




class  __magic_name__						:
				'''simple docstring'''







				def __init__(							self:   Union[str, Any]		,					_lowerCamelCase:   int		):
											SCREAMING_SNAKE_CASE_   	=    n
											SCREAMING_SNAKE_CASE_   	=    [None] * self.n
											SCREAMING_SNAKE_CASE_   	=    0  # index of the first element
											SCREAMING_SNAKE_CASE_   	=    0
											SCREAMING_SNAKE_CASE_   	=    0


				def __len__(							self:   List[Any]		):
											return self.size


				def     _A							(							self:   Optional[Any]		):
											return self.size == 0


				def     _A							(							self:   List[str]		):
											return False if self.is_empty() else self.array[self.front]


				def     _A							(							self:   List[str]		,					_lowerCamelCase:   List[str]		):
											if self.size >= self.n:
																		raise Exception('''QUEUE IS FULL'''		)

											SCREAMING_SNAKE_CASE_   	=    data
											SCREAMING_SNAKE_CASE_   	=    (self.rear + 1) % self.n
											self.size += 1
											return self





				def     _A							(							self:   str		):
											if self.size == 0:
																		raise Exception('''UNDERFLOW'''		)

											SCREAMING_SNAKE_CASE_   	=    self.array[self.front]
											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    (self.front + 1) % self.n
											self.size -= 1
											return temp








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE 						={
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE 						=[
			    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """XLMRobertaXLForCausalLM""",
			    """XLMRobertaXLForMaskedLM""",
			    """XLMRobertaXLForMultipleChoice""",
			    """XLMRobertaXLForQuestionAnswering""",
			    """XLMRobertaXLForSequenceClassification""",
			    """XLMRobertaXLForTokenClassification""",
			    """XLMRobertaXLModel""",
			    """XLMRobertaXLPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta_xl import (
			    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaXLConfig,
			    XLMRobertaXLOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaXLForCausalLM,
						    XLMRobertaXLForMaskedLM,
						    XLMRobertaXLForMultipleChoice,
						    XLMRobertaXLForQuestionAnswering,
						    XLMRobertaXLForSequenceClassification,
						    XLMRobertaXLForTokenClassification,
						    XLMRobertaXLModel,
						    XLMRobertaXLPreTrainedModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE 						=_LazyModule(__name__, globals()["""__file__"""], _import_structure)


