

class 					lowercase							:




				def __init__(   self ,  snake_case			):
											snake_case_     							=       n
											snake_case_     							=       [None] * self.n
											snake_case_     							=       0  # index of the first element
											snake_case_     							=       0
											snake_case_     							=       0




				def __len__(   self			):
											return self.size




				def        a				(   self			):
											return self.size == 0




				def        a				(   self			):
											return False if self.is_empty() else self.array[self.front]




				def        a				(   self ,  snake_case			):
											if self.size >= self.n:
																		raise Exception('QUEUE IS FULL'			)

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




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

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCAmelCase						:  Tuple									=			{
    """configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
    """tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  Tuple									=			[
					    """M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """M2M100ForConditionalGeneration""",
					    """M2M100Model""",
					    """M2M100PreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
					from .tokenization_mam_aaa import MaMaaaTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mam_aaa import (
										    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaMaaaForConditionalGeneration,
										    MaMaaaModel,
										    MaMaaaPreTrainedModel,
										)


else:
					import sys

					_UpperCAmelCase						:  Any									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




