




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 		_A(       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			Optional[Any]				)    -> Optional[Any]:
							'''simple docstring'''


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

							__lowercase      =    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__				     = '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 		_A(       UpperCamelCase__							:			List[Any]				)    -> str:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			List[str]				)    -> Optional[int]:
							'''simple docstring'''


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







def 		_A(       )    -> Any:
							'''simple docstring'''


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

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

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

							# Build the table per se
							__lowercase      =    '''|''' + '''|'''.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      =    {True: '''✅''', False: '''❌'''}
							for name in model_names:
														__lowercase      =    model_name_to_prefix[name]
														__lowercase      =    [
														    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(       UpperCamelCase__							:			str=False				)    -> Optional[Any]:
							'''simple docstring'''


							__lowercase     ,     __lowercase     ,     __lowercase     ,     __lowercase      =    _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-->'''				,       )
							__lowercase      =    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__				     = 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)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase__				     = {
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__				     = [
			    "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "M2M100ForConditionalGeneration",
			    "M2M100Model",
			    "M2M100PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
			from .tokenization_mam_aaa import MaMaaaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mam_aaa import (
						    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MaMaaaForConditionalGeneration,
						    MaMaaaModel,
						    MaMaaaPreTrainedModel,
						)


else:
			import sys

			UpperCAmelCase__				     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






