



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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   List[Any]:
			A__	  =     b.T
			A__	  =     np.sum(np.square(__UpperCamelCase       )			,						axis=1       )
			A__	  =     np.sum(np.square(__UpperCamelCase       )			,						axis=0       )
			A__	  =     np.matmul(__UpperCamelCase			,						__UpperCamelCase       )
			A__	  =     aa[:, None] - 2 * ab + ba[None, :]
			return d


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     x.reshape(-1			,						3       )
			A__	  =     squared_euclidean_distance(__UpperCamelCase			,						__UpperCamelCase       )
			return np.argmin(__UpperCamelCase			,						axis=1       )






class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		Dict      							=     ["pixel_values"]



					def __init__(  self       :							List[str]					,       _snake_case       :							Optional[Union[List[List[int]], np.ndarray]] = None					,       _snake_case       :							bool = True					,       _snake_case       :							Dict[str, int] = None					,       _snake_case       :							PILImageResampling = PILImageResampling.BILINEAR					,       _snake_case       :							bool = True					,       _snake_case       :							bool = True					,       **_snake_case       :							Dict					,       ):






								"""simple docstring"""


								super().__init__(**_snake_case       )
								A__	  =     size if size is not None else {'height': 2_56, 'width': 2_56}
								A__	  =     get_size_dict(_snake_case       )
								A__	  =     np.array(_snake_case       ) if clusters is not None else None
								A__	  =     do_resize
								A__	  =     size
								A__	  =     resample
								A__	  =     do_normalize
								A__	  =     do_color_quantize



					def      _a				(  self       :							int					,       _snake_case       :							np.ndarray					,       _snake_case       :							Dict[str, int]					,       _snake_case       :							PILImageResampling = PILImageResampling.BILINEAR					,       _snake_case       :							Optional[Union[str, ChannelDimension]] = None					,       **_snake_case       :							str					,       ):






								"""simple docstring"""


								A__	  =     get_size_dict(_snake_case       )
								if "height" not in size or "width" not in size:
											raise ValueError(F'''Size dictionary must contain both height and width keys. Got {size.keys()}'''       )
								return resize(
								    _snake_case					,       size=(size['height'], size['width'])					,       resample=_snake_case					,       data_format=_snake_case					,       **_snake_case       )



					def      _a				(  self       :							Any					,       _snake_case       :							np.ndarray					,       _snake_case       :							Optional[Union[str, ChannelDimension]] = None					,       ):






								"""simple docstring"""


								A__	  =     rescale(image=_snake_case					,       scale=1 / 127.5					,       data_format=_snake_case       )
								A__	  =     image - 1
								return image



					def      _a				(  self       :							List[Any]					,       _snake_case       :							ImageInput					,       _snake_case       :							bool = None					,       _snake_case       :							Dict[str, int] = None					,       _snake_case       :							PILImageResampling = None					,       _snake_case       :							bool = None					,       _snake_case       :							Optional[bool] = None					,       _snake_case       :							Optional[Union[List[List[int]], np.ndarray]] = None					,       _snake_case       :							Optional[Union[str, TensorType]] = None					,       _snake_case       :							Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					,       **_snake_case       :							int					,       ):






								"""simple docstring"""


								A__	  =     do_resize if do_resize is not None else self.do_resize
								A__	  =     size if size is not None else self.size
								A__	  =     get_size_dict(_snake_case       )
								A__	  =     resample if resample is not None else self.resample
								A__	  =     do_normalize if do_normalize is not None else self.do_normalize
								A__	  =     do_color_quantize if do_color_quantize is not None else self.do_color_quantize
								A__	  =     clusters if clusters is not None else self.clusters
								A__	  =     np.array(_snake_case       )

								A__	  =     make_list_of_images(_snake_case       )

								if not valid_images(_snake_case       ):
											raise ValueError(
											    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
											    'torch.Tensor, tf.Tensor or jax.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_color_quantize and clusters is None:
											raise ValueError('Clusters must be specified if do_color_quantize is True.'       )

								# All transformations expect numpy arrays.
								A__	  =     [to_numpy_array(_snake_case       ) for image in images]

								if do_resize:
											A__	  =     [self.resize(image=_snake_case					,       size=_snake_case					,       resample=_snake_case       ) for image in images]

								if do_normalize:
											A__	  =     [self.normalize(image=_snake_case       ) for image in images]

								if do_color_quantize:
											A__	  =     [to_channel_dimension_format(_snake_case					,       ChannelDimension.LAST       ) for image in images]
											# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
											A__	  =     np.array(_snake_case       )
											A__	  =     color_quantize(_snake_case					,       _snake_case       ).reshape(images.shape[:-1]       )

											# flatten to (batch_size, height*width)
											A__	  =     images.shape[0]
											A__	  =     images.reshape(_snake_case					,       -1       )

											# We need to convert back to a list of images to keep consistent behaviour across processors.
											A__	  =     list(_snake_case       )
								else:
											A__	  =     [to_channel_dimension_format(_snake_case					,       _snake_case       ) for image in images]

								A__	  =     {'input_ids': images}
								return BatchFeature(data=_snake_case					,       tensor_type=_snake_case       )









import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE__   			=   '''docs/source/en/_toctree.yml'''


def A    (							__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     defaultdict(__UpperCamelCase       )
			for doc in model_doc:
						counts[doc["local"]] += 1
			A__	  =     [key for key, value in counts.items() if value > 1]

			A__	  =     []
			for duplicate_key in duplicates:
						A__	  =     list({doc['title'] for doc in model_doc 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 model_doc if counts[doc['local']] == 1]       )

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


def A    (							__UpperCamelCase=False       )    ->   str:
			with open(__UpperCamelCase			,						encoding='utf-8'       ) as f:
						A__	  =     yaml.safe_load(f.read()       )

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

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

			A__	  =     api_doc[model_idx]['sections']

			A__	  =     [(idx, section) for idx, section in enumerate(__UpperCamelCase       ) if 'sections' in section]
			A__	  =     False
			for idx, modality_doc in modalities_docs:
						A__	  =     modality_doc['sections']
						A__	  =     clean_model_doc_toc(__UpperCamelCase       )

						if old_modality_doc != new_modality_doc:
									A__	  =     True
									if overwrite:
												A__	  =     new_modality_doc

			if diff:
						if overwrite:
									A__	  =     model_doc
									A__	  =     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__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()
							parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()

							check_model_doc(args.fix_and_overwrite)




