
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a											=							{
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						a											=							['XLMRobertaTokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						a											=							['XLMRobertaTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						a											=							[
						    'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'XLMRobertaForCausalLM',
						    'XLMRobertaForMaskedLM',
						    'XLMRobertaForMultipleChoice',
						    'XLMRobertaForQuestionAnswering',
						    'XLMRobertaForSequenceClassification',
						    'XLMRobertaForTokenClassification',
						    'XLMRobertaModel',
						    'XLMRobertaPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						a											=							[
						    'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFXLMRobertaForCausalLM',
						    'TFXLMRobertaForMaskedLM',
						    'TFXLMRobertaForMultipleChoice',
						    'TFXLMRobertaForQuestionAnswering',
						    'TFXLMRobertaForSequenceClassification',
						    'TFXLMRobertaForTokenClassification',
						    'TFXLMRobertaModel',
						    'TFXLMRobertaPreTrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						a											=							[
						    'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'FlaxXLMRobertaForMaskedLM',
						    'FlaxXLMRobertaForCausalLM',
						    'FlaxXLMRobertaForMultipleChoice',
						    'FlaxXLMRobertaForQuestionAnswering',
						    'FlaxXLMRobertaForSequenceClassification',
						    'FlaxXLMRobertaForTokenClassification',
						    'FlaxXLMRobertaModel',
						    'FlaxXLMRobertaPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_xlm_roberta import (
						    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    XLMRobertaConfig,
						    XLMRobertaOnnxConfig,
						)

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlm_roberta import XLMRobertaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_roberta import (
												    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMRobertaForCausalLM,
												    XLMRobertaForMaskedLM,
												    XLMRobertaForMultipleChoice,
												    XLMRobertaForQuestionAnswering,
												    XLMRobertaForSequenceClassification,
												    XLMRobertaForTokenClassification,
												    XLMRobertaModel,
												    XLMRobertaPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_xlm_roberta import (
												    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFXLMRobertaForCausalLM,
												    TFXLMRobertaForMaskedLM,
												    TFXLMRobertaForMultipleChoice,
												    TFXLMRobertaForQuestionAnswering,
												    TFXLMRobertaForSequenceClassification,
												    TFXLMRobertaForTokenClassification,
												    TFXLMRobertaModel,
												    TFXLMRobertaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_xlm_roberta import (
												    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaxXLMRobertaForCausalLM,
												    FlaxXLMRobertaForMaskedLM,
												    FlaxXLMRobertaForMultipleChoice,
												    FlaxXLMRobertaForQuestionAnswering,
												    FlaxXLMRobertaForSequenceClassification,
												    FlaxXLMRobertaForTokenClassification,
												    FlaxXLMRobertaModel,
												    FlaxXLMRobertaPreTrainedModel,
												)

else:
						import sys

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




"""simple docstring"""






import argparse
from collections import defaultdict


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )		->  Optional[int]:
			__lowerCAmelCase :    str				=			F"""{file}_{class_name}_{test_name}"""
			done_test[_id] += 1

			with open(__snake_case   ,"r"      ) as f:
						__lowerCAmelCase :    List[str]				=			f.readlines()

			__lowerCAmelCase :    Tuple				=			F"""class {class_name}("""
			__lowerCAmelCase :    Tuple				=			F"""{4 * ' '}def {test_name}("""
			__lowerCAmelCase :    List[str]				=			F"""{8 * ' '}{correct_line.split()[0]}"""
			__lowerCAmelCase :    List[Any]				=			F"""{16 * ' '}{correct_line.split()[0]}"""
			__lowerCAmelCase :    Any				=			False
			__lowerCAmelCase :    List[Any]				=			False
			__lowerCAmelCase :    Optional[Any]				=			False
			__lowerCAmelCase :    str				=			False
			__lowerCAmelCase :    Optional[int]				=			0
			__lowerCAmelCase :    Dict				=			0

			__lowerCAmelCase :    int				=			[]
			for line in lines:
						if line.startswith(__snake_case      ):
									__lowerCAmelCase :    Any				=			True
						elif in_class and line.startswith(__snake_case      ):
									__lowerCAmelCase :    str				=			True
						elif in_class and in_func and (line.startswith(__snake_case      ) or line.startswith(__snake_case      )):
									__lowerCAmelCase :    Optional[Any]				=			len(line.split(correct_line.split()[0]      )[0]      )
									count += 1

									if count == done_test[_id]:
												__lowerCAmelCase :    Optional[int]				=			True

						if in_class and in_func and in_line:
									if ")" not in line:
												continue
									else:
												__lowerCAmelCase :    Dict				=			True

						if in_class and in_func and in_line and insert_line:
									new_lines.append(F"""{spaces * ' '}{correct_line}"""      )
									__lowerCAmelCase :    Union[str, Any]				=			False
						else:
									new_lines.append(__snake_case      )

			with open(__snake_case   ,"w"      ) as f:
						for line in new_lines:
									f.write(__snake_case      )


def      _lowercase							(    __snake_case   ,__snake_case=None      )		->  int:
			if fail is not None:
						with open(__snake_case   ,"r"      ) as f:
									__lowerCAmelCase :    Union[str, Any]				=			{l.strip() for l in f.readlines()}
			else:
						__lowerCAmelCase :    Optional[Any]				=			None

			with open(__snake_case   ,"r"      ) as f:
						__lowerCAmelCase :    Union[str, Any]				=			f.readlines()

			__lowerCAmelCase :    Optional[Any]				=			defaultdict(__snake_case      )
			for line in correct_lines:
						__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    List[str]				=			line.split(";"      )
						if test_failures is None or "::".join([file, class_name, test_name]      ) in test_failures:
									overwrite_file(__snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )


if __name__ == "__main__":
	__snake_case				:  Union[str, Any] =       argparse.ArgumentParser()
	parser.add_argument('--correct_filename', help='filename of tests with expected result')
	parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
	__snake_case				:  int =       parser.parse_args()

	main(args.correct_filename, args.fail_filename)