'''simple docstring'''




import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
snake_case	=      '''src/transformers'''
snake_case	=      '''docs/source/en'''
snake_case	=      '''.'''







def       A_ (			_lowerCamelCase      :				List[Any]	,	_lowerCamelCase      :				Dict	,	_lowerCamelCase      :				int     ):




      with open(__A	,	'r'	,	encoding='utf-8'	,	newline='\n'     ) as f:
            _lowerCAmelCase		 = f.readlines()
      # Find the start prompt.
      _lowerCAmelCase		 = 0
      while not lines[start_index].startswith(__A     ):
            start_index += 1
      start_index += 1

      _lowerCAmelCase		 = start_index
      while not lines[end_index].startswith(__A     ):
            end_index += 1
      end_index -= 1

      while len(lines[start_index]     ) <= 1:
            start_index += 1
      while len(lines[end_index]     ) <= 1:
            end_index -= 1
      end_index += 1
      return "".join(lines[start_index:end_index]     ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
snake_case	=      '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
snake_case	=      re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
snake_case	=      re.compile(R'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
snake_case	=      re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the transformers module imported is the one in the repo.
snake_case	=      direct_transformers_import(TRANSFORMERS_PATH)







def       A_ (			_lowerCamelCase      :				Union[str, Any]     ):
      _lowerCAmelCase		 = re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'	,	__A     )
      return [m.group(0     ) for m in matches]







def       A_ (			_lowerCamelCase      :				Tuple	,	_lowerCamelCase      :				List[Any]     ):
      _lowerCAmelCase		 = 2 if text == '''✅''' or text == '''❌''' else len(__A     )
      _lowerCAmelCase		 = (width - text_length) // 2
      _lowerCAmelCase		 = width - text_length - left_indent
      return " " * left_indent + text + " " * right_indent







def       A_ (			):
      _lowerCAmelCase		 = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      _lowerCAmelCase		 = {
          name: config_maping_names[code]
          for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
          if code in config_maping_names
      }
      _lowerCAmelCase		 = {name: config.replace('Config'	,	''     ) for name, config in model_name_to_config.items()}

      # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
      _lowerCAmelCase		 = collections.defaultdict(__A     )
      _lowerCAmelCase		 = collections.defaultdict(__A     )
      _lowerCAmelCase		 = collections.defaultdict(__A     )
      _lowerCAmelCase		 = collections.defaultdict(__A     )
      _lowerCAmelCase		 = collections.defaultdict(__A     )

      # Let's lookup through all transformers object (once).
      for attr_name in dir(__A     ):
            _lowerCAmelCase		 = None
            if attr_name.endswith('Tokenizer'     ):
                  _lowerCAmelCase		 = slow_tokenizers
                  _lowerCAmelCase		 = attr_name[:-9]
            elif attr_name.endswith('TokenizerFast'     ):
                  _lowerCAmelCase		 = fast_tokenizers
                  _lowerCAmelCase		 = attr_name[:-13]
            elif _re_tf_models.match(__A     ) is not None:
                  _lowerCAmelCase		 = tf_models
                  _lowerCAmelCase		 = _re_tf_models.match(__A     ).groups()[0]
            elif _re_flax_models.match(__A     ) is not None:
                  _lowerCAmelCase		 = flax_models
                  _lowerCAmelCase		 = _re_flax_models.match(__A     ).groups()[0]
            elif _re_pt_models.match(__A     ) is not None:
                  _lowerCAmelCase		 = pt_models
                  _lowerCAmelCase		 = _re_pt_models.match(__A     ).groups()[0]

            if lookup_dict is not None:
                  while len(__A     ) > 0:
                        if attr_name in model_name_to_prefix.values():
                              _lowerCAmelCase		 = True
                              break
                        # Try again after removing the last word in the name
                        _lowerCAmelCase		 = ''''''.join(camel_case_split(__A     )[:-1]     )

                        # Let's build that table!
      _lowerCAmelCase		 = list(model_name_to_config.keys()     )
      model_names.sort(key=str.lower     )
      _lowerCAmelCase		 = ['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
      # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
      _lowerCAmelCase		 = [len(__A     ) + 2 for c in columns]
      _lowerCAmelCase		 = max([len(__A     ) for name in model_names]     ) + 2

      # Build the table per se
      _lowerCAmelCase		 = '''|''' + '''|'''.join([_center_text(__A	,	__A     ) for c, w in zip(__A	,	__A     )]     ) + '''|\n'''
      # Use ":-----:" format to center-aligned table cell texts
      table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths]     ) + "|\n"

      _lowerCAmelCase		 = {True: '''✅''', False: '''❌'''}
      for name in model_names:
            _lowerCAmelCase		 = model_name_to_prefix[name]
            _lowerCAmelCase		 = [
                name,
                check[slow_tokenizers[prefix]],
                check[fast_tokenizers[prefix]],
                check[pt_models[prefix]],
                check[tf_models[prefix]],
                check[flax_models[prefix]],
            ]
            table += "|" + "|".join([_center_text(__A	,	__A     ) for l, w in zip(__A	,	__A     )]     ) + "|\n"
      return table







def       A_ (			_lowerCamelCase      :				Any=False     ):
      _lowerCAmelCase		 = _find_text_in_file(
          filename=os.path.join(__A	,	'index.md'     )	,	start_prompt='<!--This table is updated automatically from the auto modules'	,	end_prompt='<!-- End table-->'	,	)
      _lowerCAmelCase		 = get_model_table_from_auto_modules()

      if current_table != new_table:
            if overwrite:
                  with open(os.path.join(__A	,	'index.md'     )	,	'w'	,	encoding='utf-8'	,	newline='\n'     ) as f:
                        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]     )
            else:
                  raise ValueError(
                      'The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.'     )


if __name__ == "__main__":
 snake_case	=      argparse.ArgumentParser()
 parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
 snake_case	=      parser.parse_args()

 check_model_table(args.fix_and_overwrite)













'''simple docstring'''



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,
    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 TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)


def 				lowercase_					(     __A			:							Tuple      )							->   List[List[ImageInput]]:



 """simple docstring"""




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

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

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

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





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      ['''pixel_values''']






      def __init__(			self      :		List[Any]  ,   UpperCAmelCase      :		bool = True  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		PILImageResampling = PILImageResampling.BILINEAR  ,   UpperCAmelCase      :		bool = True  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		bool = True  ,   UpperCAmelCase      :		Union[int, float] = 1 / 255  ,   UpperCAmelCase      :		bool = True  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   **UpperCAmelCase      :		Tuple  ,   )	->	None:


       '''simple docstring'''

       super().__init__(**UpperCAmelCase		)
       lowercase       : Tuple						=size if size is not None else {'''shortest_edge''': 224}
       lowercase       : str						=get_size_dict(UpperCAmelCase  ,   default_to_square=UpperCAmelCase		)
       lowercase       : List[str]						=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       lowercase       : List[Any]						=get_size_dict(UpperCAmelCase  ,   param_name='''crop_size'''		)

       lowercase       : Tuple						=do_resize
       lowercase       : Any						=size
       lowercase       : Optional[Any]						=do_center_crop
       lowercase       : str						=crop_size
       lowercase       : Any						=resample
       lowercase       : List[Any]						=do_rescale
       lowercase       : Dict						=rescale_factor
       lowercase       : List[str]						=do_normalize
       lowercase       : Optional[int]						=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowercase       : List[Any]						=image_std if image_std is not None else IMAGENET_STANDARD_STD






      def 					A__   (			self      :		Dict  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		Dict[str, int]  ,   UpperCAmelCase      :		PILImageResampling = PILImageResampling.BILINEAR  ,   UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None  ,   **UpperCAmelCase      :		List[str]  ,   )	->	np.ndarray:


       '''simple docstring'''

       lowercase       : str						=get_size_dict(UpperCAmelCase  ,   default_to_square=UpperCAmelCase		)
       if "shortest_edge" in size:
        lowercase       : int						=get_resize_output_image_size(UpperCAmelCase  ,   size['''shortest_edge''']  ,   default_to_square=UpperCAmelCase		)
       elif "height" in size and "width" in size:
        lowercase       : str						=(size['''height'''], size['''width'''])
       else:
        raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'		)
       return resize(UpperCAmelCase  ,   size=UpperCAmelCase  ,   resample=UpperCAmelCase  ,   data_format=UpperCAmelCase  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		Dict[str, int]  ,   UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None  ,   **UpperCAmelCase      :		Optional[Any]  ,   )	->	np.ndarray:


       '''simple docstring'''

       lowercase       : List[str]						=get_size_dict(UpperCAmelCase		)
       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(UpperCAmelCase  ,   size=(size['''height'''], size['''width'''])  ,   data_format=UpperCAmelCase  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		int  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		Union[int, float]  ,   UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None  ,   **UpperCAmelCase      :		Tuple  ,   )	->	List[Any]:


       '''simple docstring'''

       return rescale(UpperCAmelCase  ,   scale=UpperCAmelCase  ,   data_format=UpperCAmelCase  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		Union[float, List[float]]  ,   UpperCAmelCase      :		Union[float, List[float]]  ,   UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None  ,   **UpperCAmelCase      :		int  ,   )	->	np.ndarray:


       '''simple docstring'''

       return normalize(UpperCAmelCase  ,   mean=UpperCAmelCase  ,   std=UpperCAmelCase  ,   data_format=UpperCAmelCase  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		int  ,   UpperCAmelCase      :		ImageInput  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		PILImageResampling = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		float = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[ChannelDimension] = ChannelDimension.FIRST  ,   )	->	np.ndarray:


       '''simple docstring'''

       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.'''		)

       # All transformations expect numpy arrays.
       lowercase       : Dict						=to_numpy_array(UpperCAmelCase		)

       if do_resize:
        lowercase       : Union[str, Any]						=self.resize(image=UpperCAmelCase  ,   size=UpperCAmelCase  ,   resample=UpperCAmelCase		)

       if do_center_crop:
        lowercase       : Optional[Any]						=self.center_crop(UpperCAmelCase  ,   size=UpperCAmelCase		)

       if do_rescale:
        lowercase       : Dict						=self.rescale(image=UpperCAmelCase  ,   scale=UpperCAmelCase		)

       if do_normalize:
        lowercase       : int						=self.normalize(image=UpperCAmelCase  ,   mean=UpperCAmelCase  ,   std=UpperCAmelCase		)

       lowercase       : Optional[Any]						=to_channel_dimension_format(UpperCAmelCase  ,   UpperCAmelCase		)
       return image






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		ImageInput  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		PILImageResampling = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Dict[str, int] = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		float = None  ,   UpperCAmelCase      :		bool = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[Union[float, List[float]]] = None  ,   UpperCAmelCase      :		Optional[Union[str, TensorType]] = None  ,   UpperCAmelCase      :		ChannelDimension = ChannelDimension.FIRST  ,   **UpperCAmelCase      :		Optional[int]  ,   )	->	PIL.Image.Image:


       '''simple docstring'''

       lowercase       : Any						=do_resize if do_resize is not None else self.do_resize
       lowercase       : Union[str, Any]						=resample if resample is not None else self.resample
       lowercase       : Tuple						=do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase       : List[Any]						=do_rescale if do_rescale is not None else self.do_rescale
       lowercase       : str						=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       : List[str]						=image_mean if image_mean is not None else self.image_mean
       lowercase       : Optional[int]						=image_std if image_std is not None else self.image_std

       lowercase       : Optional[Any]						=size if size is not None else self.size
       lowercase       : Any						=get_size_dict(UpperCAmelCase  ,   default_to_square=UpperCAmelCase		)
       lowercase       : Union[str, Any]						=crop_size if crop_size is not None else self.crop_size
       lowercase       : Optional[int]						=get_size_dict(UpperCAmelCase  ,   param_name='''crop_size'''		)

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

       lowercase       : List[str]						=make_batched(UpperCAmelCase		)

       lowercase       : Union[str, Any]						=[
           [
               self._preprocess_image(
                   image=UpperCAmelCase  ,   do_resize=UpperCAmelCase  ,   size=UpperCAmelCase  ,   resample=UpperCAmelCase  ,   do_center_crop=UpperCAmelCase  ,   crop_size=UpperCAmelCase  ,   do_rescale=UpperCAmelCase  ,   rescale_factor=UpperCAmelCase  ,   do_normalize=UpperCAmelCase  ,   image_mean=UpperCAmelCase  ,   image_std=UpperCAmelCase  ,   data_format=UpperCAmelCase  ,   )
               for img in video
           ]
           for video in videos
       ]

       lowercase       : Dict						={'''pixel_values''': videos}
       return BatchFeature(data=UpperCAmelCase  ,   tensor_type=UpperCAmelCase		)



