

"""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
_UpperCamelCase		     =    """src/transformers"""
_UpperCamelCase		     =    """docs/source/en"""
_UpperCamelCase		     =    """."""



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

 lowerCAmelCase__   :				Union[str, Any] 			=				start_index
 while not lines[end_index].startswith(lowercase__   ):
  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 |
_UpperCamelCase		     =    """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_UpperCamelCase		     =    re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCamelCase		     =    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.
_UpperCamelCase		     =    re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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



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



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Optional[Any]:
 lowerCAmelCase__   :				int 			=				2 if text == "✅" or text == "❌" else len(lowercase__   )
 lowerCAmelCase__   :				Tuple 			=				(width - text_length) // 2
 lowerCAmelCase__   :				Any 			=				width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent



def       SCREAMING_SNAKE_CASE							( )			->						int:
 lowerCAmelCase__   :				Union[str, Any] 			=				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 lowerCAmelCase__   :				Optional[int] 			=				{
     name: config_maping_names[code]
     for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
     if code in config_maping_names
 }
 lowerCAmelCase__   :				List[Any] 			=				{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__   :				Union[str, Any] 			=				collections.defaultdict(lowercase__   )
 lowerCAmelCase__   :				Optional[Any] 			=				collections.defaultdict(lowercase__   )
 lowerCAmelCase__   :				Tuple 			=				collections.defaultdict(lowercase__   )
 lowerCAmelCase__   :				Any 			=				collections.defaultdict(lowercase__   )
 lowerCAmelCase__   :				str 			=				collections.defaultdict(lowercase__   )

 # Let's lookup through all transformers object (once).
 for attr_name in dir(lowercase__   ):
  lowerCAmelCase__   :				str 			=				None
  if attr_name.endswith("Tokenizer"   ):
   lowerCAmelCase__   :				List[str] 			=				slow_tokenizers
   lowerCAmelCase__   :				Tuple 			=				attr_name[:-9]
  elif attr_name.endswith("TokenizerFast"   ):
   lowerCAmelCase__   :				Optional[Any] 			=				fast_tokenizers
   lowerCAmelCase__   :				List[str] 			=				attr_name[:-1_3]
  elif _re_tf_models.match(lowercase__   ) is not None:
   lowerCAmelCase__   :				int 			=				tf_models
   lowerCAmelCase__   :				Dict 			=				_re_tf_models.match(lowercase__   ).groups()[0]
  elif _re_flax_models.match(lowercase__   ) is not None:
   lowerCAmelCase__   :				int 			=				flax_models
   lowerCAmelCase__   :				Optional[int] 			=				_re_flax_models.match(lowercase__   ).groups()[0]
  elif _re_pt_models.match(lowercase__   ) is not None:
   lowerCAmelCase__   :				List[str] 			=				pt_models
   lowerCAmelCase__   :				str 			=				_re_pt_models.match(lowercase__   ).groups()[0]

  if lookup_dict is not None:
   while len(lowercase__   ) > 0:
    if attr_name in model_name_to_prefix.values():
     lowerCAmelCase__   :				Dict 			=				True
     break
    # Try again after removing the last word in the name
    lowerCAmelCase__   :				List[Any] 			=				"".join(camel_case_split(lowercase__   )[:-1]   )

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

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

 lowerCAmelCase__   :				Optional[int] 			=				{True: "✅", False: "❌"}
 for name in model_names:
  lowerCAmelCase__   :				Optional[int] 			=				model_name_to_prefix[name]
  lowerCAmelCase__   :				str 			=				[
      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(lowercase__					,					lowercase__   ) for l, w in zip(lowercase__					,					lowercase__   )]   ) + "|\n"
 return table



def       SCREAMING_SNAKE_CASE							( lowercase__=False   )			->						Optional[int]:
 lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				Any 			=				_find_text_in_file(
     filename=os.path.join(lowercase__					,					"index.md"   )					,					start_prompt="<!--This table is updated automatically from the auto modules"					,					end_prompt="<!-- End table-->"					,					)
 lowerCAmelCase__   :				List[str] 			=				get_model_table_from_auto_modules()

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

    check_model_table(args.fix_and_overwrite)









"""simple docstring"""

def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						list:
 lowerCAmelCase__   :				List[str] 			=				len(lowercase__   )
 lowerCAmelCase__   :				Dict 			=				[]
 for i in range(len(lowercase__   ) - pat_len + 1   ):
  lowerCAmelCase__   :				Union[str, Any] 			=				True
  for j in range(lowercase__   ):
   if s[i + j] != pattern[j]:
    lowerCAmelCase__   :				int 			=				False
    break
  if match_found:
   position.append(lowercase__   )
 return position


if __name__ == "__main__":
    assert naive_pattern_search("""ABCDEFG""", """DE""") == [3]
    print(naive_pattern_search("""ABAAABCDBBABCDDEBCABC""", """ABC"""))






