

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
lowerCAmelCase__								=		'''src/transformers'''
lowerCAmelCase__								=		'''docs/source/en'''
lowerCAmelCase__								=		'''.'''






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     Optional[int]	)						->				Tuple:




	'''simple docstring'''




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

	_UpperCamelCase   :		Union[str, Any]			=						start_index
	while not lines[end_index].startswith(__UpperCamelCase	):
		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 |
lowerCAmelCase__								=		'''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
lowerCAmelCase__								=		re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
lowerCAmelCase__								=		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.
lowerCAmelCase__								=		re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int	)						->				str:




	'''simple docstring'''




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






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     int	)						->				Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Dict			=						2 if text == """✅""" or text == """❌""" else len(__UpperCamelCase	)
	_UpperCamelCase   :		Tuple			=						(width - text_length) // 2
	_UpperCamelCase   :		Any			=						width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent






def 					lowerCamelCase_     (							)						->				Optional[Any]:




	'''simple docstring'''




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

	# Let's lookup through all transformers object (once).
	for attr_name in dir(__UpperCamelCase	):
		_UpperCamelCase   :		Union[str, Any]			=						None
		if attr_name.endswith('Tokenizer'	):
			_UpperCamelCase   :		Dict			=						slow_tokenizers
			_UpperCamelCase   :		Any			=						attr_name[:-9]
		elif attr_name.endswith('TokenizerFast'	):
			_UpperCamelCase   :		Optional[Any]			=						fast_tokenizers
			_UpperCamelCase   :		str			=						attr_name[:-1_3]
		elif _re_tf_models.match(__UpperCamelCase	) is not None:
			_UpperCamelCase   :		List[Any]			=						tf_models
			_UpperCamelCase   :		Tuple			=						_re_tf_models.match(__UpperCamelCase	).groups()[0]
		elif _re_flax_models.match(__UpperCamelCase	) is not None:
			_UpperCamelCase   :		Dict			=						flax_models
			_UpperCamelCase   :		Tuple			=						_re_flax_models.match(__UpperCamelCase	).groups()[0]
		elif _re_pt_models.match(__UpperCamelCase	) is not None:
			_UpperCamelCase   :		List[Any]			=						pt_models
			_UpperCamelCase   :		Any			=						_re_pt_models.match(__UpperCamelCase	).groups()[0]

		if lookup_dict is not None:
			while len(__UpperCamelCase	) > 0:
				if attr_name in model_name_to_prefix.values():
					_UpperCamelCase   :		Any			=						True
					break
				# Try again after removing the last word in the name
				_UpperCamelCase   :		Tuple			=						"""""".join(camel_case_split(__UpperCamelCase	)[:-1]	)

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

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

	_UpperCamelCase   :		List[Any]			=						{True: """✅""", False: """❌"""}
	for name in model_names:
		_UpperCamelCase   :		Union[str, Any]			=						model_name_to_prefix[name]
		_UpperCamelCase   :		Optional[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(__UpperCamelCase							,       __UpperCamelCase	) for l, w in zip(__UpperCamelCase							,       __UpperCamelCase	)]	) + "|\n"
	return table






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any=False	)						->				Any:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[int]			=						_find_text_in_file(
	    filename=os.path.join(__UpperCamelCase							,       'index.md'	)							,       start_prompt='<!--This table is updated automatically from the auto modules'							,       end_prompt='<!-- End table-->'							,       )
	_UpperCamelCase   :		int			=						get_model_table_from_auto_modules()

	if current_table != new_table:
		if overwrite:
			with open(os.path.join(__UpperCamelCase							,       '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__":
							lowerCAmelCase__								=		argparse.ArgumentParser()
							parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
							lowerCAmelCase__								=		parser.parse_args()

							check_model_table(args.fix_and_overwrite)







from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels




