o
    wi                     @   s   d dl mZmZmZ d dlZddlmZmZm	Z	m
Z
mZ ddlmZmZ e	 r3d dlmZ ddlmZ e r<dd	lmZ e
eZeed
dG dd deZdS )    )AnyUnionoverloadN   )add_end_docstringsis_torch_availableis_vision_availableloggingrequires_backends   )Pipelinebuild_pipeline_init_args)Image)
load_image)&MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMEST)has_image_processorc                	       s   e Zd ZdZ fddZdd Zedeedf de	d	dfd
dZ
edeee ed f de	d	ed fddZ
deeee ded f de	d	eded f f fddZ
dd ZdddZdd Z  ZS )ImageToImagePipelineao  
    Image to Image pipeline using any `AutoModelForImageToImage`. This pipeline generates an image based on a previous
    image input.

    Example:

    ```python
    >>> from PIL import Image
    >>> import requests

    >>> from transformers import pipeline

    >>> upscaler = pipeline("image-to-image", model="caidas/swin2SR-classical-sr-x2-64")
    >>> img = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
    >>> img = img.resize((64, 64))
    >>> upscaled_img = upscaler(img)
    >>> img.size
    (64, 64)

    >>> upscaled_img.size
    (144, 144)
    ```

    This image to image pipeline can currently be loaded from [`pipeline`] using the following task identifier:
    `"image-to-image"`.

    See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=image-to-image).
    c                    s*   t  j|i | t| d | t d S )Nvision)super__init__r
   check_model_typer   )selfargskwargs	__class__ b/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/transformers/pipelines/image_to_image.pyr   F   s   
zImageToImagePipeline.__init__c                 K   s>   i }i }i }d|v r|d |d< d|v r|d |d< |||fS )Ntimeout	head_maskr   )r   r   preprocess_paramspostprocess_paramsforward_paramsr   r   r   _sanitize_parametersK   s   
z)ImageToImagePipeline._sanitize_parametersimageszImage.Imager   returnc                 K      d S Nr   r   r$   r   r   r   r   __call__W      zImageToImagePipeline.__call__c                 K   r&   r'   r   r(   r   r   r   r)   Z   r*   c                    s   t  j|fi |S )a  
        Transform the image(s) passed as inputs.

        Args:
            images (`str`, `list[str]`, `PIL.Image` or `list[PIL.Image]`):
                The pipeline handles three types of images:

                - A string containing a http link pointing to an image
                - A string containing a local path to an image
                - An image loaded in PIL directly

                The pipeline accepts either a single image or a batch of images, which must then be passed as a string.
                Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL
                images.
            timeout (`float`, *optional*, defaults to None):
                The maximum time in seconds to wait for fetching images from the web. If None, no timeout is used and
                the call may block forever.

        Return:
            An image (Image.Image) or a list of images (list["Image.Image"]) containing result(s). If the input is a
            single image, the return will be also a single image, if the input is a list of several images, it will
            return a list of transformed images.
        )r   r)   r(   r   r   r   r)   ]   s   c                 C   s   | j di |}|S )Nr   )model)r   model_inputsmodel_outputsr   r   r   _forwardy   s   zImageToImagePipeline._forwardNc                 C   s6   t ||d}| j|gdd}| jdkr|| j}|S )N)r   pt)r$   return_tensors)r   image_processor	frameworktotorch_dtype)r   imager   inputsr   r   r   
preprocess}   s
   
zImageToImagePipeline.preprocessc                 C   s   g }d|  v r|j}|D ]+}|j   dd }tj	|ddd}|d 
 tj}|t| qt|dkrA|S |d S )Nreconstructionr   r   )sourcedestinationg     o@)keysr8   datasqueezefloatcpuclamp_numpynpmoveaxisroundastypeuint8appendr   	fromarraylen)r   r-   r$   outputsoutputr   r   r   postprocess   s   z ImageToImagePipeline.postprocessr'   )__name__
__module____qualname____doc__r   r#   r   r   strr   r)   listr.   r7   rM   __classcell__r   r   r   r   r   '   s$     ,
r   )typingr   r   r   rB   rC   utilsr   r   r   r	   r
   baser   r   PILr   image_utilsr   models.auto.modeling_autor   
get_loggerrN   loggerr   r   r   r   r   <module>   s   
