
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					: Optional[int]      =       """src/transformers"""
_UpperCAmelCase					: Tuple      =       """docs/source/en"""
_UpperCAmelCase					: Optional[Any]      =       """."""


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  Dict:
		'''simple docstring'''

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

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


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


def       A       (					lowercase )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'    ,       __a )
		return [m.group(0 ) for m in matches]


def       A       (					lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     2 if text == '''✅''' or text == '''❌''' else len(__a )
		UpperCamelCase		=     (width - text_length) // 2
		UpperCamelCase		=     width - text_length - left_indent
		return " " * left_indent + text + " " * right_indent


def       A       (					)   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		UpperCamelCase		=     {
		    name: config_maping_names[code]
		    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
		    if code in config_maping_names
		}
		UpperCamelCase		=     {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.
		UpperCamelCase		=     collections.defaultdict(__a )
		UpperCamelCase		=     collections.defaultdict(__a )
		UpperCamelCase		=     collections.defaultdict(__a )
		UpperCamelCase		=     collections.defaultdict(__a )
		UpperCamelCase		=     collections.defaultdict(__a )

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

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

		# Build the table per se
		UpperCamelCase		=     '''|''' + '''|'''.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"

		UpperCamelCase		=     {True: '''✅''', False: '''❌'''}
		for name in model_names:
				UpperCamelCase		=     model_name_to_prefix[name]
				UpperCamelCase		=     [
				    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       (					lowercase=False )   ->  List[str]:
		'''simple docstring'''

		UpperCamelCase		=     _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-->'    ,       )
		UpperCamelCase		=     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__":
					_UpperCAmelCase					: Union[str, Any]      =       argparse.ArgumentParser()
					parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
					_UpperCAmelCase					: Any      =       parser.parse_args()

					check_model_table(args.fix_and_overwrite)








"""simple docstring"""






from math import factorial



def 					_A			(__a = 20							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Dict    						=		2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
    # 2, 3,...
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		n // 2

    return int(factorial(__a							) / (factorial(__a							) * factorial(n - k							))							)


if __name__ == "__main__":
  import sys

  if len(sys.argv) == 1:
    print(solution(20))
  else:
    try:
      UpperCAmelCase_   : List[str]   			= int(sys.argv[1])
      print(solution(n))
    except ValueError:
      print("""Invalid entry - please enter a number.""")


