



"""simple docstring"""







import argparse
from collections import defaultdict

import yaml


a_							=    """docs/source/en/_toctree.yml"""

def     UpperCAmelCase_					(				__a   :			Union[str, Any]						):




			'''simple docstring'''



			_lowerCamelCase					:					List[Any]	       =    defaultdict(_A						)
			for doc in model_doc:
						counts[doc["local"]] += 1
			_lowerCamelCase					:					List[str]	       =    [key for key, value in counts.items() if value > 1]

			_lowerCamelCase					:					Dict	       =    []
			for duplicate_key in duplicates:
						_lowerCamelCase					:					List[str]	       =    list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}						)
						if len(_A						) > 1:
									raise ValueError(
									    f"{duplicate_key} is present several times in the documentation table of content at "
									    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
									    'others.'						)
						# Only add this once
						new_doc.append({'local': duplicate_key, 'title': titles[0]}						)

			# Add none duplicate-keys
			new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]						)

			# Sort
			return sorted(_A      ,							key=lambda __a						: s["title"].lower()						)

def     UpperCAmelCase_					(				__a   :			Optional[Any]=False						):




			'''simple docstring'''



			with open(_A      ,							encoding='utf-8'						) as f:
						_lowerCamelCase					:					Dict	       =    yaml.safe_load(f.read()						)

			# Get to the API doc
			_lowerCamelCase					:					Union[str, Any]	       =    0
			while content[api_idx]["title"] != "API":
						api_idx += 1
			_lowerCamelCase					:					List[str]	       =    content[api_idx]["""sections"""]

			# Then to the model doc
			_lowerCamelCase					:					Dict	       =    0
			while api_doc[model_idx]["title"] != "Models":
						model_idx += 1

			_lowerCamelCase					:					Dict	       =    api_doc[model_idx]["""sections"""]

			_lowerCamelCase					:					Tuple	       =    [(idx, section) for idx, section in enumerate(_A						) if """sections""" in section]
			_lowerCamelCase					:					Union[str, Any]	       =    False
			for idx, modality_doc in modalities_docs:
						_lowerCamelCase					:					Any	       =    modality_doc["""sections"""]
						_lowerCamelCase					:					Any	       =    clean_model_doc_toc(_A						)

						if old_modality_doc != new_modality_doc:
									_lowerCamelCase					:					Tuple	       =    True
									if overwrite:
												_lowerCamelCase					:					Dict	       =    new_modality_doc

			if diff:
						if overwrite:
									_lowerCamelCase					:					Union[str, Any]	       =    model_doc
									_lowerCamelCase					:					Optional[Any]	       =    api_doc
									with open(_A      ,							'w'      ,							encoding='utf-8'						) as f:
												f.write(yaml.dump(_A      ,							allow_unicode=_A						)						)
						else:
									raise ValueError(
									    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'						)


if __name__ == "__main__":
	a_							=    argparse.ArgumentParser()
	parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
	a_							=    parser.parse_args()

	check_model_doc(args.fix_and_overwrite)













"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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 logging


if is_vision_available():
				import PIL

_lowerCAmelCase            =	logging.get_logger(__name__)


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

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

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

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


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : str												=		["""pixel_values"""]






						def __init__(		self  :str			, __magic_name__  :bool = True			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :PILImageResampling = PILImageResampling.BILINEAR			, __magic_name__  :bool = True			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :bool = True			, __magic_name__  :Union[int, float] = 1 / 255			, __magic_name__  :bool = True			, __magic_name__  :bool = True			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, **__magic_name__  :List[str]			, )      ->None:
										super().__init__(**__magic_name__				)
										lowercase    :      Union[str, Any]       = size if size is not None else {"""shortest_edge""": 256}
										lowercase    :      Tuple       = get_size_dict(__magic_name__			, default_to_square=__magic_name__				)
										lowercase    :      List[Any]       = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										lowercase    :      List[str]       = get_size_dict(__magic_name__			, param_name="""crop_size"""				)

										lowercase    :      Tuple       = do_resize
										lowercase    :      Optional[Any]       = size
										lowercase    :      Union[str, Any]       = do_center_crop
										lowercase    :      List[Any]       = crop_size
										lowercase    :      Union[str, Any]       = resample
										lowercase    :      Optional[Any]       = do_rescale
										lowercase    :      int       = rescale_factor
										lowercase    :      Dict       = offset
										lowercase    :      Optional[int]       = do_normalize
										lowercase    :      Optional[Any]       = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowercase    :      Tuple       = image_std if image_std is not None else IMAGENET_STANDARD_STD






						def      __snake_case      (		self  :Any			, __magic_name__  :np.ndarray			, __magic_name__  :Dict[str, int]			, __magic_name__  :PILImageResampling = PILImageResampling.BILINEAR			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :Optional[Any]			, )      ->np.ndarray:
										lowercase    :      Optional[int]       = get_size_dict(__magic_name__			, default_to_square=__magic_name__				)
										if "shortest_edge" in size:
														lowercase    :      int       = get_resize_output_image_size(__magic_name__			, size["""shortest_edge"""]			, default_to_square=__magic_name__				)
										elif "height" in size and "width" in size:
														lowercase    :      Tuple       = (size["""height"""], size["""width"""])
										else:
														raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""				)
										return resize(__magic_name__			, size=__magic_name__			, resample=__magic_name__			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :np.ndarray			, __magic_name__  :Dict[str, int]			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :str			, )      ->np.ndarray:
										lowercase    :      Any       = get_size_dict(__magic_name__				)
										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(__magic_name__			, size=(size["""height"""], size["""width"""])			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :np.ndarray			, __magic_name__  :Union[int, float]			, __magic_name__  :bool = True			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :Optional[Any]			, )      ->Any:
										lowercase    :      Optional[int]       = image.astype(np.floataa				)
										if offset:
														lowercase    :      Optional[int]       = image - (scale / 2)
										return rescale(__magic_name__			, scale=__magic_name__			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :int			, __magic_name__  :np.ndarray			, __magic_name__  :Union[float, List[float]]			, __magic_name__  :Union[float, List[float]]			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :Union[str, Any]			, )      ->np.ndarray:
										return normalize(__magic_name__			, mean=__magic_name__			, std=__magic_name__			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :List[Any]			, __magic_name__  :ImageInput			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :PILImageResampling = None			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :bool = None			, __magic_name__  :float = None			, __magic_name__  :bool = None			, __magic_name__  :bool = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[ChannelDimension] = 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."""				)

										if offset and not do_rescale:
														raise ValueError("""For offset, do_rescale must also be set to True."""				)

										# All transformations expect numpy arrays.
										lowercase    :      Optional[Any]       = to_numpy_array(__magic_name__				)

										if do_resize:
														lowercase    :      Any       = self.resize(image=__magic_name__			, size=__magic_name__			, resample=__magic_name__				)

										if do_center_crop:
														lowercase    :      Any       = self.center_crop(__magic_name__			, size=__magic_name__				)

										if do_rescale:
														lowercase    :      Optional[int]       = self.rescale(image=__magic_name__			, scale=__magic_name__			, offset=__magic_name__				)

										if do_normalize:
														lowercase    :      Union[str, Any]       = self.normalize(image=__magic_name__			, mean=__magic_name__			, std=__magic_name__				)

										lowercase    :      Optional[Any]       = to_channel_dimension_format(__magic_name__			, __magic_name__				)
										return image






						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :ImageInput			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :PILImageResampling = None			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :bool = None			, __magic_name__  :float = None			, __magic_name__  :bool = None			, __magic_name__  :bool = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[str, TensorType]] = None			, __magic_name__  :ChannelDimension = ChannelDimension.FIRST			, **__magic_name__  :List[str]			, )      ->PIL.Image.Image:
										lowercase    :      List[Any]       = do_resize if do_resize is not None else self.do_resize
										lowercase    :      Dict       = resample if resample is not None else self.resample
										lowercase    :      Optional[int]       = do_center_crop if do_center_crop is not None else self.do_center_crop
										lowercase    :      List[Any]       = do_rescale if do_rescale is not None else self.do_rescale
										lowercase    :      Tuple       = rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase    :      Optional[Any]       = offset if offset is not None else self.offset
										lowercase    :      str       = do_normalize if do_normalize is not None else self.do_normalize
										lowercase    :      List[str]       = image_mean if image_mean is not None else self.image_mean
										lowercase    :      Union[str, Any]       = image_std if image_std is not None else self.image_std

										lowercase    :      Optional[int]       = size if size is not None else self.size
										lowercase    :      Optional[Any]       = get_size_dict(__magic_name__			, default_to_square=__magic_name__				)
										lowercase    :      Tuple       = crop_size if crop_size is not None else self.crop_size
										lowercase    :      List[str]       = get_size_dict(__magic_name__			, param_name="""crop_size"""				)

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

										lowercase    :      Tuple       = make_batched(__magic_name__				)

										lowercase    :      List[str]       = [
										    [
										        self._preprocess_image(
										            image=__magic_name__			, do_resize=__magic_name__			, size=__magic_name__			, resample=__magic_name__			, do_center_crop=__magic_name__			, crop_size=__magic_name__			, do_rescale=__magic_name__			, rescale_factor=__magic_name__			, offset=__magic_name__			, do_normalize=__magic_name__			, image_mean=__magic_name__			, image_std=__magic_name__			, data_format=__magic_name__			, )
										        for img in video
										    ]
										    for video in videos
										]

										lowercase    :      Dict       = {"""pixel_values""": videos}
										return BatchFeature(data=__magic_name__			, tensor_type=__magic_name__				)
