





'''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

UpperCAmelCase_      							=     logging.get_logger(__name__)





def 						lowerCamelCase__     (     UpperCamelCase__			:       Tuple						)			-> List[List[ImageInput]]:



					'''simple docstring'''




					if isinstance(UpperCamelCase__       ,    (list, tuple)						) and isinstance(videos[0]       ,    (list, tuple)						) and is_valid_image(videos[0][0]						):
										return videos

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

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

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




class 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	['''pixel_values''']




					def __init__(		self				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = PILImageResampling.BILINEAR				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = 1 / 255				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							None:
										super().__init__(**lowerCAmelCase_  )
										_snake_case									=				size if size is not None else {'shortest_edge': 256}
										_snake_case									=				get_size_dict(lowerCAmelCase_				,						default_to_square=lowerCAmelCase_  )
										_snake_case									=				crop_size if crop_size is not None else {'height': 224, 'width': 224}
										_snake_case									=				get_size_dict(lowerCAmelCase_				,						param_name='crop_size'  )

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




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = PILImageResampling.BILINEAR				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							np.ndarray:
										_snake_case									=				get_size_dict(lowerCAmelCase_				,						default_to_square=lowerCAmelCase_  )
										if "shortest_edge" in size:
															_snake_case									=				get_resize_output_image_size(lowerCAmelCase_				,						size['shortest_edge']				,						default_to_square=lowerCAmelCase_  )
										elif "height" in size and "width" in size:
															_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(lowerCAmelCase_				,						size=lowerCAmelCase_				,						resample=lowerCAmelCase_				,						data_format=lowerCAmelCase_				,						**lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							np.ndarray:
										_snake_case									=				get_size_dict(lowerCAmelCase_  )
										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(lowerCAmelCase_				,						size=(size['height'], size['width'])				,						data_format=lowerCAmelCase_				,						**lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							int:
										_snake_case									=				image.astype(np.floataa  )
										if offset:
															_snake_case									=				image - (scale / 2)
										return rescale(lowerCAmelCase_				,						scale=lowerCAmelCase_				,						data_format=lowerCAmelCase_				,						**lowerCAmelCase_  )




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




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = 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.
										_snake_case									=				to_numpy_array(lowerCAmelCase_  )

										if do_resize:
															_snake_case									=				self.resize(image=lowerCAmelCase_				,						size=lowerCAmelCase_				,						resample=lowerCAmelCase_  )

										if do_center_crop:
															_snake_case									=				self.center_crop(lowerCAmelCase_				,						size=lowerCAmelCase_  )

										if do_rescale:
															_snake_case									=				self.rescale(image=lowerCAmelCase_				,						scale=lowerCAmelCase_				,						offset=lowerCAmelCase_  )

										if do_normalize:
															_snake_case									=				self.normalize(image=lowerCAmelCase_				,						mean=lowerCAmelCase_				,						std=lowerCAmelCase_  )

										_snake_case									=				to_channel_dimension_format(lowerCAmelCase_				,						lowerCAmelCase_  )
										return image






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

										_snake_case									=				size if size is not None else self.size
										_snake_case									=				get_size_dict(lowerCAmelCase_				,						default_to_square=lowerCAmelCase_  )
										_snake_case									=				crop_size if crop_size is not None else self.crop_size
										_snake_case									=				get_size_dict(lowerCAmelCase_				,						param_name='crop_size'  )

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

										_snake_case									=				make_batched(lowerCAmelCase_  )

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

										_snake_case									=				{'pixel_values': videos}
										return BatchFeature(data=lowerCAmelCase_				,						tensor_type=lowerCAmelCase_  )










import argparse
from collections import defaultdict

import yaml


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





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[Any]						)			-> str:



					'''simple docstring'''




					_snake_case									=				defaultdict(UpperCamelCase__						)
					_snake_case									=				[]
					_snake_case									=				[]
					for doc in doc_list:
										if "local" in doc:
															counts[doc["local"]] += 1

										if doc["title"].lower() == "overview":
															overview_doc.append({'local': doc['local'], 'title': doc['title']}						)
										else:
															new_doc_list.append(UpperCamelCase__						)

					_snake_case									=				new_doc_list
					_snake_case									=				[key for key, value in counts.items() if value > 1]

					_snake_case									=				[]
					for duplicate_key in duplicates:
										_snake_case									=				list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}						)
										if len(UpperCamelCase__						) > 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 doc_list if 'local' not in counts or counts[doc['local']] == 1]						)
					_snake_case									=				sorted(UpperCamelCase__       ,    key=lambda UpperCamelCase__						: s["title"].lower()						)

					# "overview" gets special treatment and is always first
					if len(UpperCamelCase__						) > 1:
										raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'						)

					overview_doc.extend(UpperCamelCase__						)

					# Sort
					return overview_doc





def 						lowerCamelCase__     (     UpperCamelCase__			:       Dict=False						)			-> Optional[int]:



					'''simple docstring'''




					with open(UpperCamelCase__       ,    encoding='utf-8'						) as f:
										_snake_case									=				yaml.safe_load(f.read()						)

					# Get to the API doc
					_snake_case									=				0
					while content[api_idx]["title"] != "API":
										api_idx += 1
					_snake_case									=				content[api_idx]['sections']

					# Then to the model doc
					_snake_case									=				0
					while api_doc[scheduler_idx]["title"] != "Schedulers":
										scheduler_idx += 1

					_snake_case									=				api_doc[scheduler_idx]['sections']
					_snake_case									=				clean_doc_toc(UpperCamelCase__						)

					_snake_case									=				False
					if new_scheduler_doc != scheduler_doc:
										_snake_case									=				True
										if overwrite:
															_snake_case									=				new_scheduler_doc

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





def 						lowerCamelCase__     (     UpperCamelCase__			:       Tuple=False						)			-> List[Any]:



					'''simple docstring'''




					with open(UpperCamelCase__       ,    encoding='utf-8'						) as f:
										_snake_case									=				yaml.safe_load(f.read()						)

					# Get to the API doc
					_snake_case									=				0
					while content[api_idx]["title"] != "API":
										api_idx += 1
					_snake_case									=				content[api_idx]['sections']

					# Then to the model doc
					_snake_case									=				0
					while api_doc[pipeline_idx]["title"] != "Pipelines":
										pipeline_idx += 1

					_snake_case									=				False
					_snake_case									=				api_doc[pipeline_idx]['sections']
					_snake_case									=				[]

					# sort sub pipeline docs
					for pipeline_doc in pipeline_docs:
										if "section" in pipeline_doc:
															_snake_case									=				pipeline_doc['section']
															_snake_case									=				clean_doc_toc(UpperCamelCase__						)
															if overwrite:
																				_snake_case									=				new_sub_pipeline_doc
										new_pipeline_docs.append(UpperCamelCase__						)

					# sort overall pipeline doc
					_snake_case									=				clean_doc_toc(UpperCamelCase__						)

					if new_pipeline_docs != pipeline_docs:
										_snake_case									=				True
										if overwrite:
															_snake_case									=				new_pipeline_docs

					if diff:
										if overwrite:
															_snake_case									=				api_doc
															with open(UpperCamelCase__       ,    'w'       ,    encoding='utf-8'						) as f:
																				f.write(yaml.dump(UpperCamelCase__       ,    allow_unicode=UpperCamelCase__						)						)
										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__":
					UpperCAmelCase_      							=     argparse.ArgumentParser()
					parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
					UpperCAmelCase_      							=     parser.parse_args()

					check_scheduler_doc(args.fix_and_overwrite)
					check_pipeline_doc(args.fix_and_overwrite)



