





'''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
lowercase__    :      List[Any]						=    '''src/transformers'''
lowercase__    :      List[Any]						=    '''docs/source/en'''
lowercase__    :      Tuple						=    '''.'''






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

     __A						: Tuple      =       start_index
     while not lines[end_index].startswith(__snake_case			):
          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 |
lowercase__    :      str						=    '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
lowercase__    :      List[str]						=    re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowercase__    :      List[str]						=    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.
lowercase__    :      List[Any]						=    re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the transformers module imported is the one in the repo.
lowercase__    :      Optional[int]						=    direct_transformers_import(TRANSFORMERS_PATH)






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






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






def        _lowerCAmelCase			(  )     ->     int:
     __A						: Any      =       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     __A						: Dict      =       {
         name: config_maping_names[code]
         for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
         if code in config_maping_names
     }
     __A						: Tuple      =       {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.
     __A						: Optional[int]      =       collections.defaultdict(__snake_case			)
     __A						: Dict      =       collections.defaultdict(__snake_case			)
     __A						: Optional[Any]      =       collections.defaultdict(__snake_case			)
     __A						: Any      =       collections.defaultdict(__snake_case			)
     __A						: Tuple      =       collections.defaultdict(__snake_case			)

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

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

    # Let's build that table!
     __A						: List[str]      =       list(model_name_to_config.keys()			)
     model_names.sort(key=str.lower			)
     __A						: List[str]      =       ['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).
     __A						: str      =       [len(__snake_case			) + 2 for c in columns]
     __A						: Tuple      =       max([len(__snake_case			) for name in model_names]			) + 2

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

     __A						: Union[str, Any]      =       {True: '✅', False: '❌'}
     for name in model_names:
          __A						: List[str]      =       model_name_to_prefix[name]
          __A						: Optional[int]      =       [
              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(__snake_case ,							__snake_case			) for l, w in zip(__snake_case ,							__snake_case			)]			) + "|\n"
     return table






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

     if current_table != new_table:
          if overwrite:
               with open(os.path.join(__snake_case ,							'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__":
  lowercase__    :      str						=    argparse.ArgumentParser()
  parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
  lowercase__    :      Union[str, Any]						=    parser.parse_args()

  check_model_table(args.fix_and_overwrite)






'''simple docstring'''




from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       ['''image_processor''', '''tokenizer''']
     lowerCAmelCase					=       '''BlipImageProcessor'''
     lowerCAmelCase					=       ('''BertTokenizer''', '''BertTokenizerFast''')


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: int      =       False
          super().__init__(_UpperCAmelCase						,	_UpperCAmelCase)
          __A						: Optional[int]      =       self.image_processor


     def __call__(						self						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = True						,	_UpperCAmelCase = False						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = 0						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = True						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          if images is None and text is None:
               raise ValueError('You have to specify either images or text.')

          # Get only text
          if images is None:
               __A						: int      =       self.tokenizer
               __A						: Optional[Any]      =       self.tokenizer(
                   text=_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	stride=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	return_special_tokens_mask=_UpperCAmelCase						,	return_offsets_mapping=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_length=_UpperCAmelCase						,	verbose=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
               return text_encoding

          # add pixel_values
          __A						: List[Any]      =       self.image_processor(_UpperCAmelCase						,	return_tensors=_UpperCAmelCase)

          if text is not None:
               __A						: Optional[Any]      =       self.tokenizer(
                   text=_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	stride=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	return_special_tokens_mask=_UpperCAmelCase						,	return_offsets_mapping=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_length=_UpperCAmelCase						,	verbose=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
          else:
               __A						: int      =       None

          if text_encoding is not None:
               encoding_image_processor.update(_UpperCAmelCase)

          return encoding_image_processor


     def 							SCREAMING_SNAKE_CASE  (						self						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.batch_decode(*_UpperCAmelCase						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.decode(*_UpperCAmelCase						,	**_UpperCAmelCase)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       self.tokenizer.model_input_names
          __A						: Tuple      =       self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))