


'''simple docstring'''
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,
)


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

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

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     [
		    '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:
		__snake_case							      =     [
		    '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:
		__snake_case							      =     [
		    '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

		__snake_case							      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

def     __lowerCAmelCase			(			a__  )       ->  str:
				__a										=    []
				__a										=    set({'''(''', '''[''', '''{'''}  )
				__a										=    set({''')''', ''']''', '''}'''}  )
				__a										=    {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

				for i in range(len(a__  )  ):
								if s[i] in open_brackets:
												stack.append(s[i]  )

								elif s[i] in closed_brackets and (
								    len(a__  ) == 0 or (len(a__  ) > 0 and open_to_closed[stack.pop()] != s[i])
								):
												return False

				return len(a__  ) == 0


def     __lowerCAmelCase			(			)       ->  Dict:
				__a										=    input('''Enter sequence of brackets: '''  )
				if is_balanced(a__  ):
								print(a__						,    '''is balanced'''  )
				else:
								print(a__						,    '''is not balanced'''  )


if __name__ == "__main__":
					main()