
'''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)









# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


__UpperCamelCase					:       List[str]    					=      logging.get_logger(__name__)

__UpperCamelCase					:       Dict[Optional[str], Type[Formatter]]    					=      {}
__UpperCamelCase					:       Dict[Optional[str], str]    					=      {}
__UpperCamelCase					:       Dict[Optional[str], Exception]    					=      {}





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = None							,			)      ->       Optional[int]:
	a										=							aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    f'Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'    )
	a										=							formatter_cls
	for alias in set(aliases + [format_type]    ):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    f'Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'    )
		a										=							format_type





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = None    )      ->       List[str]:
	a										=							aliases if aliases is not None else []
	for alias in set(aliases + [format_type]    ):
		a										=							unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["python"])
_register_formatter(ArrowFormatter, "arrow", aliases=["pa", "pyarrow"])
_register_formatter(NumpyFormatter, "numpy", aliases=["np"])
_register_formatter(PandasFormatter, "pandas", aliases=["pd"])
_register_formatter(CustomFormatter, "custom")

if config.TORCH_AVAILABLE:
		from .torch_formatter import TorchFormatter

		_register_formatter(TorchFormatter, "torch", aliases=["pt", "pytorch"])
else:
		__UpperCamelCase					:       str    					=      ValueError("PyTorch needs to be installed to be able to return PyTorch tensors.")
		_register_unavailable_formatter(_torch_error, "torch", aliases=["pt", "pytorch"])

if config.TF_AVAILABLE:
		from .tf_formatter import TFFormatter

		_register_formatter(TFFormatter, "tensorflow", aliases=["tf"])
else:
		__UpperCamelCase					:       List[str]    					=      ValueError("Tensorflow needs to be installed to be able to return Tensorflow tensors.")
		_register_unavailable_formatter(_tf_error, "tensorflow", aliases=["tf"])

if config.JAX_AVAILABLE:
		from .jax_formatter import JaxFormatter

		_register_formatter(JaxFormatter, "jax", aliases=[])
else:
		__UpperCamelCase					:       List[str]    					=      ValueError("JAX needs to be installed to be able to return JAX arrays.")
		_register_unavailable_formatter(_jax_error, "jax", aliases=[])





def 		__A		(	__lowerCamelCase    )      ->       Optional[str]:
	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type





def 		__A		(	__lowerCamelCase							,			**__lowerCamelCase    )      ->       Formatter:
	a										=							get_format_type_from_alias(__lowerCamelCase    )
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**__lowerCamelCase    )
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    f'Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None    )}, but got \'{format_type}\''    )

