






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A        =			logging.get_logger(__name__)


class 							__a							(  lowercase__						):





		'''simple docstring'''



		UpperCAmelCase__      :				List[str]				    =						["""pixel_values"""]


		def __init__(						self ,    UpperCamelCase__ = True ,    UpperCamelCase__ = 32 ,    UpperCamelCase__=PILImageResampling.BILINEAR ,    UpperCamelCase__ = True ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   do_resize
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   do_rescale
						SCREAMING_SNAKE_CASE_   :	int													=   size_divisor
						SCREAMING_SNAKE_CASE_   :	Any													=   resample
						super().__init__(**__lowerCamelCase       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    **UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   get_image_size(__lowerCamelCase       )
						# Rounds the height and width down to the closest multiple of size_divisor
						SCREAMING_SNAKE_CASE_   :	Tuple													=   height // size_divisor * size_divisor
						SCREAMING_SNAKE_CASE_   :	Dict													=   width // size_divisor * size_divisor
						SCREAMING_SNAKE_CASE_   :	Tuple													=   resize(__lowerCamelCase ,    (new_h, new_w) ,    resample=__lowerCamelCase ,    data_format=__lowerCamelCase ,    **__lowerCamelCase       )
						return image


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    **UpperCamelCase__       ):
						return rescale(image=__lowerCamelCase ,    scale=__lowerCamelCase ,    data_format=__lowerCamelCase ,    **__lowerCamelCase       )






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__=None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = ChannelDimension.FIRST ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   do_rescale if do_rescale is not None else self.do_rescale
						SCREAMING_SNAKE_CASE_   :	Dict													=   size_divisor if size_divisor is not None else self.size_divisor
						SCREAMING_SNAKE_CASE_   :	List[str]													=   resample if resample is not None else self.resample

						if do_resize and size_divisor is None:
										raise ValueError('size_divisor is required for resizing'       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   make_list_of_images(__lowerCamelCase       )

						if not valid_images(__lowerCamelCase       ):
										raise ValueError('Invalid image(s)'       )

						# All transformations expect numpy arrays.
						SCREAMING_SNAKE_CASE_   :	Dict													=   [to_numpy_array(__lowerCamelCase       ) for img in images]

						if do_resize:
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [self.resize(__lowerCamelCase ,    size_divisor=__lowerCamelCase ,    resample=__lowerCamelCase       ) for image in images]

						if do_rescale:
										SCREAMING_SNAKE_CASE_   :	Any													=   [self.rescale(__lowerCamelCase ,    scale=1 / 255       ) for image in images]

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [to_channel_dimension_format(__lowerCamelCase ,    __lowerCamelCase       ) for image in images]

						SCREAMING_SNAKE_CASE_   :	Dict													=   {"pixel_values": images}
						return BatchFeature(data=__lowerCamelCase ,    tensor_type=__lowerCamelCase       )











import importlib
import inspect
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
A        =			'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
A        =			importlib.util.spec_from_file_location(
    'transformers',
    os.path.join(PATH_TO_TRANSFORMERS, '__init__.py'),
    submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
A        =			spec.loader.load_module()

A        =			transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
A        =			re.compile('\[(.+?)\]\((https://huggingface\.co/.+?)\)')


A        =			{
    'CLIPConfigMixin',
    'DecisionTransformerConfigMixin',
    'EncoderDecoderConfigMixin',
    'RagConfigMixin',
    'SpeechEncoderDecoderConfigMixin',
    'VisionEncoderDecoderConfigMixin',
    'VisionTextDualEncoderConfigMixin',
}



def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   []

				for config_class in list(CONFIG_MAPPING.values()      ):
								SCREAMING_SNAKE_CASE_   :	int													=   False

								# source code of `config_class`
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   inspect.getsource(lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   _re_checkpoint.findall(lowerCAmelCase__      )

								for checkpoint in checkpoints:
												# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
												# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
												SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Tuple													=   checkpoint

												# verify the checkpoint name corresponds to the checkpoint link
												SCREAMING_SNAKE_CASE_   :	List[str]													=   F'''https://huggingface.co/{ckpt_name}'''
												if ckpt_link == ckpt_link_from_name:
																SCREAMING_SNAKE_CASE_   :	Optional[int]													=   True
																break

								SCREAMING_SNAKE_CASE_   :	Tuple													=   config_class.__name__
								if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
												configs_without_checkpoint.append(lowerCAmelCase__      )

				if len(lowerCAmelCase__      ) > 0:
								SCREAMING_SNAKE_CASE_   :	str													=   '\n'.join(sorted(lowerCAmelCase__      )      )
								raise ValueError(F'''The following configurations don\'t contain any valid checkpoint:\n{message}'''      )


if __name__ == "__main__":
				check_config_docstrings_have_checkpoints()