



"""simple docstring"""







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  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     ["""pixel_values"""]



			def __init__(			self							:				Tuple   ,	_lowercase							:				bool = True   ,	_lowercase							:				int = 32   ,	_lowercase							:				Optional[int]=PILImageResampling.BILINEAR   ,	_lowercase							:				bool = True   ,	**_lowercase							:				str   ,	)							->					None:
									snake_case   :			Tuple						    = do_resize
									snake_case   :			str						    = do_rescale
									snake_case   :			Any						    = size_divisor
									snake_case   :			List[Any]						    = resample
									super().__init__(**_lowercase		)



			def 				__lowercase					(			self							:				Tuple   ,	_lowercase							:				np.ndarray   ,	_lowercase							:				int   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				Optional[ChannelDimension] = None   ,	**_lowercase							:				Tuple		)							->					np.ndarray:
									snake_case    ,			snake_case   :			List[str]						    = get_image_size(_lowercase		)
									# Rounds the height and width down to the closest multiple of size_divisor
									snake_case   :			Dict						    = height // size_divisor * size_divisor
									snake_case   :			Any						    = width // size_divisor * size_divisor
									snake_case   :			Union[str, Any]						    = resize(_lowercase   ,	(new_h, new_w)   ,	resample=_lowercase   ,	data_format=_lowercase   ,	**_lowercase		)
									return image



			def 				__lowercase					(			self							:				int   ,	_lowercase							:				np.ndarray   ,	_lowercase							:				float   ,	_lowercase							:				Optional[ChannelDimension] = None   ,	**_lowercase							:				Tuple		)							->					np.ndarray:
									return rescale(image=_lowercase   ,	scale=_lowercase   ,	data_format=_lowercase   ,	**_lowercase		)



			def 				__lowercase					(			self							:				Tuple   ,	_lowercase							:				Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]   ,	_lowercase							:				Optional[bool] = None   ,	_lowercase							:				Optional[int] = None   ,	_lowercase							:				Union[str, Any]=None   ,	_lowercase							:				Optional[bool] = None   ,	_lowercase							:				Optional[Union[TensorType, str]] = None   ,	_lowercase							:				ChannelDimension = ChannelDimension.FIRST   ,	**_lowercase							:				Optional[int]   ,	)							->					BatchFeature:
									snake_case   :			List[str]						    = do_resize if do_resize is not None else self.do_resize
									snake_case   :			List[Any]						    = do_rescale if do_rescale is not None else self.do_rescale
									snake_case   :			Optional[int]						    = size_divisor if size_divisor is not None else self.size_divisor
									snake_case   :			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"		)

									snake_case   :			Union[str, Any]						    = make_list_of_images(_lowercase		)

									if not valid_images(_lowercase		):
															raise ValueError("Invalid image(s)"		)

									# All transformations expect numpy arrays.
									snake_case   :			List[Any]						    = [to_numpy_array(_lowercase		) for img in images]

									if do_resize:
															snake_case   :			List[Any]						    = [self.resize(_lowercase   ,	size_divisor=_lowercase   ,	resample=_lowercase		) for image in images]

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

									snake_case   :			List[str]						    = [to_channel_dimension_format(_lowercase   ,	_lowercase		) for image in images]

									snake_case   :			List[str]						    = {"pixel_values": images}
									return BatchFeature(data=_lowercase   ,	tensor_type=_lowercase		)








"""simple docstring"""







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  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			Union[str, Any]						    = []

						for config_class in list(CONFIG_MAPPING.values() ):
												snake_case   :			List[str]						    = False

												# source code of `config_class`
												snake_case   :			Optional[int]						    = inspect.getsource(lowerCamelCase_ )
												snake_case   :			Optional[int]						    = _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')`
																		snake_case    ,			snake_case   :			Tuple						    = checkpoint

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

												snake_case   :			Any						    = 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:
												snake_case   :			List[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()



