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,
)


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

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

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Any       			=		[
				    '''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:
				_lowercase:	int       			=		[
				    '''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:
				_lowercase:	int       			=		[
				    '''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

				_lowercase:	Any       			=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







from math import ceil, sqrt






def _lowerCamelCase  (		snake_case = 1_000_000  ):
							_lowerCAmelCase							=							0

							for outer_width in range(3     , (limit // 4) + 2  ):
														if outer_width**2 > limit:
																					_lowerCAmelCase							=							max(ceil(sqrt(outer_width**2 - limit  )  )     , 1  )
														else:
																					_lowerCAmelCase							=							1
														if (outer_width - hole_width_lower_bound) % 2:
																					hole_width_lower_bound += 1

														answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

							return answer


if __name__ == "__main__":
				print(f"""{solution() = }""")






