
'''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 :  Dict    							=       "src/transformers"
_lowercase :  List[Any]    							=       "docs/source/en"
_lowercase :  Optional[Any]    							=       "."
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[int]      ,      __SCREAMING_SNAKE_CASE					: Tuple      ,      __SCREAMING_SNAKE_CASE					: Optional[Any]							):



						"""simple docstring"""





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

						lowercase_	:     List[str]													=       start_index
						while not lines[end_index].startswith(__SCREAMING_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 :  List[Any]    							=       "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_lowercase :  Dict    							=       re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_lowercase :  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.
_lowercase :  str    							=       re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


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



						"""simple docstring"""





						lowercase_	:     Optional[Any]													=       re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''      ,      __SCREAMING_SNAKE_CASE							)
						return [m.group(0							) for m in matches]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: List[Any]							):



						"""simple docstring"""





						lowercase_	:     Union[str, Any]													=       2 if text == '''✅''' or text == '''❌''' else len(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     str													=       (width - text_length) // 2
						lowercase_	:     str													=       width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent
def 					snake_case_		(				):



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						lowercase_	:     Dict													=       {
						    name: config_maping_names[code]
						    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
						    if code in config_maping_names
						}
						lowercase_	:     Dict													=       {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.
						lowercase_	:     Union[str, Any]													=       collections.defaultdict(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     Dict													=       collections.defaultdict(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     List[Any]													=       collections.defaultdict(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     str													=       collections.defaultdict(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     Optional[int]													=       collections.defaultdict(__SCREAMING_SNAKE_CASE							)

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

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

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

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

						lowercase_	:     int													=       {True: '''✅''', False: '''❌'''}
						for name in model_names:
												lowercase_	:     Dict													=       model_name_to_prefix[name]
												lowercase_	:     Tuple													=       [
												    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(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							) for l, w in zip(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)]							) + "|\n"
						return table
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Dict=False							):



						"""simple docstring"""





						lowercase_						, lowercase_						, lowercase_						, lowercase_	:     Any													=       _find_text_in_file(
						    filename=os.path.join(__SCREAMING_SNAKE_CASE      ,      '''index.md'''							)      ,      start_prompt='''<!--This table is updated automatically from the auto modules'''      ,      end_prompt='''<!-- End table-->'''      ,      )
						lowercase_	:     str													=       get_model_table_from_auto_modules()

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

							check_model_table(args.fix_and_overwrite)





'''simple docstring'''







from __future__ import annotations

from collections import Counter
from random import random






class  lowerCAmelCase__   :






					def __init__(   self		):


											"""simple docstring"""





											lowercase_	:     int													=       {}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       {}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if nodea not in self.connections:
																	self.add_node(__SCREAMING_SNAKE_CASE		)
											if nodea not in self.connections:
																	self.add_node(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       probability






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return list(self.connections		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       0
											lowercase_	:     Tuple													=       random()

											for dest in self.connections[node]:
																	current_probability += self.connections[node][dest]
																	if current_probability > random_value:
																							return dest
											return ""







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: list[tuple[str, str, float]]      ,      __SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       MarkovChainGraphUndirectedUnweighted()

						for nodea, nodea, probability in transitions:
												graph.add_transition_probability(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)

						lowercase_	:     str													=       Counter(graph.get_nodes()							)
						lowercase_	:     Any													=       start

						for _ in range(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     int													=       graph.transition(__SCREAMING_SNAKE_CASE							)
												visited[node] += 1

						return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()



