o }oi#%@sddlZddlZddlmZddlZddlZddlZ ddl Z ddl m Z ddlZddlmZddlmZmZddlmZddlmZmZddZe d Zed Zd Zee\Z Z!Gd d d Z"e # d'de$e%de&fddZ'd(ddZ(ddZ)ddZ*e #ej+j,d)dede%fddZ-ej+j.e-dd ej/fd!d"Z0e1d#kre 2d$e3ej4d%Z5e j67e5ej+j8e-e0d&dSdS)*N)Callable) rearrange)T5EncoderModelT5TokenizerFast)CausalVideoTokenizer) read_image resize_videocCs6tjd|d}tjd|d}|d|||fS)a. Initializes the T5 tokenizer and encoder model, loading them from a specified cache directory. Args: t5_cache_dir (str): Path to the cache directory for storing the pretrained model files. Returns: tuple: A tuple containing the tokenizer and encoder model instances. zgoogle-t5/t5-11b) cache_dircuda)rfrom_pretrainedrtoeval) t5_cache_dir tokenizer text_encoderrk/home/ubuntu/.local/lib/python3.10/site-packages/nemo/collections/diffusion/data/prepare_energon_dataset.pyinitialize_text_encoder s   rzUhf://datasets/huggan/smithsonian_butterflies_subset/data/train-00000-of-00001.parquetCosmosCausalCV_f4x8x8c@s:eZdZdZdejdedejdejfddZd d d Zd S) EncodedSamplea| A class representing an encoded sample, containing the text encoding, length, attention mask, and offset mappings. Attributes: encoded_text (np.ndarray): Encoded text array. length (int): Length of the encoding. attn_mask (np.ndarray): Attention mask for the encoding. offset_mappings (np.ndarray): Mappings for offset positions. encoded_textlength attn_maskoffset_mappingscCs||_||_||_||_dS)N)rrrr)selfrrrrrrr__init__Is zEncodedSample.__init__returnNcCs`|jd|jtj|_|jd|jtj|_|jdur.|jd|jtj|_dSdS)zj Truncates the encoded text, attention mask, and offset mappings to the specified length. rN)rrastypenpfloat16rint32r)rrrrtruncateOs  zEncodedSample.truncate)rN) __name__ __module__ __qualname____doc__rndarrayintrr"rrrrr=s rTpromptsr"c Cs@|j|ddd|d|d}|j}|j}|r#|d} | } nd} |||d} | j} |jdd } t| j d D] } d | | | | d<q=| } |}| ddd|f} |ddd|f}g}t| j d D]}|r|| |}nd}| t | | t j| ||||qs|r|D]}|q|S) a% Encodes a batch of text prompts into T5 embeddings. Args: tokenizer: Tokenizer instance for encoding. encoder: T5 encoder model instance. prompts (list[str]): List of text prompts to encode. truncate (bool): If True, truncates the output embeddings. max_length (int): Maximum length for each encoded prompt. output_mapping (bool): If True, returns offset mappings for each prompt. Returns: list[EncodedSample]: A list of encoded samples containing text encodings and masks. ptT max_length)return_tensors truncationpaddingr, return_lengthreturn_offsets_mappingoffset_mappingN) input_idsattention_mask)dimr)batch_encode_plusr3r r4cpunumpylast_hidden_statesumrangeshapeappendrrrr r")rencoderr*r"r,output_mappingbatch_encodingr3roffsets_mappingoutputsrlengthsbatch_idoutidxoffsetsxrrrencode_for_batchYsD     * rJc CsVt|||gd}tj|jtjd}|j\}}tjd||tjd}||dd|f<|S)a[ Generates a T5 embedding for a single text prompt. Args: tokenizer: T5 tokenizer instance. text_encoder: T5 encoder model instance. prompt (str): The text prompt to encode. t5_embeding_max_length (int): Maximum length for the embedding. Returns: torch.Tensor: Padded T5 embedding tensor. rdtyper5N)rJtorchtensorrbfloat16r=zeros) rrpromptt5_embeding_max_lengthrFrLCt5_embedrrrgenerate_t5_embeds  rVcCs0||}||}||dkr||n|}||fS)a6 Calculates the start and end indices for distributed processing based on rank. Args: dataset_size (int): Total dataset size. rank (int): Current process rank. world_size (int): Total number of processes. Returns: tuple: (start index, end index) for the rank. r5r) dataset_sizerank world_size split_size start_idxend_idxrrrget_start_end_idx_for_this_ranks r]c Cstj|}|d}|d}t|}t|ddd}t|dd}|tjdf}t|dd \}}tt t |}|d |j t j d t||jd |jd dd} | S)z Generates a sample dictionary with image latent tensor, caption, and metadata. Args: index (int): Index of the dataset row. Returns: dict: Dictionary containing processed image latents, embeddings, and metadata. image_urlnamezh w (t c) -> t h w cr5)tr)) short_size.)temporal_window06rK) image_height image_width)__key__z.pthz.picklez.json)dfilocrrrrnewaxis autoencoderrVrrr rMrOpickledumpsr=) indexrowr^ image_captionvideo batch_video_ image_latenttext_embeddingsamplerrrbutterfly_process_funcs"     rxoutput process_func output_dirc Cst}tj}ttt||\}}tj |ddtj |d|d}t j |dd}t||D] }||} || q2WddS1sIwYdS)z Prepares a WebDataset using the specified processing function, for distributed settings. Args: process_func (Callable): Function to process each dataset entry. output_dir (str): Output directory to save processed dataset. T)exist_okrXz -%06d.tari')maxcountN)distget_rankrM distributedget_world_sizer]lenriosmakedirspathjoinwds ShardWriterr<write) rzr{rXrYr[r\ output_tarsinkirwrrrprepares  "r)targetrcCstjttdd}|S)z Prepares the butterfly dataset for distributed training. Returns: run.Partial: Partially configured run for WebDataset preparation. butterfly_webdataset)rzr{)runPartialrrx)reciperrrprepare_butterfly_datasetsr__main__nccl LOCAL_RANK)default_factory)Tr)T)r))ry)9rrmtypingrnemo_runrr9rpandaspdrMtorch.distributedrr~ webdatasetreinopsr transformersrr9nemo.collections.common.video_tokenizers.cosmos_tokenizerr.nemo.collections.common.video_tokenizers.utilsrrr read_parquetrir rlrrrrno_gradliststrboolrJrVr]rxcli entrypointrfactoryrrr#init_process_groupr(environ local_rankr set_devicemainrrrrsP        ?(