






'''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
A:      Union[str, Any]  				=				"src/transformers"
A:      Any  				=				"docs/source/en"
A:      Tuple  				=				"."






def 	_UpperCAmelCase    (  a		:	List[Any]						,			a		:	Dict						,			a		:	List[str]							)			->			List[str]:





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

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


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






def 	_UpperCAmelCase    (  a		:	Optional[Any]							)			->			Any:





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






def 	_UpperCAmelCase    (  a		:	int						,			a		:	Tuple							)			->			Tuple:





						"""simple docstring"""
						lowercase_      :   Any        =			2 if text == '✅' or text == '❌' else len(a							)
						lowercase_      :   int        =			(width - text_length) // 2
						lowercase_      :   Any        =			width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent






def 	_UpperCAmelCase    (  )			->			Union[str, Any]:





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

						# Let's lookup through all transformers object (once).
						for attr_name in dir(a							):
												lowercase_      :   Dict        =			None
												if attr_name.endswith('Tokenizer'							):
																		lowercase_      :   List[str]        =			slow_tokenizers
																		lowercase_      :   Dict        =			attr_name[:-9]
												elif attr_name.endswith('TokenizerFast'							):
																		lowercase_      :   Any        =			fast_tokenizers
																		lowercase_      :   Optional[Any]        =			attr_name[:-1_3]
												elif _re_tf_models.match(a							) is not None:
																		lowercase_      :   Optional[Any]        =			tf_models
																		lowercase_      :   Union[str, Any]        =			_re_tf_models.match(a							).groups()[0]
												elif _re_flax_models.match(a							) is not None:
																		lowercase_      :   Optional[Any]        =			flax_models
																		lowercase_      :   Union[str, Any]        =			_re_flax_models.match(a							).groups()[0]
												elif _re_pt_models.match(a							) is not None:
																		lowercase_      :   Optional[int]        =			pt_models
																		lowercase_      :   Union[str, Any]        =			_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_      :   Union[str, Any]        =			True
																														break
																								# Try again after removing the last word in the name
																								lowercase_      :   Optional[int]        =			''.join(camel_case_split(a							)[:-1]							)

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

						# Build the table per se
						lowercase_      :   Optional[Any]        =			'|' + '|'.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_      :   Any        =			{True: '✅', False: '❌'}
						for name in model_names:
												lowercase_      :   List[str]        =			model_name_to_prefix[name]
												lowercase_      :   int        =			[
												    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 	_UpperCAmelCase    (  a		:	Optional[int]=False							)			->			Tuple:





						"""simple docstring"""
						lowercase_  ,      lowercase_  ,      lowercase_  ,      lowercase_      :   str        =			_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_      :   Union[str, Any]        =			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__":
						A:      List[Any]  				=				argparse.ArgumentParser()
						parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
						A:      str  				=				parser.parse_args()

						check_model_table(args.fix_and_overwrite)








'''simple docstring'''

from typing import TYPE_CHECKING

from ..utils import _LazyModule


A:      int  				=				{
    "config": [
        "EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
        "OnnxConfig",
        "OnnxConfigWithPast",
        "OnnxSeq2SeqConfigWithPast",
        "PatchingSpec",
    ],
    "convert": ["export", "validate_model_outputs"],
    "features": ["FeaturesManager"],
    "utils": ["ParameterFormat", "compute_serialized_parameters_size"],
}


if TYPE_CHECKING:
						from .config import (
						    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
						    OnnxConfig,
						    OnnxConfigWithPast,
						    OnnxSeqaSeqConfigWithPast,
						    PatchingSpec,
						)
						from .convert import export, validate_model_outputs
						from .features import FeaturesManager
						from .utils import ParameterFormat, compute_serialized_parameters_size

else:
						import sys

						A:      Dict  				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
