my IDE setup:

## Open tabs:
- .env: .env

## My request for Codex:
i'm really not sure about the prefixes but the test bucket has podcasts/, is there a simpler way that searches recursively ? but the cleaned bucket has diff ones. what if we search recursively  ? does boto or anything else gives us leads and once done, run it and give that updated list.


**Evaluating R2 Operations**

In R2, there are no egress fees, which is great! I understand they care about cost and time efficiency. Listing 500k objects means making around 500 list requests, as each list fetches up to 1000 keys. I realize they mention “hella lot of time,” which might hint they've used the Cloudflare dashboard or R2 metadata search. They want something fast and cheap. I’m thinking of using the Inventory feature for a daily CSV output, which could be done asynchronously, but they might need results immediately!


**Exploring S3 Listing Options**

I’m considering using S3's "list objects" with prefixes to streamline searches, even though each prefix differs. If I can map the keys, like using something like "podcasts/<videoID>.wav" and "cleaned/<videoID>.wav," it might help normalize the videoID for computing the difference. A simpler approach is leveraging the AWS CLI with R2 endpoints for listing objects. However, I realize that printing 500k entries to the terminal isn’t ideal; saving to a file seems better. I could use a Python script with Boto3 for efficient key listing and diff comparison.


**Considering Recursive Search Options**

I’m thinking about how we can perform a streaming diff using sorted lists and external sorting with UNIX `sort`, or even utilizing SQLite to store keys or Bloom filters. The users are curious about searching recursively. I realize we can run a script in the repo using .env credentials, and I'd expect that they include the R2 connection details. I also want to ensure we check for existing scripts in the repo while adhering to AGENTS instructions, looking for any AGENTS.md documents available.


**Reviewing AGENTS Instructions**

I noticed that the user message mentions "AGENTS.md instructions for /home/ubuntu/maya3," which contain skills we need to follow. However, they didn’t mention anything particularly relevant for the bucket diff task, like a skill for a creator or installer. I think it's still worth checking for other AGENTS. While we have root instructions about code style, we really just need to focus on what the user provided. Therefore, I'll suggest a final approach, perhaps implementing a Python script that lists both buckets and computes any missing IDs.


**Determining Efficient Script Implementation**

Given the user's request for the fastest and cheapest approach, I realize we can implement a script to generate a list of missing IDs. However, I need to be cautious because listing full buckets could take some time. There’s also a chance that we might not have the necessary credentials, depending on what's in the .env file. So, I plan to use shell commands to list the repository files and examine the .env. Running commands concurrently with multi_tool_use.parallel will help streamline this investigation. I'll start with `ls -la` and check for relevant code using `rg`.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
total 89180
drwxrwxr-x 15 ubuntu ubuntu     4096 Jan 13 02:13 .
drwxr-x--- 43 ubuntu ubuntu     4096 Jan 13 02:33 ..
-rw-rw-r--  1 ubuntu ubuntu    39841 Jan 12 20:23 2026-01-12-this-session-is-being-continued-from-a-previous-co.txt
drwxrwxr-x  3 ubuntu ubuntu     4096 Jan  8 00:45 .cache
drwx------  2 ubuntu ubuntu     4096 Jan 11 06:38 .claude
drwxrwxr-x  7 ubuntu ubuntu     4096 Jan  8 04:29 dashboard-ui
drwxrwxr-x  3 ubuntu ubuntu     4096 Jan  8 00:32 data
-rw-rw-r--  1 ubuntu ubuntu  8622072 Jan 12 20:21 discovered_podcasts_te_SUPABASE_UNIQUE_50000.csv
-rw-rw-r--  1 ubuntu ubuntu     1950 Jan  6 22:23 docker-compose.yml
-rw-rw-r--  1 ubuntu ubuntu     4672 Jan  8 06:01 Dockerfile
-rw-rw-r--  1 ubuntu ubuntu      213 Jan  6 22:23 .dockerignore
-rw-rw-r--  1 ubuntu ubuntu     3652 Jan 11 07:43 .env
-rw-rw-r--  1 ubuntu ubuntu      470 Jan  6 22:23 .env.example
drwxrwxr-x 11 ubuntu ubuntu     4096 Jan 13 02:14 .git
-rw-rw----  1 ubuntu ubuntu      336 Jan 13 02:13 .gitattributes
-rw-rw-r--  1 ubuntu ubuntu      619 Jan 13 02:13 .gitignore
-rwxrwxr-x  1 ubuntu ubuntu    53910 Jan  9 06:53 massive_process.py
-rw-rw-r--  1 ubuntu ubuntu   131296 Jan  8 01:02 metadata.json
-rw-rw-r--  1 ubuntu ubuntu 53926254 Jan 11 06:05 new_podcasts.csv
drwxr-xr-x  4 root   root       4096 Jan  8 02:27 output
-rw-rw-r--  1 ubuntu ubuntu    69304 Jan 11 06:13 pipeline.py
drwxrwxr-x  2 ubuntu ubuntu     4096 Jan  8 14:57 __pycache__
-rw-rw-r--  1 ubuntu ubuntu 28311552 Jan  8 17:12 R2_chapters_FINAL.csv
-rw-rw-r--  1 ubuntu ubuntu     3654 Jan  8 00:31 README.md
drwxrwxr-x  2 ubuntu ubuntu     4096 Jan  8 16:07 reports
-rw-rw-r--  1 ubuntu ubuntu      623 Jan  8 02:14 requirements.txt
drwxrwxr-x  7 ubuntu ubuntu     4096 Jan 13 03:05 scripts
drwxrwxr-x  3 ubuntu ubuntu     4096 Jan 11 06:12 src
drwxrwxr-x  3 ubuntu ubuntu     4096 Jan  8 19:59 tmp
-rw-rw-r--  1 ubuntu ubuntu    30615 Jan  6 22:23 vast_ai.md
-rw-rw-r--  1 ubuntu ubuntu    19445 Jan  6 22:23 vast_ai_sdk.md
drwxrwxr-x  2 ubuntu ubuntu     4096 Jan  8 04:16 .vercel
drwxrwxr-x  2 ubuntu ubuntu     4096 Jan  6 22:31 .xdg_run


**Searching for R2 scripts**

I need to check the scripts directory for any existing R2 scripts. It sounds like a straightforward task, but I want to make sure I'm thorough in my search. How many scripts do we have? I’ll focus on anything relevant that might help streamline our work. Let's see what we can find!


rg -n "R2|Cloudflare|CLOUDFLARE|S3|boto3|list_objects|list-objects|endpoint_url|supabase" .


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 434
Output:
./requirements.txt:36:boto3
./requirements.txt:37:supabase
./Dockerfile:59:    .venv/bin/pip install --no-cache-dir supabase
./Dockerfile:116:# Default command - Supabase queue mode with R2 upload
./Dockerfile:120:# deployments, which makes R2 appear "missing". Prefer sync export for reliability.
./Dockerfile:121:CMD ["--supabase-queue", "--r2-upload", "--output", "./output"]
./.env.example:8:URL=https://your-project.supabase.co
./.env.example:11:# Cloudflare R2 (required for audio source and output)
./pipeline.py:125:    supabase_metadata: Optional[Dict] = None,
./pipeline.py:135:    If supabase_metadata is provided, it's passed to download_audio for:
./pipeline.py:219:            audio_path, metadata = download_audio(video_url, config, supabase_metadata=supabase_metadata)
./pipeline.py:873:        'source_url': metadata.get('source_url'),  # R2/YouTube URL for re-download
./pipeline.py:996:            from src.r2_client import R2Client
./pipeline.py:1016:                # Record the exact destination (including R2_PREFIX) even though
./pipeline.py:1018:                # locate outputs in R2 from Supabase without guessing prefixes.
./pipeline.py:1020:                    r2 = R2Client(bucket_type=getattr(config, 'r2_upload_bucket_type', 'production'))
./pipeline.py:1033:            from src.r2_client import R2Client
./pipeline.py:1052:                # Step 1.5: Remove original audio from output (source is preserved in R2)
./pipeline.py:1057:                        logger.debug(f"Removed _original.flac from output (source in R2)")
./pipeline.py:1065:                # Step 3: Upload to R2 (if enabled)
./pipeline.py:1068:                    r2 = R2Client(bucket_type=getattr(config, 'r2_upload_bucket_type', 'production'))
./pipeline.py:1070:                        logger.info(f"R2 upload skipped (exists): {r2.uri(remote_key)}")
./pipeline.py:1074:                            raise RuntimeError(f"R2 upload failed for {r2.uri(remote_key)}")
./pipeline.py:1088:                            logger.info(f"🧹 Cleaned up output directory after R2 upload")
./pipeline.py:1092:                    # No R2 upload - still cleanup if cleanup_after_processing is enabled
./pipeline.py:1097:                            logger.info(f"🧹 Cleaned up output directory (no R2 upload)")
./docker-compose.yml:34:      # Persist output (optional if using R2)
./docker-compose.yml:45:      --supabase-queue
./README.md:3:High-throughput, GPU-accelerated audio diarization and segmentation pipeline. Processes podcast audio from R2 storage, performs speaker diarization, music detection, and outputs clean speaker-segmented data.
./README.md:14:R2 (Source Audio) → Supabase Queue → GPU Workers → R2 (Output .tar)
./README.md:23:| 1. Download | Fetch audio from R2 bucket |
./README.md:32:| 10. Export | Upload .tar to R2 |
./README.md:42:- Cloudflare R2 bucket (for storage)
./README.md:84:| `ACCOUNT_ID` | Cloudflare account ID |
./README.md:85:| `API_TOKEN` | Cloudflare R2 API token |
./README.md:98:python massive_process.py --supabase-queue --output ./output
./README.md:121:│   ├── download.py       # R2 download
./README.md:128:│   ├── r2_client.py      # R2 storage client
./README.md:129:│   ├── supabase_client.py # Supabase queue client
./massive_process.py:63:    from src.supabase_client import SupabaseClient, STATUS_COMPLETED, STATUS_FAILED
./massive_process.py:68:# Optional: load .env for HF/R2/Supabase credentials
./massive_process.py:105:        'R2_ENDPOINT_URL': 'Cloudflare R2 endpoint',
./massive_process.py:106:        'R2_ACCESS_KEY_ID': 'R2 access key',
./massive_process.py:107:        'R2_SECRET_ACCESS_KEY': 'R2 secret key',
./massive_process.py:115:    # R2 vars only required if --r2-upload is used
./massive_process.py:452:def gpu_worker_supabase(
./massive_process.py:489:    supabase_worker_id = f"{machine_id}_gpu{gpu_id}"
./massive_process.py:491:    worker_logger.info(f"Starting worker {supabase_worker_id} on GPU {gpu_id}")
./massive_process.py:515:        supabase = SupabaseClient()
./massive_process.py:521:            supabase_url=os.environ.get('URL') or os.environ.get('SUPABASE_URL'),
./massive_process.py:522:            supabase_key=os.environ.get('SUPABASE_ADMIN'),
./massive_process.py:569:                video = supabase.claim_next_video(
./massive_process.py:570:                    worker_id=supabase_worker_id,
./massive_process.py:596:                full_video = supabase.get_video(video_id)
./massive_process.py:611:                result = process_single_video(video_url, config, supabase_metadata=full_video)
./massive_process.py:626:                    # Store the fully-qualified key (includes R2_PREFIX) so it
./massive_process.py:627:                    # can be located in R2 without guessing prefixes.
./massive_process.py:638:                        'worker_id': supabase_worker_id,
./massive_process.py:653:                supabase.update_status(video_id, STATUS_COMPLETED, update_data)
./m…28208 chars truncated…s/prepare_benchmark.py:57:    response = r2.client.list_objects_v2(**kwargs)
./scripts/prepare_benchmark.py:68:print(f"   Found {len(all_r2_files)} files in R2")
./scripts/prepare_benchmark.py:93:# Filter to videos that are in both R2 AND Supabase
./scripts/prepare_benchmark.py:102:print(f"   Validated: {len(validated)} videos (in R2 + Supabase)")
./scripts/prepare_benchmark.py:162:print(f"  .venv/bin/python massive_process.py --supabase-queue --max-videos {len(validated)} --output ./output")
./scripts/reset_for_production.py:3:Reset Supabase tables and R2 folder for fresh production run.
./scripts/reset_for_production.py:8:3. Deletes everything in R2 bucket 1-cleaned-data/cleaned/trail/
./scripts/reset_for_production.py:27:    from supabase import create_client
./scripts/reset_for_production.py:100:    from supabase import create_client
./scripts/reset_for_production.py:177:    """Delete everything in R2 bucket 1-cleaned-data/cleaned/trail/."""
./scripts/reset_for_production.py:178:    from src.r2_client import R2Client
./scripts/reset_for_production.py:180:    print("\n🪣 R2 Bucket: 1-cleaned-data")
./scripts/reset_for_production.py:185:        client = R2Client(bucket_type='production')
./scripts/reset_for_production.py:191:        # Use boto3 paginator for listing all objects
./scripts/reset_for_production.py:192:        paginator = client.client.get_paginator('list_objects_v2')
./scripts/reset_for_production.py:211:        print(f"\n🗑  Deleting {len(objects)} objects from R2...")
./scripts/reset_for_production.py:213:        # Delete in batches of 1000 (S3/R2 limit)
./scripts/reset_for_production.py:236:    parser = argparse.ArgumentParser(description='Reset Supabase and R2 for fresh production run')
./scripts/reset_for_production.py:238:    parser.add_argument('--skip-r2', action='store_true', help='Skip deleting R2 cleaned/trail/ prefix')
./scripts/reset_for_production.py:266:    # 3. Delete R2 folder
./scripts/reset_for_production.py:269:        print("STEP 3: Skip R2 deletion (--skip-r2)")
./scripts/reset_for_production.py:273:        print("STEP 3: Delete R2 cleaned/trail/ folder")
./scripts/monitor/dashboard.py:29:from supabase import create_client
./scripts/run_analytics_migration.py:33:    from supabase import create_client
./scripts/run_analytics_migration.py:35:    print("ERROR: supabase package not installed")
./scripts/run_analytics_migration.py:36:    print("  Run: pip install supabase")
./scripts/run_analytics_migration.py:197:    print("  1. Go to: " + SUPABASE_URL.replace('.supabase.co', '.supabase.co/project/default/sql'))
./scripts/run_analytics_migration.py:198:    print("  2. Copy and paste the SQL from: scripts/supabase_migrations.sql")
./scripts/run_analytics_migration.py:202:    print(f"  cat scripts/supabase_migrations.sql")
./scripts/docker/smoke_test.sh:8:R2_UPLOAD="${R2_UPLOAD:-0}"
./scripts/docker/smoke_test.sh:34:  "${IMAGE_TAG}" -c "from src.supabase_client import SupabaseClient; print(SupabaseClient().get_stats())"
./scripts/docker/smoke_test.sh:36:echo "==> R2 connectivity smoke test"
./scripts/docker/smoke_test.sh:39:  "${IMAGE_TAG}" -c "from src.r2_client import R2Client; print('source_bucket', R2Client(bucket_type='source').bucket); print('production_bucket', R2Client(bucket_type='production').bucket)"
./scripts/docker/smoke_test.sh:47:    --supabase-queue
./scripts/docker/smoke_test.sh:56:  if [ "${R2_UPLOAD}" = "1" ]; then
./scripts/docker/smoke_test.sh:74:    echo "Warning: no .tar found in ${OUTPUT_DIR} (maybe R2 upload deleted it, or export disabled)."
./scripts/monitor/lease_recovery.py:26:from src.supabase_client import SupabaseClient, STATUS_PENDING, STATUS_CLAIMED
./scripts/monitor/lease_recovery.py:61:    from supabase import create_client
./scripts/vast/deploy_from_offers.py:19:  - This script loads `.env` by default for Maya3 settings (URL, SUPABASE_ADMIN, R2_*, HF_TOKEN)
./scripts/vast/deploy_from_offers.py:203:        "R2_ENDPOINT_URL",
./scripts/vast/deploy_from_offers.py:204:        "R2_ACCESS_KEY_ID",
./scripts/vast/deploy_from_offers.py:205:        "R2_SECRET_ACCESS_KEY",
./scripts/vast/deploy_from_offers.py:215:        "-e R2_ENDPOINT_URL": os.environ["R2_ENDPOINT_URL"],
./scripts/vast/deploy_from_offers.py:216:        "-e R2_ACCESS_KEY_ID": os.environ["R2_ACCESS_KEY_ID"],
./scripts/vast/deploy_from_offers.py:217:        "-e R2_SECRET_ACCESS_KEY": os.environ["R2_SECRET_ACCESS_KEY"],
./scripts/vast/deploy_from_offers.py:224:    # by explicitly setting R2_PREFIX to empty.
./scripts/vast/deploy_from_offers.py:225:    env_map["-e R2_PREFIX"] = r2_prefix
./scripts/vast/deploy_from_offers.py:272:    p.add_argument("--r2-prefix", default="", help="R2_PREFIX to set (default: empty => upload to bucket root)")
./scripts/vast/deploy_from_offers.py:308:            "--supabase-queue --workers-per-gpu 1 --r2-upload --output ./output "
./scripts/vast/deploy_from_offers.py:315:            "--supabase-queue --workers-per-gpu 1 --r2-upload --output ./output"


sed -n '1,240p' scripts/prepare_benchmark.py


sed -n '1,260p' scripts/reset_for_production.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
# Maya3 Pipeline Environment Variables
# Copy this file to .env and fill in your values

# Hugging Face (required for PyAnnote models)
HF_TOKEN=hf_your_token_here

# Supabase (required for queue management)
URL=https://your-project.supabase.co
SUPABASE_ADMIN=your_service_role_key_here

# Cloudflare R2 (required for audio source and output)
ACCOUNT_ID=your_cloudflare_account_id
API_TOKEN=your_r2_api_token

# Optional: RunPod deployment
RUNPOD_KEY=your_runpod_api_key


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
R2 Client Utilities for High-Throughput Pipeline Migration

Manages Cloudflare R2 buckets for:
- Production outputs (1-cleaned-data)
- Test outputs during migration (1-cleaned-data-test)
- Baseline snapshots for comparison (1-cleaned-data-baseline)

Usage:
    from src.r2_client import R2Client

    client = R2Client()
    client.upload_json("path/to/file.json", "video_id/metadata.json")
    client.download_json("video_id/metadata.json", "local/path.json")
"""

import os
import json
import logging
from pathlib import Path
from typing import Dict, Any, Optional, List
from dataclasses import dataclass

import boto3
from botocore.config import Config as BotoConfig

logger = logging.getLogger("R2Client")

@dataclass
class R2Config:
    """R2 bucket configuration."""
    endpoint_url: str
    access_key_id: str
    secret_access_key: str
    bucket: str
    prefix: str = ""

# Bucket configurations
BUCKETS = {
    'production': '1-cleaned-data',
    'test': '1-cleaned-data-test',
    'baseline': '1-cleaned-data-baseline',
    # Source/original videos (DO NOT DELETE). This bucket is expected to contain
    # the original YouTube files re-uploaded to R2 to avoid rate limits.
    'source': 'test',
}

def get_r2_config_from_env() -> R2Config:
    """Load R2 configuration from environment variables."""
    return R2Config(
        # Support a few common aliases used in various deploy templates.
        endpoint_url=os.environ.get('R2_ENDPOINT_URL') or os.environ.get('S3_API') or '',
        access_key_id=os.environ.get('R2_ACCESS_KEY_ID') or os.environ.get('AWS_ACCESS_KEY_ID') or '',
        secret_access_key=os.environ.get('R2_SECRET_ACCESS_KEY') or os.environ.get('AWS_SECRET_ACCESS_KEY') or '',
        bucket=os.environ.get('R2_BUCKET', 'test'),
        prefix=os.environ.get('R2_PREFIX', ''),
    )

class R2Client:
    """
    Cloudflare R2 client for pipeline data management.

    Supports multiple bucket targets for A/B testing during migration.
    """

    def __init__(
        self,
        config: Optional[R2Config] = None,
        bucket_type: str = 'test'
    ):
        """
        Initialize R2 client.

        Args:
            config: R2 configuration (loads from env if None)
            bucket_type: One of 'production', 'test', 'baseline'
        """
        self.config = config or get_r2_config_from_env()

        # Override bucket based on type
        if bucket_type in BUCKETS:
            self.bucket = BUCKETS[bucket_type]
        else:
            self.bucket = self.config.bucket

        # IMPORTANT: The output prefix (R2_PREFIX) should not accidentally apply
        # to the *source* bucket. Vast/RunPod templates often set R2_PREFIX for
        # uploads; for source reads we want R2_SOURCE_PREFIX (or empty).
        if bucket_type == 'source' and config is None:
            self.prefix = os.environ.get('R2_SOURCE_PREFIX', '') or ''
        else:
            self.prefix = self.config.prefix

        # Initialize boto3 client
        self._client = None

    @property
    def client(self):
        """Lazy-initialize boto3 client."""
        if self._client is None:
            if not self.config.endpoint_url:
                raise RuntimeError("R2 not configured: missing R2_ENDPOINT_URL (or S3_API)")
            if not self.config.access_key_id:
                raise RuntimeError("R2 not configured: missing R2_ACCESS_KEY_ID (or AWS_ACCESS_KEY_ID)")
            if not self.config.secret_access_key:
                raise RuntimeError("R2 not configured: missing R2_SECRET_ACCESS_KEY (or AWS_SECRET_ACCESS_KEY)")
            self._client = boto3.client(
                's3',
                endpoint_url=self.config.endpoint_url,
                aws_access_key_id=self.config.access_key_id,
                aws_secret_access_key=self.config.secret_access_key,
                config=BotoConfig(
                    signature_version='s3v4',
                    retries={'max_attempts': 3, 'mode': 'adaptive'},
                    # Prevent long hangs on HEAD/LIST during intermittent network stalls.
                    connect_timeout=int(os.environ.get("R2_CONNECT_TIMEOUT_SEC", "10") or 10),
                    read_timeout=int(os.environ.get("R2_READ_TIMEOUT_SEC", "60") or 60),
                    max_pool_connections=int(os.environ.get("R2_MAX_POOL_CONNECTIONS", "64") or 64),
                )
            )
        return self._client

    def full_key(self, key: str) -> str:
        """Public helper: return fully-qualified key (including configured prefix)."""
        return self._full_key(key)

    def uri(self, key: str) -> str:
        """Return an s3:// URI for a key (including configured prefix)."""
        return f"s3://{self.bucket}/{self._full_key(key)}"

    def _full_key(self, key: str) -> str:
        """Get full S3 key with prefix."""
        if self.prefix:
            return f"{self.prefix}/{key}"
        return key

    def upload_file(self, local_path: str, remote_key: str) -> bool:
        """
        Upload a file to R2.

        Args:
            local_path: Path to local file
            remote_key: Remote key (path in bucket)

        Returns:
            True if successful
        """
        try:
            full_key = self._full_key(remote_key)
            self.client.upload_file(local_path, self.bucket, full_key)
            logger.info(f"Uploaded {local_path} -> s3://{self.bucket}/{full_key}")
            return True
        except Exception as e:
            logger.error(f"Upload failed: {e}")
            return False

    def download_file(self, remote_key: str, local_path: str) -> bool:
        """
        Download a file from R2.

        Args:
            remote_key: Remote key (path in bucket)
            local_path: Path to save locally

        Returns:
            True if successful
        """
        try:
            full_key = self._full_key(remote_key)
            Path(local_path).parent.mkdir(parents=True, exist_ok=True)
            self.client.download_file(self.bucket, full_key, local_path)
            logger.info(f"Downloaded s3://{self.bucket}/{full_key} -> {local_path}")
            return True
        except Exception as e:
            logger.error(f"Download failed: {e}")
            return False

    def upload_json(self, data: Dict[str, Any], remote_key: str) -> bool:
        """Upload JSON data directly to R2."""
        try:
            full_key = self._full_key(remote_key)
            body = json.dumps(data, indent=2).encode('utf-8')
            self.client.put_object(
                Bucket=self.bucket,
                Key=full_key,
                Body=body,
                ContentType='application/json'
            )
            logger.info(f"Uploaded JSON -> s3://{self.bucket}/{full_key}")
            return True
        except Exception as e:
            logger.error(f"JSON upload failed: {e}")
            return False

    def download_json(self, remote_key: str) -> Optional[Dict[str, Any]]:
        """Download JSON data from R2."""
        try:
            full_key = self._full_key(remote_key)
            response = self.client.get_object(Bucket=self.bucket, Key=full_key)
            data = json.loads(response['Body'].read().decode('utf-8'))
            logger.info(f"Downloaded JSON <- s3://{self.bucket}/{full_key}")
            return data
        except Exception as e:
            logger.error(f"JSON download failed: {e}")
            return None

    def head_object(self, remote_key: str) -> Optional[Dict[str, Any]]:
        """Return S3 head_object response for a key, or None if missing/unreadable."""
        try:
            full_key = self._full_key(remote_key)
            return self.client.head_object(Bucket=self.bucket, Key=full_key)
        except Exception:
            return None

    def generate_presigned_get_url(self, remote_key: str, expires_in_sec: int = 3600) -> str:


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
Prepare 100 videos for benchmark by:
1. Listing R2 files that exist (podcasts/*.webm)
2. Cross-referencing with Supabase
3. Setting them to PENDING status
"""

import os
import sys
import time
import random

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

try:
    from dotenv import load_dotenv
    load_dotenv()
except ImportError:
    pass

from supabase import create_client
from src.r2_client import R2Client

# Config
TARGET_COUNT = 100
SUPABASE_URL = os.environ.get('URL') or os.environ.get('SUPABASE_URL')
SUPABASE_KEY = os.environ.get('SUPABASE_ADMIN')

if not SUPABASE_URL or not SUPABASE_KEY:
    print("ERROR: Missing Supabase credentials")
    sys.exit(1)

print("=" * 70)
print("BENCHMARK PREPARATION - 100 Videos")
print("=" * 70)

# Initialize clients
sb = create_client(SUPABASE_URL, SUPABASE_KEY)
r2 = R2Client(bucket_type='source')  # 'test' bucket has the podcasts/

print(f"\n1. Listing R2 files from {r2.bucket}/podcasts/...")

# List all files in podcasts/ prefix
all_r2_files = []
continuation_token = None

while True:
    kwargs = {
        'Bucket': r2.bucket,
        'Prefix': 'podcasts/',
        'MaxKeys': 1000
    }
    if continuation_token:
        kwargs['ContinuationToken'] = continuation_token

    response = r2.client.list_objects_v2(**kwargs)
    files = response.get('Contents', [])
    all_r2_files.extend(files)

    if not response.get('IsTruncated'):
        break
    continuation_token = response.get('NextContinuationToken')

    if len(all_r2_files) >= 5000:  # Limit scan
        break

print(f"   Found {len(all_r2_files)} files in R2")

# Extract video IDs from filenames (podcasts/{video_id}.webm)
r2_video_ids = set()
for f in all_r2_files:
    key = f['Key']  # podcasts/---g_VL7ySo.webm
    if key.endswith('.webm'):
        video_id = key.replace('podcasts/', '').replace('.webm', '')
        r2_video_ids.add(video_id)

print(f"   Extracted {len(r2_video_ids)} unique video IDs")

print(f"\n2. Cross-referencing with Supabase...")

# Query Supabase for these videos
# Do in batches to avoid URL length limits
video_ids_list = list(r2_video_ids)[:500]  # Limit for query

result = sb.table('videos').select(
    'youtube_id, title, source_duration_min, language, status'
).in_('youtube_id', video_ids_list).execute()

videos_in_db = {v['youtube_id']: v for v in (result.data or [])}
print(f"   Found {len(videos_in_db)} videos in Supabase DB")

# Filter to videos that are in both R2 AND Supabase
validated = []
for vid in video_ids_list:
    if vid in videos_in_db:
        v = videos_in_db[vid]
        validated.append(v)
        if len(validated) >= TARGET_COUNT:
            break

print(f"   Validated: {len(validated)} videos (in R2 + Supabase)")

if len(validated) < TARGET_COUNT:
    print(f"\n   WARNING: Only {len(validated)} videos available")
    if len(validated) < 10:
        print("   ERROR: Not enough videos for benchmark")
        sys.exit(1)

# Show status distribution before reset
statuses = {}
for v in validated:
    s = v.get('status', 'unknown')
    statuses[s] = statuses.get(s, 0) + 1
print(f"   Current status: {statuses}")

print(f"\n3. Resetting {len(validated)} videos to PENDING status...")

# Reset to PENDING for benchmark
video_ids = [v['youtube_id'] for v in validated]
batch_size = 50

for i in range(0, len(video_ids), batch_size):
    batch = video_ids[i:i+batch_size]
    sb.table('videos').update({
        'status': 'PENDING',
        'claimed_by': None,
        'claimed_at': None,
        'lease_expires_at': None,
    }).in_('youtube_id', batch).execute()
    print(f"   Reset batch {i//batch_size + 1}/{(len(video_ids) + batch_size - 1)//batch_size}")

print(f"\n4. Summary of benchmark videos:")

# Stats
durations = [v.get('source_duration_min', 0) or 0 for v in validated]
total_duration = sum(durations)
avg_duration = total_duration / len(validated) if validated else 0

print(f"   Total videos: {len(validated)}")
print(f"   Total duration: {total_duration:.1f} min ({total_duration/60:.1f} hours)")
print(f"   Avg duration: {avg_duration:.1f} min")
if durations:
    print(f"   Duration range: {min(durations):.1f} - {max(durations):.1f} min")

# Language distribution
langs = {}
for v in validated:
    lang = v.get('language', 'unknown') or 'unknown'
    langs[lang] = langs.get(lang, 0) + 1
print(f"   Languages: {dict(sorted(langs.items(), key=lambda x: -x[1]))}")

print(f"\n5. Sample videos:")
for v in validated[:5]:
    title = (v.get('title') or 'No title')[:50]
    dur = v.get('source_duration_min', 0) or 0
    print(f"   • {v['youtube_id']} | {dur:.1f}min | {title}")

print(f"\n" + "=" * 70)
print(f"READY: {len(validated)} videos set to PENDING")
print(f"Run benchmark with:")
print(f"  .venv/bin/python massive_process.py --supabase-queue --max-videos {len(validated)} --output ./output")
print("=" * 70)

# Save video IDs for reference
with open('/tmp/benchmark_videos.txt', 'w') as f:
    for v in validated:
        f.write(f"{v['youtube_id']}\n")
print(f"\nVideo IDs saved to /tmp/benchmark_videos.txt")


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
Reset Supabase tables and R2 folder for fresh production run.

This script:
1. Resets all videos in Supabase to PENDING status
2. Clears all analytics tables (worker_heartbeats, worker_metrics, processing_events, error_logs)
3. Deletes everything in R2 bucket 1-cleaned-data/cleaned/trail/

Usage:
    python scripts/reset_for_production.py [--dry-run] [--skip-r2]
"""

import os
import sys
import argparse
from pathlib import Path

# Add project root to path
sys.path.insert(0, str(Path(__file__).parent.parent))

from dotenv import load_dotenv
load_dotenv()

def reset_videos_to_pending(dry_run: bool = False):
    """Reset all videos to PENDING status."""
    from supabase import create_client

    url = os.environ.get('URL', '')
    key = os.environ.get('SUPABASE_ADMIN', '')

    if not url or not key:
        print("❌ Missing Supabase credentials (URL, SUPABASE_ADMIN)")
        return False

    client = create_client(url, key)

    # Count videos by status first
    print("\n📊 Current video status counts:")
    for status in ['PENDING', 'CLAIMED', 'COMPLETED', 'FAILED', 'SKIPPED']:
        result = client.table('videos').select('youtube_id', count='exact').eq('status', status).execute()
        count = result.count if hasattr(result, 'count') else len(result.data)
        print(f"   {status}: {count}")

    if dry_run:
        print("\n🔍 [DRY RUN] Would reset all videos to PENDING")
        return True

    # Reset all to PENDING (also clears leases/attempts so the queue truly
    # restarts from the beginning).
    #
    # IMPORTANT: A single massive UPDATE can hit Postgres statement_timeout on
    # Supabase. Do this in small batches instead.
    print("\n🔄 Resetting videos to PENDING (batched; clearing leases + attempt_count)...")
    update_payload = {
        'status': 'PENDING',
        'claimed_by': None,
        'claimed_at': None,
        'lease_expires_at': None,
        'attempt_count': 0,
    }

    batch_size = int(os.environ.get("RESET_BATCH_SIZE", "500") or 500)
    total_updated = 0

    while True:
        # Grab a small batch of non-pending ids.
        result = (
            client.table('videos')
            .select('youtube_id,status', count='exact')
            .neq('status', 'PENDING')
            .limit(batch_size)
            .execute()
        )
        rows = result.data or []
        if not rows:
            break

        ids = [r.get('youtube_id') for r in rows if r.get('youtube_id')]
        if not ids:
            break

        if dry_run:
            total_updated += len(ids)
            print(f"   [DRY RUN] Would reset batch of {len(ids)} videos (total so far: {total_updated})")
            continue

        # Update just this batch.
        upd = client.table('videos').update(update_payload).in_('youtube_id', ids).execute()
        updated = len(upd.data) if upd.data else 0
        total_updated += updated
        print(f"   ✅ Reset batch: {updated} videos (total: {total_updated})")

    print(f"   ✅ Done. Reset {total_updated} videos to PENDING")

    return True

def clear_analytics_tables(dry_run: bool = False):
    """Clear all analytics tables."""
    from supabase import create_client

    url = os.environ.get('URL', '')
    key = os.environ.get('SUPABASE_ADMIN', '')

    if not url or not key:
        print("❌ Missing Supabase credentials")
        return False

    client = create_client(url, key)

    tables = ['worker_heartbeats', 'worker_metrics', 'processing_events', 'error_logs']

    print("\n📊 Analytics tables:")
    for table in tables:
        try:
            result = client.table(table).select('*', count='exact').execute()
            count = result.count if hasattr(result, 'count') else len(result.data)
            print(f"   {table}: {count} rows")
        except Exception as e:
            print(f"   {table}: Error - {e}")

    if dry_run:
        print("\n🔍 [DRY RUN] Would delete all rows from analytics tables")
        return True

    print("\n🗑  Clearing analytics tables (batched)...")
    # For UUID primary keys, PostgREST `in.(...)` filters can exceed URL limits
    # if we include too many values. Use a smaller batch for those tables.
    batch_size_worker = int(os.environ.get("RESET_BATCH_SIZE", "1000") or 1000)
    batch_size_uuid = int(os.environ.get("RESET_UUID_BATCH_SIZE", "200") or 200)

    def delete_in_batches(table_name: str, pk: str):
        deleted_total = 0
        while True:
            limit = batch_size_worker if pk == 'worker_id' else batch_size_uuid
            query = client.table(table_name).select(pk).limit(limit)
            # Make progress deterministic for large UUID tables when created_at exists.
            if pk != 'worker_id':
                query = query.order('created_at', desc=False)
            try:
                sel = query.execute()
            except Exception as e:
                # Some tables (e.g. worker_heartbeats/worker_metrics) have no created_at.
                if pk != 'worker_id' and 'created_at' in str(e) and 'does not exist' in str(e):
                    sel = client.table(table_name).select(pk).limit(limit).execute()
                else:
                    raise
            rows = sel.data or []
            if not rows:
                break
            # Supabase can return UUID objects for `id` columns; normalize to str
            # to avoid "JSON could not be generated" errors.
            pks = [str(r.get(pk)) for r in rows if r.get(pk) is not None and str(r.get(pk))]
            if not pks:
                break
            if dry_run:
                deleted_total += len(pks)
                print(f"   [DRY RUN] Would delete {len(pks)} rows from {table_name} (total so far: {deleted_total})")
                continue
            client.table(table_name).delete().in_(pk, pks).execute()
            deleted_total += len(pks)
        return deleted_total

    for table in tables:
        try:
            if table in ('worker_heartbeats', 'worker_metrics'):
                deleted = delete_in_batches(table, 'worker_id')
            else:
                deleted = delete_in_batches(table, 'id')
            print(f"   ✅ Cleared {table}: {deleted} rows")
        except Exception as e:
            print(f"   ⚠  Could not clear {table}: {e}")

    return True

def delete_r2_folder(dry_run: bool = False):
    """Delete everything in R2 bucket 1-cleaned-data/cleaned/trail/."""
    from src.r2_client import R2Client

    print("\n🪣 R2 Bucket: 1-cleaned-data")
    print("   Prefix to delete: cleaned/trail/")

    try:
        # Initialize client for production bucket with the specific prefix
        client = R2Client(bucket_type='production')

        # List objects in the folder
        objects = []
        prefix = "cleaned/trail/"

        # Use boto3 paginator for listing all objects
        paginator = client.client.get_paginator('list_objects_v2')
        for page in paginator.paginate(Bucket='1-cleaned-data', Prefix=prefix):
            if 'Contents' in page:
                objects.extend([obj['Key'] for obj in page['Contents']])

        print(f"   Found {len(objects)} objects to delete")

        if not objects:
            print("   ℹ  No objects found in cleaned/trail/")
            return True

        if dry_run:
            print(f"\n🔍 [DRY RUN] Would delete {len(objects)} objects:")
            for obj in objects[:10]:
                print(f"      - {obj}")
            if len(objects) > 10:
                print(f"      ... and {len(objects) - 10} more")
            return True

        print(f"\n🗑  Deleting {len(objects)} objects from R2...")

        # Delete in batches of 1000 (S3/R2 limit)
        batch_size = 1000
        deleted = 0
        for i in range(0, len(objects), batch_size):
            batch = objects[i:i+batch_size]
            delete_objects = {'Objects': [{'Key': key} for key in batch]}
            response = client.client.delete_objects(
                Bucket='1-cleaned-data',
                Delete=delete_objects
            )
            deleted += len(response.get('Deleted', []))
            errors = response.get('Errors', [])
            if errors:
                print(f"   ⚠  {len(errors)} errors in batch")

        print(f"   ✅ Deleted {deleted} objects from cleaned/trail/")
        return True

    except Exception as e:
        print(f"   ❌ Error: {e}")
        return False

def main():
    parser = argparse.ArgumentParser(description='Reset Supabase and R2 for fresh production run')
    parser.add_argument('--dry-run', action='store_true', help='Show what would be done without making changes')
    parser.add_argument('--skip-r2', action='store_true', help='Skip deleting R2 cleaned/trail/ prefix')
    args = parser.parse_args()

    print("=" * 60)
    print("PRODUCTION RESET SCRIPT")
    print("=" * 60)

    if args.dry_run:
        print("\n⚠  DRY RUN MODE - No changes will be made\n")
    else:
        print("\n⚠  WARNING: This will reset ALL data!")
        confirm = input("Type 'RESET' to confirm: ")
        if confirm != 'RESET':
            print("Aborted.")
            return

    # 1. Reset videos
    print("\n" + "=" * 60)
    print("STEP 1: Reset Videos to PENDING")
    print("=" * 60)
    reset_videos_to_pending(args.dry_run)

    # 2. Clear analytics


rg -n "r2\.upload|R2_PREFIX|remote_key|\.tar" pipeline.py massive_process.py src -S


Output:
Exit code: 0
Wall time: 0 seconds
Output:
massive_process.py:391:                        r2_upload_key=(result.get('r2_upload', {}) or {}).get('remote_key', ''),
massive_process.py:626:                    # Store the fully-qualified key (includes R2_PREFIX) so it
massive_process.py:1213:    # R2 upload (processed .tar -> 1-cleaned-data)
massive_process.py:1215:                       help='Upload per-video .tar into R2 bucket 1-cleaned-data (no deletes)')
pipeline.py:986:        remote_key = f"{video_id}.tar" if not upload_prefix else f"{upload_prefix}/{video_id}.tar"
pipeline.py:988:            result['r2_upload_key'] = remote_key
pipeline.py:1007:                    r2_upload_key=remote_key,
pipeline.py:1015:                result['r2_upload_key'] = remote_key
pipeline.py:1016:                # Record the exact destination (including R2_PREFIX) even though
pipeline.py:1024:                        'r2_upload_full_key': r2.full_key(remote_key),
pipeline.py:1025:                        'r2_upload_uri': r2.uri(remote_key),
pipeline.py:1060:                tar_path = output_dir.parent / f"{video_id}.tar"
pipeline.py:1069:                    if getattr(config, 'r2_upload_skip_if_exists', False) and r2.exists(remote_key):
pipeline.py:1070:                        logger.info(f"R2 upload skipped (exists): {r2.uri(remote_key)}")
pipeline.py:1072:                        upload_ok = r2.upload_file(str(tar_path), remote_key)
pipeline.py:1074:                            raise RuntimeError(f"R2 upload failed for {r2.uri(remote_key)}")
pipeline.py:1106:                'r2_upload_key': remote_key if getattr(config, 'r2_upload_enabled', False) else None,
pipeline.py:1108:                'r2_upload_full_key': r2.full_key(remote_key) if getattr(config, 'r2_upload_enabled', False) else None,
pipeline.py:1109:                'r2_upload_uri': r2.uri(remote_key) if getattr(config, 'r2_upload_enabled', False) else None,
src/r2_client.py:60:        prefix=os.environ.get('R2_PREFIX', ''),
src/r2_client.py:91:        # IMPORTANT: The output prefix (R2_PREFIX) should not accidentally apply
src/r2_client.py:92:        # to the *source* bucket. Vast/RunPod templates often set R2_PREFIX for
src/r2_client.py:142:    def upload_file(self, local_path: str, remote_key: str) -> bool:
src/r2_client.py:148:            remote_key: Remote key (path in bucket)
src/r2_client.py:154:            full_key = self._full_key(remote_key)
src/r2_client.py:162:    def download_file(self, remote_key: str, local_path: str) -> bool:
src/r2_client.py:167:            remote_key: Remote key (path in bucket)
src/r2_client.py:174:            full_key = self._full_key(remote_key)
src/r2_client.py:183:    def upload_json(self, data: Dict[str, Any], remote_key: str) -> bool:
src/r2_client.py:186:            full_key = self._full_key(remote_key)
src/r2_client.py:200:    def download_json(self, remote_key: str) -> Optional[Dict[str, Any]]:
src/r2_client.py:203:            full_key = self._full_key(remote_key)
src/r2_client.py:212:    def head_object(self, remote_key: str) -> Optional[Dict[str, Any]]:
src/r2_client.py:215:            full_key = self._full_key(remote_key)
src/r2_client.py:220:    def generate_presigned_get_url(self, remote_key: str, expires_in_sec: int = 3600) -> str:
src/r2_client.py:226:        full_key = self._full_key(remote_key)
src/r2_client.py:265:    def exists(self, remote_key: str) -> bool:
src/r2_client.py:268:            full_key = self._full_key(remote_key)
src/r2_client.py:274:    def delete(self, remote_key: str) -> bool:
src/r2_client.py:284:            full_key = self._full_key(remote_key)
src/download.py:410:        # Avoid accidental prefixing from generic R2_PREFIX when accessing the source bucket.
src/download.py:499:        (remote_key, head_object_response)
src/download.py:1005:        remote_key, head = _resolve_r2_source_object(video_id, config)
src/download.py:1009:        presigned_url = client.generate_presigned_get_url(remote_key, expires_in_sec=ttl)
src/download.py:1012:            f"📦 R2 source resolved: s3://{client.bucket}/{remote_key} "
src/download.py:1028:        metadata['r2_source_key'] = remote_key
src/export.py:164:        tar_path = job.output_dir.parent / f"{job.video_id}.tar"
src/export.py:167:        result.tar_size_bytes = tar_path.stat().st_size if tar_path.exists() else 0
src/export.py:175:            upload_ok = r2.upload_file(str(tar_path), job.r2_upload_key)
src/config.py:60:    # When enabled, pipeline will package each video's output directory into a .tar


Summary: auto


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 221
Output:
            'overlap_proximity_segments': len(overlap_proximity_segs),
            'overlap_proximity_duration': round(overlap_proximity_duration, 2),
            # v6.3 chunk reassignment stats
            'split_segments': chunk_reassignment_stats.segments_with_changes if chunk_reassignment_stats else 0,
            'new_speakers_from_splits': chunk_reassignment_stats.new_speakers_created if chunk_reassignment_stats else 0,
            # v7.7 detailed reassignment stats (validation gates debug info)
            'chunk_reassignment_detail': chunk_reassignment_full_stats if chunk_reassignment_full_stats else {},
            # v7.8 micro-contamination detection stats
            'micro_contamination': micro_contamination_stats if micro_contamination_stats else {},
            # v6.7 music detection stats
            'music_detection': music_detection_stats if music_detection_stats else {},
            # v7.9 boundary refinement stats
            'boundary_refinement': boundary_refinement_stats if boundary_refinement_stats else {},
        },
        'vad_stats': {
            'speech_segments': len(vad_segments),
            'total_speech': round(sum(s['duration'] for s in vad_segments), 2),
        },
        'sample_clips': {spk: [Path(p).name for p in paths] for spk, paths in sample_clips.items()},
        'timing': {k: round(v, 2) for k, v in timings.items()},
        'timing_total': round(total_time, 2),
        'compute_stats': compute_stats,
        'config': {
            'model': MODELS._model_name,
            'merge_threshold': config.cluster_merge_threshold,
            'vad_workers': config.vad_workers,
            'embedding_batch_size': config.embedding_batch_size,
            'min_segment_duration': config.min_segment_duration,
            # v6.1 config
            'min_tts_duration': config.min_tts_duration,
            'min_snr_db': config.min_snr_db,
            'filter_by_quality': config.filter_by_quality,
            # v6.3 chunk reassignment config
            'chunk_reassignment_enabled': config.enable_chunk_reassignment,
            'chunk_reassignment_threshold': config.chunk_reassignment_threshold,
            'chunk_reassignment_severe': config.chunk_reassignment_severe,
            # v6.7 music detection config
            'music_detection_enabled': config.enable_music_detection,
            'music_ratio_clean': config.music_ratio_clean,
            'music_ratio_demucs': config.music_ratio_demucs,
            # v6.8 intro skip + audio config
            'auto_intro_skip': config.auto_intro_skip,
            'preserve_original_audio': config.preserve_original_audio,
            # v7.9 boundary refinement
            'boundary_refinement_enabled': config.enable_boundary_refinement,
        },
        'processed_at': datetime.now().isoformat(),
        'pipeline_version': 'v7.9-vad-boundary-refinement',
        # === v7.5: Add download_meta for database storage ===
        'download_meta': {
            'native_sample_rate': metadata.get('original_sample_rate'),
            'channels': metadata.get('channels', 1),
            'acodec': metadata.get('acodec', 'unknown'),
            'abr': metadata.get('abr'),
            'format': metadata.get('format'),
            'duration': metadata.get('original_duration'),
        },
        # v6.4 cache stats
        'embedding_cache_stats': cache_stats if 'cache_stats' in dir() else {},
    }

    # Save metadata JSON
    with open(output_dir / "metadata.json", 'w') as f:
        json.dump(result, f, indent=2)

    # === v6.8+: Cleanup 16kHz processing file if original is preserved ===
    # Keep only the high-quality original for final export
    if config.preserve_original_audio and metadata.get('original_audio_preserved'):
        trimmed_file = Path(audio_path)
        original_file = Path(metadata.get('original_audio_path', ''))
        if trimmed_file.exists() and original_file.exists():
            trimmed_size_mb = trimmed_file.stat().st_size / (1024 * 1024)
            trimmed_file.unlink()
            logger.info(f"🧹 Cleaned up 16kHz processing file ({trimmed_size_mb:.1f}MB), keeping original quality")

    # ========================================
    # STAGE 10: Export (Segmentation + Package + Upload)
    # Can run in background to avoid blocking GPU
    # ========================================
    export_enabled = getattr(config, 'r2_upload_enabled', False) or getattr(config, 'segment_audio', False)
    background_export = getattr(config, 'background_export', False)

    if export_enabled:
        log_stage(10, "Export", "START")

        upload_prefix = (getattr(config, 'r2_upload_prefix', '') or '').strip().strip('/')
        remote_key = f"{video_id}.tar" if not upload_prefix else f"{upload_prefix}/{video_id}.tar"
        if getattr(config, 'r2_upload_enabled', False):
            result['r2_upload_key'] = remote_key
        original_audio_path = Path(metadata.get('original_audio_path', ''))
       …2235 chars truncated… mode

        if not background_export:
            # === SYNCHRONOUS EXPORT MODE ===
            from src.r2_client import R2Client
            from src.export import segment_audio_parallel

            with COMPUTE.monitor_stage("Export") as metrics:
                # Step 1: Segment original audio (parallel ffmpeg)
                segments_exported = 0
                if getattr(config, 'segment_audio', True) and original_audio_path.exists():
                    seg_start = time.time()
                    segments_exported = segment_audio_parallel(
                        audio_path=original_audio_path,
                        segments=segments,
                        output_dir=output_dir,
                        intro_offset=intro_offset,
                        max_workers=8,
                        output_format="flac"
                    )
                    seg_time = time.time() - seg_start
                    logger.info(f"📦 Segmented {segments_exported} clips in {seg_time:.1f}s")

                # Step 1.5: Remove original audio from output (source is preserved in R2)
                if not getattr(config, 'include_original_in_tar', False):
                    original_in_output = output_dir / f"{video_id}_original.flac"
                    if original_in_output.exists():
                        original_in_output.unlink()
                        logger.debug(f"Removed _original.flac from output (source in R2)")

                # Step 2: Create tar archive
                tar_path = output_dir.parent / f"{video_id}.tar"
                with tarfile.open(tar_path, "w") as tar:
                    tar.add(output_dir, arcname=video_id)
                tar_size_bytes = tar_path.stat().st_size if tar_path.exists() else 0

                # Step 3: Upload to R2 (if enabled)
                upload_ok = True
                if getattr(config, 'r2_upload_enabled', False):
                    r2 = R2Client(bucket_type=getattr(config, 'r2_upload_bucket_type', 'production'))
                    if getattr(config, 'r2_upload_skip_if_exists', False) and r2.exists(remote_key):
                        logger.info(f"R2 upload skipped (exists): {r2.uri(remote_key)}")
                    else:
                        upload_ok = r2.upload_file(str(tar_path), remote_key)
                        if not upload_ok:
                            raise RuntimeError(f"R2 upload failed for {r2.uri(remote_key)}")

                    # Delete local tar after upload
                    if getattr(config, 'r2_upload_delete_local_tar', True):
                        try:
                            tar_path.unlink()
                        except Exception:
                            pass

                    # Delete per-video output dir after successful upload (prevents disk fill).
                    if getattr(config, 'r2_upload_delete_local_output_dir', True):
                        try:
                            import shutil
                            shutil.rmtree(output_dir, ignore_errors=True)
                            logger.info(f"🧹 Cleaned up output directory after R2 upload")
                        except Exception:
                            pass
                else:
                    # No R2 upload - still cleanup if cleanup_after_processing is enabled
                    if getattr(config, 'cleanup_after_processing', True):
                        try:
                            import shutil
                            shutil.rmtree(output_dir, ignore_errors=True)
                            logger.info(f"🧹 Cleaned up output directory (no R2 upload)")
                        except Exception:
                            pass

            timings['export'] = metrics.duration
            compute_stats['export'] = {'cpu': metrics.cpu_percent_avg, 'gpu': metrics.gpu_percent_avg}
            result['export'] = {
                'segments_exported': segments_exported,
                'tar_size_bytes': int(tar_size_bytes),
                'r2_upload_key': remote_key if getattr(config, 'r2_upload_enabled', False) else None,
                'r2_upload_bucket': r2.bucket if getattr(config, 'r2_upload_enabled', False) else None,
                'r2_upload_full_key': r2.full_key(remote_key) if getattr(config, 'r2_upload_enabled', False) else None,
                'r2_upload_uri': r2.uri(remote_key) if getattr(config, 'r2_upload_enabled', False) else None,
            }

            log_stage(10, "Export", "DONE", f"{timings['export']:.1f}s | {segments_exported} segs | {tar_size_bytes/(1024**2):.1f}MB")

            # Update timing_total
            total_time = time.time() - total_start
            result['timing'] = {k: round(v, 2) for k, v in timings.items()}
            result['timing_total'] = round(total_time, 2)
            result['compute_stats'] = compute_stats

            # Update metadata.json


**Analyzing object counts**

I’m considering that the object counts might vary because there could be multiple files for each video, like having both a .webm file and metadata. For raw audio files, it seems there should be just one per video, stored under `podcasts/<video_id>.webm` or `.m4a`. To find missing processed videos, we can compare the video IDs present in both source and cleaned buckets. I'll need to list objects, filter them down to just `.tar` files, and extract the relevant video IDs accordingly.


**Evaluating bucket listing strategies**

I’m thinking about the prefixes in the test bucket that has `podcasts/`, and it might contain other files too. They’re worried about time and cost associated with listing all 536k objects, which could take some time due to the need for multiple calls to get all keys. Each call retrieves 1000 keys, resulting in about 536 calls, which might take under a few minutes with network latency.

Alternatively, we could use Supabase statuses, which track video rows with completion details, allowing us to find videos with a 'COMPLETED' status that lack an output file and focus on finding missing IDs from the cleaned bucket.


**Considering inventory and listing methods**

If Supabase already has those IDs, I can query it for rows marked as COMPLETED, checking for keys like r2_upload_full_key. However, there could be discrepancies from videos that weren’t processed or mismatches in prefixes. A more efficient approach might be using R2's inventory feature, which can create daily CSV reports of keys and may save some costs. But for speed, using the AWS CLI command `aws s3api list-objects-v2` seems like the quickest way to get the list.

I can write a script, `scripts/r2_bucket_diff.py`, that loads environment variables with boto3 to help process multiple prefixes and extract video IDs, while also providing progress updates.


**Exploring fastest and simplest options**

The user is asking if there’s a quicker and cheaper way to find discrepancies, or if they have to take a more complicated route. They’re interested in a simpler recursive search method. I think we could propose an approach and even implement a script, but the user might want to run it themselves. As a Codex CLI agent, I could execute a script to compute differences and generate a list, assuming I have the right credentials.

Yet, there are concerns about network access and whether I have the AWS CLI installed. I can utilize Python with boto3, which is included in the requirements. We might run this in the current virtual environment or use the system Python to check for boto3 availability. I'll need to verify that everything is set up properly.


python -c "import boto3, botocore; print('boto3', boto3.__version__)"


Output:
Exit code: 127
Wall time: 0 seconds
Output:
/bin/bash: line 1: python: command not found


Hmm, it looks like I can't find the `python` command. I'm thinking that maybe I should be using `python3` instead. It’s interesting how those little details can trip me up! I mean, it’s like one tiny difference can change everything. I should definitely check to see if that works. I wonder if this is a common mix-up for others too. Let me confirm that and see how I can proceed.


python3 -c "import boto3, botocore; print('boto3', boto3.__version__)"


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
boto3 1.40.61


python3 - <<'PY'
import os
from dotenv import load_dotenv
load_dotenv()
keys=[
 'R2_ENDPOINT_URL','S3_API','R2_ACCESS_KEY_ID','AWS_ACCESS_KEY_ID','R2_SECRET_ACCESS_KEY','AWS_SECRET_ACCESS_KEY',
 'R2_BUCKET','R2_PREFIX','R2_SOURCE_PREFIX','URL','SUPABASE_URL','SUPABASE_ADMIN','ACCOUNT_ID','API_TOKEN'
]
for k in keys:
 v=os.environ.get(k)
 if v is None:
  continue
 if 'KEY' in k or 'SECRET' in k or 'TOKEN' in k or 'ADMIN' in k:
  masked=v[:4]+'…'+v[-4:] if len(v)>8 else '***'
  print(f"{k}={masked} (len={len(v)})")
 else:
  print(f"{k}={v}")
PY


Output:
Exit code: 1
Wall time: 0.1 seconds
Output:
Traceback (most recent call last):
  File "<stdin>", line 3, in <module>
  File "/home/ubuntu/.local/lib/python3.10/site-packages/dotenv/main.py", line 346, in load_dotenv
    dotenv_path = find_dotenv()
  File "/home/ubuntu/.local/lib/python3.10/site-packages/dotenv/main.py", line 305, in find_dotenv
    assert frame.f_back is not None
AssertionError


**Troubleshooting dotenv error**

I'm looking into the `find_dotenv()` assertion error that occurs when running Python. It seems that this happens because there might not be a file path found. Actually, `find_dotenv()` uses stack frames, which can cause issues when running interactively. A workaround could be to pass the path directly by using `load_dotenv(".env")`. I’ll try rerunning it with this adjustment and see if it resolves the issue!


python3 - <<'PY'
import os
from dotenv import load_dotenv
load_dotenv(dotenv_path='.env')
keys=[
 'R2_ENDPOINT_URL','S3_API','R2_ACCESS_KEY_ID','AWS_ACCESS_KEY_ID','R2_SECRET_ACCESS_KEY','AWS_SECRET_ACCESS_KEY',
 'R2_BUCKET','R2_PREFIX','R2_SOURCE_PREFIX','URL','SUPABASE_URL','SUPABASE_ADMIN','ACCOUNT_ID','API_TOKEN'
]
for k in keys:
 v=os.environ.get(k)
 if v is None:
  continue
 if any(s in k for s in ['KEY','SECRET','TOKEN','ADMIN']):
  masked=v[:4]+'…'+v[-4:] if len(v)>8 else '***'
  print(f"{k}={masked} (len={len(v)})")
 else:
  print(f"{k}={v}")
PY


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
R2_ENDPOINT_URL=https://cb908ed13329eb7b186e06ab51bda190.r2.cloudflarestorage.com
S3_API=https://cb908ed13329eb7b186e06ab51bda190.r2.cloudflarestorage.com
R2_ACCESS_KEY_ID=c3c9…0210 (len=32)
R2_SECRET_ACCESS_KEY=eab9…b576 (len=64)
R2_BUCKET=test
R2_PREFIX=
URL=https://exlkkfpymkpqlxulurel.supabase.co
SUPABASE_ADMIN=eyJh…XeIw (len=219)
ACCOUNT_ID=cb908ed13329eb7b186e06ab51bda190
API_TOKEN=*** (len=0)


**Computing discrepancies in S3 buckets**

I see that the API token is empty, so they might be using S3 API instead.

We've got R2 credentials for S3, which is great! Now I need to compute the discrepancies between the 'test' and '1-cleaned-data' buckets.

I'll write a script to list keys for the source bucket under the prefix 'podcasts/'. For the cleaned bucket, I might check for `.tar` files across all keys. We just need to identify which keys match the cleaned results. The key format looks like `<upload_prefix>/<video_id>.tar`. Also, multiple prefixes could be set for uploads. While scanning the entire bucket, I might run into extra `.tar` files not related to the video pipeline, but most likely they’ll all belong. There might be additional objects like logs and analytics as well.


**Analyzing cleaned data discrepancies**

They mentioned that the number of items—502.24k—likely represents all cleaned data, which includes mostly `.tar` files plus some extras. This difference of 20-30k suggests some files might be missing.

The simplest approach would be to list all `.tar` objects in the cleaned bucket and extract their IDs from the filenames, treating them as video_ids. I'll do the same for the source bucket under the `podcasts/` prefix. After that, I can compute the difference between `source_ids` and `cleaned_ids` to identify what's missing.

We also need to handle cases with multiple audio files for each video, like transcripts and segments, which might create duplicates. Using a set will help deduplicate. I should also account for different file extensions in the source bucket, like webm, m4a, and mp3. The base name extraction is necessary, and I need to be careful of any nested directories within the `podcasts/` path. A quick sample check could confirm the file naming structure!


**Extracting IDs from audio files**

I need to include other formats like `.mp3` as well, so I'll create a generic approach. For each key under `podcasts/`, I’ll take the last segment of the path, remove the extension, and treat it as an ID. I do need to be careful, though, since some keys could have suffixes like `_original`.

In the source bucket, original audio might be named with formats like `<video_id>.m4a`. I’ll use regex to identify YouTube IDs, which are 11 characters long. However, the reference to "videoIDs" might suggest they're not strictly YouTube IDs; they could be internal.

To extract an ID accurately, I can use regex like `r'([A-Za-z0-9_-]{11})'`. Still, I have to consider other 11-character sequences that could exist. So, it might be safer to compute by checking the filename length and matching it to our allowed patterns. If it’s not an exact match, I can look for an 11-character string within it. For the cleaned bucket, I'll remember that tar filenames follow the format `<video_id>.tar`.


**Optimizing ID extraction process**

I’m focusing on the base name length of 11, which will apply to filenames like `cleaned/trail/<video_id>.tar`. I also need to consider the cleaned bucket, as there could be tar files that don't have corresponding sources, so I'll compute `cleaned_ids - source_ids` to spot any extras.

For performance, listing 500k objects in a bucket can be efficiently done using `list_objects_v2` with pagination via boto3. This lets us stream the extraction without needing to store all keys, just the video IDs in a Python set.

Storing 500k IDs might require around 25MB of memory, plus a bit more for overhead. However, they want the cheapest option. Each list request counts as Class A, which means about 1000 Class A requests overall. Cloudflare R2 charges roughly $4.50 per million operations, so 1000 requests will only cost about $0.0045, which is trivial. The whole process might take just a few minutes!