

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
snake_case_        =			'''src/transformers'''


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

snake_case_        =			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)`
snake_case_        =			re.compile('''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')


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

def      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       int				=	[]

     for config_class in list(CONFIG_MAPPING.values()      ):
          lowercase__   :       Tuple				=	False

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

          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')`
               lowercase__				,			lowercase__   :       str				=	checkpoint

               # verify the checkpoint name corresponds to the checkpoint link
               lowercase__   :       Any				=	f"""https://huggingface.co/{ckpt_name}"""
               if ckpt_link == ckpt_link_from_name:
                    lowercase__   :       Dict				=	True
                    break

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

     if len(SCREAMING_SNAKE_CASE_      ) > 0:
          lowercase__   :       Optional[Any]				=	'\n'.join(sorted(SCREAMING_SNAKE_CASE_      )      )
          raise ValueError(f"""The following configurations don't contain any valid checkpoint:\n{message}"""      )


if __name__ == "__main__":
       check_config_docstrings_have_checkpoints()




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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case_        =			logging.get_logger(__name__)


if is_vision_available():
       import PIL


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Optional[Any]														=					["""pixel_values"""]





    def __init__(				self						,       a = True						,       a = None						,       a = PILImageResampling.BICUBIC						,       a = True						,       a = None						,       a = True						,       a = 1 / 255						,       a = True						,       a = None						,       a = None						,       a = True						,       **a						,       ):
         super().__init__(**a)
         lowercase__   :       List[str]				=	size if size is not None else {'shortest_edge': 224}
         lowercase__   :       str				=	get_size_dict(a						,       default_to_square=a)
         lowercase__   :       Optional[int]				=	crop_size if crop_size is not None else {'height': 224, 'width': 224}
         lowercase__   :       Union[str, Any]				=	get_size_dict(a						,       default_to_square=a						,       param_name='crop_size')

         lowercase__   :       List[str]				=	do_resize
         lowercase__   :       List[Any]				=	size
         lowercase__   :       Tuple				=	resample
         lowercase__   :       int				=	do_center_crop
         lowercase__   :       Union[str, Any]				=	crop_size
         lowercase__   :       int				=	do_rescale
         lowercase__   :       List[str]				=	rescale_factor
         lowercase__   :       Tuple				=	do_normalize
         lowercase__   :       str				=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         lowercase__   :       Optional[Any]				=	image_std if image_std is not None else OPENAI_CLIP_STD
         lowercase__   :       List[Any]				=	do_convert_rgb





    def 				snake_case_    (				self						,       a						,       a						,       a = PILImageResampling.BICUBIC						,       a = None						,       **a						,       ):
         lowercase__   :       str				=	get_size_dict(a						,       default_to_square=a)
         if "shortest_edge" not in size:
              raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""")
         lowercase__   :       str				=	get_resize_output_image_size(a						,       size=size['shortest_edge']						,       default_to_square=a)
         return resize(a						,       size=a						,       resample=a						,       data_format=a						,       **a)





    def 				snake_case_    (				self						,       a						,       a						,       a = None						,       **a						,       ):
         lowercase__   :       List[str]				=	get_size_dict(a)
         if "height" not in size or "width" not in size:
              raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""")
         return center_crop(a						,       size=(size['height'], size['width'])						,       data_format=a						,       **a)





    def 				snake_case_    (				self						,       a						,       a						,       a = None						,       **a						,       ):
         return rescale(a						,       scale=a						,       data_format=a						,       **a)





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a = None						,       **a						,       ):
         return normalize(a						,       mean=a						,       std=a						,       data_format=a						,       **a)





    def 				snake_case_    (				self						,       a						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = ChannelDimension.FIRST						,       **a						,       ):
         lowercase__   :       int				=	do_resize if do_resize is not None else self.do_resize
         lowercase__   :       Tuple				=	size if size is not None else self.size
         lowercase__   :       Union[str, Any]				=	get_size_dict(a						,       param_name='size'						,       default_to_square=a)
         lowercase__   :       Optional[Any]				=	resample if resample is not None else self.resample
         lowercase__   :       int				=	do_center_crop if do_center_crop is not None else self.do_center_crop
         lowercase__   :       List[Any]				=	crop_size if crop_size is not None else self.crop_size
         lowercase__   :       Union[str, Any]				=	get_size_dict(a						,       param_name='crop_size'						,       default_to_square=a)
         lowercase__   :       Dict				=	do_rescale if do_rescale is not None else self.do_rescale
         lowercase__   :       int				=	rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase__   :       List[str]				=	do_normalize if do_normalize is not None else self.do_normalize
         lowercase__   :       Optional[Any]				=	image_mean if image_mean is not None else self.image_mean
         lowercase__   :       List[str]				=	image_std if image_std is not None else self.image_std
         lowercase__   :       Any				=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         lowercase__   :       str				=	make_list_of_images(a)

         if not valid_images(a):
              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:
              raise ValueError('Size 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.')

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
              lowercase__   :       str				=	[convert_to_rgb(a) for image in images]

         # All transformations expect numpy arrays.
         lowercase__   :       Dict				=	[to_numpy_array(a) for image in images]

         if do_resize:
              lowercase__   :       Tuple				=	[self.resize(image=a						,       size=a						,       resample=a) for image in images]

         if do_center_crop:
              lowercase__   :       List[str]				=	[self.center_crop(image=a						,       size=a) for image in images]

         if do_rescale:
              lowercase__   :       str				=	[self.rescale(image=a						,       scale=a) for image in images]

         if do_normalize:
              lowercase__   :       Tuple				=	[self.normalize(image=a						,       mean=a						,       std=a) for image in images]

         lowercase__   :       Optional[int]				=	[to_channel_dimension_format(a						,       a) for image in images]

         lowercase__   :       Dict				=	{'pixel_values': images}
         return BatchFeature(data=a						,       tensor_type=a)

