





'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


lowercase_					= logging.get_logger(__name__)





def lowerCamelCase						(     __lowerCamelCase :    List[Any]    )						->List[List[ImageInput]]:
    if isinstance(__lowerCamelCase					,       (list, tuple)    ) and isinstance(videos[0]					,       (list, tuple)    ) and is_valid_image(videos[0][0]    ):
        return videos

    elif isinstance(__lowerCamelCase					,       (list, tuple)    ) and is_valid_image(videos[0]    ):
        return [videos]

    elif is_valid_image(__lowerCamelCase    ):
        return [[videos]]

    raise ValueError(F'Could not make batched video from {videos}'    )






class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = ['''pixel_values''']


    def __init__(		self ,		A = True ,		A = None ,		A = PILImageResampling.BILINEAR ,		A = True ,		A = None ,		A = True ,		A = 1 / 255 ,		A = True ,		A = None ,		A = None ,		**A ,		)		->   None:
        super().__init__(**A       )
        _SCREAMING_SNAKE_CASE									=			size if size is not None else {"""shortest_edge""": 224}
        _SCREAMING_SNAKE_CASE									=			get_size_dict(A ,		default_to_square=A       )
        _SCREAMING_SNAKE_CASE									=			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        _SCREAMING_SNAKE_CASE									=			get_size_dict(A ,		param_name="""crop_size"""       )

        _SCREAMING_SNAKE_CASE									=			do_resize
        _SCREAMING_SNAKE_CASE									=			size
        _SCREAMING_SNAKE_CASE									=			do_center_crop
        _SCREAMING_SNAKE_CASE									=			crop_size
        _SCREAMING_SNAKE_CASE									=			resample
        _SCREAMING_SNAKE_CASE									=			do_rescale
        _SCREAMING_SNAKE_CASE									=			rescale_factor
        _SCREAMING_SNAKE_CASE									=			do_normalize
        _SCREAMING_SNAKE_CASE									=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        _SCREAMING_SNAKE_CASE									=			image_std if image_std is not None else IMAGENET_STANDARD_STD


    def 			snake_case_(		self ,		A ,		A ,		A = PILImageResampling.BILINEAR ,		A = None ,		**A ,		)		->   np.ndarray:
        _SCREAMING_SNAKE_CASE									=			get_size_dict(A ,		default_to_square=A       )
        if "shortest_edge" in size:
            _SCREAMING_SNAKE_CASE									=			get_resize_output_image_size(A ,		size["""shortest_edge"""] ,		default_to_square=A       )
        elif "height" in size and "width" in size:
            _SCREAMING_SNAKE_CASE									=			(size["""height"""], size["""width"""])
        else:
            raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'       )
        return resize(A ,		size=A ,		resample=A ,		data_format=A ,		**A       )


    def 			snake_case_(		self ,		A ,		A ,		A = None ,		**A ,		)		->   np.ndarray:
        _SCREAMING_SNAKE_CASE									=			get_size_dict(A       )
        if "height" not in size or "width" not in size:
            raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'       )
        return center_crop(A ,		size=(size["""height"""], size["""width"""]) ,		data_format=A ,		**A       )


    def 			snake_case_(		self ,		A ,		A ,		A = None ,		**A ,		)		->   List[str]:
        return rescale(A ,		scale=A ,		data_format=A ,		**A       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		A = None ,		**A ,		)		->   np.ndarray:
        return normalize(A ,		mean=A ,		std=A ,		data_format=A ,		**A       )


    def 			snake_case_(		self ,		A ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = ChannelDimension.FIRST ,		)		->   np.ndarray:
        if do_resize and size is None or resample is None:
            raise ValueError("""Size and resample must be specified if do_resize is True."""       )

        if do_center_crop and crop_size is None:
            raise ValueError("""Crop size must be specified if do_center_crop is True."""       )

        if do_rescale and rescale_factor is None:
            raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

        if do_normalize and (image_mean is None or image_std is None):
            raise ValueError("""Image mean and std must be specified if do_normalize is True."""       )

        # All transformations expect numpy arrays.
        _SCREAMING_SNAKE_CASE									=			to_numpy_array(A       )

        if do_resize:
            _SCREAMING_SNAKE_CASE									=			self.resize(image=A ,		size=A ,		resample=A       )

        if do_center_crop:
            _SCREAMING_SNAKE_CASE									=			self.center_crop(A ,		size=A       )

        if do_rescale:
            _SCREAMING_SNAKE_CASE									=			self.rescale(image=A ,		scale=A       )

        if do_normalize:
            _SCREAMING_SNAKE_CASE									=			self.normalize(image=A ,		mean=A ,		std=A       )

        _SCREAMING_SNAKE_CASE									=			to_channel_dimension_format(A ,		A       )
        return image





    def 			snake_case_(		self ,		A ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = ChannelDimension.FIRST ,		**A ,		)		->   PIL.Image.Image:
        _SCREAMING_SNAKE_CASE									=			do_resize if do_resize is not None else self.do_resize
        _SCREAMING_SNAKE_CASE									=			resample if resample is not None else self.resample
        _SCREAMING_SNAKE_CASE									=			do_center_crop if do_center_crop is not None else self.do_center_crop
        _SCREAMING_SNAKE_CASE									=			do_rescale if do_rescale is not None else self.do_rescale
        _SCREAMING_SNAKE_CASE									=			rescale_factor if rescale_factor is not None else self.rescale_factor
        _SCREAMING_SNAKE_CASE									=			do_normalize if do_normalize is not None else self.do_normalize
        _SCREAMING_SNAKE_CASE									=			image_mean if image_mean is not None else self.image_mean
        _SCREAMING_SNAKE_CASE									=			image_std if image_std is not None else self.image_std

        _SCREAMING_SNAKE_CASE									=			size if size is not None else self.size
        _SCREAMING_SNAKE_CASE									=			get_size_dict(A ,		default_to_square=A       )
        _SCREAMING_SNAKE_CASE									=			crop_size if crop_size is not None else self.crop_size
        _SCREAMING_SNAKE_CASE									=			get_size_dict(A ,		param_name="""crop_size"""       )

        if not valid_images(A       ):
            raise ValueError(
                """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                """torch.Tensor, tf.Tensor or jax.ndarray."""       )

        _SCREAMING_SNAKE_CASE									=			make_batched(A       )

        _SCREAMING_SNAKE_CASE									=			[
            [
                self._preprocess_image(
                    image=A ,		do_resize=A ,		size=A ,		resample=A ,		do_center_crop=A ,		crop_size=A ,		do_rescale=A ,		rescale_factor=A ,		do_normalize=A ,		image_mean=A ,		image_std=A ,		data_format=A ,		)
                for img in video
            ]
            for video in videos
        ]

        _SCREAMING_SNAKE_CASE									=			{"""pixel_values""": videos}
        return BatchFeature(data=A ,		tensor_type=A       )










'''simple docstring'''

import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowercase_					= HUGGINGFACE_HUB_CACHE


lowercase_					= """config.json"""
lowercase_					= """diffusion_pytorch_model.bin"""
lowercase_					= """diffusion_flax_model.msgpack"""
lowercase_					= """model.onnx"""
lowercase_					= """diffusion_pytorch_model.safetensors"""
lowercase_					= """weights.pb"""
lowercase_					= """https://huggingface.co"""
lowercase_					= default_cache_path
lowercase_					= """diffusers_modules"""
lowercase_					= os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
lowercase_					= ["""fp16""", """non-ema"""]
lowercase_					= """.self_attn"""



