





"""simple docstring"""
import argparse
import collections
import importlib.util
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_table.py
lowerCAmelCase:	List[str]							      ="""src/diffusers"""
lowerCAmelCase:	Dict							      ="""docs/source/en"""
lowerCAmelCase:	Any							      ="""."""




def 			__snake_case						(      __A ,__A ,__A    )		->				Tuple:

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

   lowercase    :			Optional[Any]  =       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 |
lowerCAmelCase:	Optional[int]							      ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
lowerCAmelCase:	str							      =re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
lowerCAmelCase:	Optional[Any]							      =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.
lowerCAmelCase:	List[Any]							      =re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the diffusers module imported is the one in the repo.
lowerCAmelCase:	Any							      =importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(TRANSFORMERS_PATH, "__init__.py"),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
lowerCAmelCase:	str							      =spec.loader.load_module()




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




def 			__snake_case						(      __A ,__A    )		->				Optional[int]:
   lowercase    :			Dict  =       2 if text == "✅" or text == "❌" else len(__A    )
   lowercase    :			int  =       (width - text_length) // 2
   lowercase    :			int  =       width - text_length - left_indent
   return " " * left_indent + text + " " * right_indent




def 			__snake_case						(      )		->				Any:
   lowercase    :			Any  =       diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   lowercase    :			List[Any]  =       {
       name: config_mapping_names[code]
       for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
       if code in config_mapping_names
   }
   lowercase    :			Tuple  =       {name: config.replace("""ConfigMixin""" ,""""""    ) 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.
   lowercase    :			int  =       collections.defaultdict(__A    )
   lowercase    :			Optional[int]  =       collections.defaultdict(__A    )
   lowercase    :			Tuple  =       collections.defaultdict(__A    )
   lowercase    :			Dict  =       collections.defaultdict(__A    )
   lowercase    :			Optional[int]  =       collections.defaultdict(__A    )

   # Let's lookup through all diffusers object (once).
   for attr_name in dir(__A    ):
      lowercase    :			List[str]  =       None
      if attr_name.endswith("""Tokenizer"""    ):
         lowercase    :			int  =       slow_tokenizers
         lowercase    :			List[str]  =       attr_name[:-9]
      elif attr_name.endswith("""TokenizerFast"""    ):
         lowercase    :			Optional[int]  =       fast_tokenizers
         lowercase    :			Optional[int]  =       attr_name[:-13]
      elif _re_tf_models.match(__A    ) is not None:
         lowercase    :			Tuple  =       tf_models
         lowercase    :			Optional[int]  =       _re_tf_models.match(__A    ).groups()[0]
      elif _re_flax_models.match(__A    ) is not None:
         lowercase    :			str  =       flax_models
         lowercase    :			str  =       _re_flax_models.match(__A    ).groups()[0]
      elif _re_pt_models.match(__A    ) is not None:
         lowercase    :			Optional[Any]  =       pt_models
         lowercase    :			int  =       _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():
               lowercase    :			Optional[int]  =       True
               break
            # Try again after removing the last word in the name
            lowercase    :			List[Any]  =       "".join(camel_case_split(__A    )[:-1]    )

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

   # Build the table per se
   lowercase    :			str  =       "|" + "|".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"

   lowercase    :			Union[str, Any]  =       {True: "✅", False: "❌"}
   for name in model_names:
      lowercase    :			Optional[int]  =       model_name_to_prefix[name]
      lowercase    :			List[Any]  =       [
          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 			__snake_case						(      __A=False    )		->				int:
   lowercase    :			int  =       _find_text_in_file(
       filename=os.path.join(__A ,"""index.mdx"""    ) ,start_prompt="""<!--This table is updated automatically from the auto modules""" ,end_prompt="""<!-- End table-->""" ,)
   lowercase    :			Union[str, Any]  =       get_model_table_from_auto_modules()

   if current_table != new_table:
      if overwrite:
         with open(os.path.join(__A ,"""index.mdx"""    ) ,"""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.mdx` has not been updated. Run `make fix-copies` to fix this."""    )


if __name__ == "__main__":
   lowerCAmelCase:	Optional[Any]							      =argparse.ArgumentParser()
   parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
   lowerCAmelCase:	List[str]							      =parser.parse_args()

   check_model_table(args.fix_and_overwrite)









"""simple docstring"""
from collections import defaultdict




def 			__snake_case						(      __A    )		->				dict:
   lowercase    :			defaultdict[str, int]  =       defaultdict(__A    )
   # Creating a dictionary containing count of each word
   for word in sentence.split():
      occurrence[word] += 1
   return occurrence


if __name__ == "__main__":
   for word, count in word_occurrence("INPUT STRING").items():
      print(F'{word}: {count}')


