


"""simple docstring"""



from __future__ import annotations

from typing import Any
class 					__magic_name__		:






       def __init__(   self  :    Any  ,     snake_case__  :    int = 6				):

              '''simple docstring'''






              lowercase		:Node | None       							=				None
              lowercase		:Node | None       							=				None
              self.create_linked_list(snake_case__				)






       def  __snake_case						(   self  :    Any  ,     snake_case__  :    int				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				Node()
              lowercase		:List[str]       							=				current_node
              lowercase		:Any       							=				current_node
              lowercase		:Dict       							=				current_node
              for _ in range(1  ,     snake_case__				):
                     lowercase		:Tuple       							=				Node()
                     lowercase		:Dict       							=				current_node
                     lowercase		:List[Any]       							=				previous_node
                     lowercase		:List[Any]       							=				current_node
              lowercase		:Tuple       							=				self.front
              lowercase		:Any       							=				previous_node






       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              return (
                  self.front == self.rear
                  and self.front is not None
                  and self.front.data is None
              )






       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              self.check_can_perform_operation()
              return self.front.data if self.front else None






       def  __snake_case						(   self  :    int  ,     snake_case__  :    Any				):

              '''simple docstring'''






              if self.rear is None:
                     return

              self.check_is_full()
              if not self.is_empty():
                     lowercase		:Optional[Any]       							=				self.rear.next
              if self.rear:
                     lowercase		:List[str]       							=				data






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              self.check_can_perform_operation()
              if self.rear is None or self.front is None:
                     return None
              if self.front == self.rear:
                     lowercase		:Union[str, Any]       							=				self.front.data
                     lowercase		:List[Any]       							=				None
                     return data

              lowercase		:Union[str, Any]       							=				self.front
              lowercase		:Any       							=				old_front.next
              lowercase		:List[str]       							=				old_front.data
              lowercase		:str       							=				None
              return data






       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              if self.is_empty():
                     raise Exception('''Empty Queue'''				)







       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              if self.rear and self.rear.next == self.front:
                     raise Exception('''Full Queue'''				)







class 					__magic_name__		:






       def __init__(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Any | None       							=				None
              lowercase		:Node | None       							=				None
              lowercase		:Node | None       							=				None


if __name__ == "__main__":
 import doctest

 doctest.testmod()





"""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,
)


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

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

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

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

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

