"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a   :	Union[str, Any] 					=     {
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	List[Any] 					=     ['RoFormerTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	Optional[Any] 					=     [
         'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'RoFormerForCausalLM',
         'RoFormerForMaskedLM',
         'RoFormerForMultipleChoice',
         'RoFormerForQuestionAnswering',
         'RoFormerForSequenceClassification',
         'RoFormerForTokenClassification',
         'RoFormerLayer',
         'RoFormerModel',
         'RoFormerPreTrainedModel',
         'load_tf_weights_in_roformer',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	List[Any] 					=     [
         'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFRoFormerForCausalLM',
         'TFRoFormerForMaskedLM',
         'TFRoFormerForMultipleChoice',
         'TFRoFormerForQuestionAnswering',
         'TFRoFormerForSequenceClassification',
         'TFRoFormerForTokenClassification',
         'TFRoFormerLayer',
         'TFRoFormerModel',
         'TFRoFormerPreTrainedModel',
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	List[Any] 					=     [
         'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'FlaxRoFormerForMaskedLM',
         'FlaxRoFormerForMultipleChoice',
         'FlaxRoFormerForQuestionAnswering',
         'FlaxRoFormerForSequenceClassification',
         'FlaxRoFormerForTokenClassification',
         'FlaxRoFormerModel',
         'FlaxRoFormerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
     from .tokenization_roformer import RoFormerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_roformer_fast import RoFormerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roformer import (
              ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              RoFormerForCausalLM,
              RoFormerForMaskedLM,
              RoFormerForMultipleChoice,
              RoFormerForQuestionAnswering,
              RoFormerForSequenceClassification,
              RoFormerForTokenClassification,
              RoFormerLayer,
              RoFormerModel,
              RoFormerPreTrainedModel,
              load_tf_weights_in_roformer,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_roformer import (
              TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForMultipleChoice,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerLayer,
              TFRoFormerModel,
              TFRoFormerPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_roformer import (
              FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              FlaxRoFormerForMaskedLM,
              FlaxRoFormerForMultipleChoice,
              FlaxRoFormerForQuestionAnswering,
              FlaxRoFormerForSequenceClassification,
              FlaxRoFormerForTokenClassification,
              FlaxRoFormerModel,
              FlaxRoFormerPreTrainedModel,
          )


else:
     import sys

     _a   :	List[str] 					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""simple docstring"""




class 		__A							(				SCREAMING_SNAKE_CASE_	):
     pass







class 		__A							(				SCREAMING_SNAKE_CASE_	):
     pass







class 		__A							:






     def __init__(	self						):
       _lowerCAmelCase		: Union[str, Any]						    =  [
           [],
           [],
           [],
       ]






     def 					__A						(	self       ,  a__       ,  a__						):
       try:
         if len(self.queues[priority]						) >= 100:
           raise OverflowError("""Maximum queue size is 100"""						)
         self.queues[priority].append(a__						)
       except IndexError:
         raise ValueError("""Valid priorities are 0, 1, and 2"""						)






     def 					__A						(	self						):
       for queue in self.queues:
         if queue:
           return queue.pop(0						)
       raise UnderFlowError("""All queues are empty"""						)






     def __str__(	self						):
       return "\n".join(F"Priority {i}: {q}" for i, q in enumerate(self.queues						)						)







class 		__A							:






     def __init__(	self						):
       _lowerCAmelCase		: int						    =  []






     def 					__A						(	self       ,  a__						):
       if len(self.queue						) == 100:
         raise OverFlowError("""Maximum queue size is 100"""						)
       self.queue.append(a__						)






     def 					__A						(	self						):
       if not self.queue:
         raise UnderFlowError("""The queue is empty"""						)
       else:
         _lowerCAmelCase		: int						    =  min(self.queue						)
         self.queue.remove(a__						)
         return data






     def __str__(	self						):
       return str(self.queue						)







def 							SCREAMING_SNAKE_CASE  (    ) ->   str:
  _lowerCAmelCase		: Union[str, Any]						    =  FixedPriorityQueue()
  fpq.enqueue(0    ,10			)
  fpq.enqueue(1    ,70			)
  fpq.enqueue(0    ,100			)
  fpq.enqueue(2    ,1			)
  fpq.enqueue(2    ,5			)
  fpq.enqueue(1    ,7			)
  fpq.enqueue(2    ,4			)
  fpq.enqueue(1    ,64			)
  fpq.enqueue(0    ,128			)
  print(_lowerCamelCase			)
  print(fpq.dequeue()			)
  print(fpq.dequeue()			)
  print(fpq.dequeue()			)
  print(fpq.dequeue()			)
  print(fpq.dequeue()			)
  print(_lowerCamelCase			)
  print(fpq.dequeue()			)
  print(fpq.dequeue()			)
  print(fpq.dequeue()			)
  print(fpq.dequeue()			)
  print(fpq.dequeue()			)





def 							SCREAMING_SNAKE_CASE  (    ) ->   Union[str, Any]:
  _lowerCAmelCase		: Tuple						    =  ElementPriorityQueue()
  epq.enqueue(10			)
  epq.enqueue(70			)
  epq.enqueue(100			)
  epq.enqueue(1			)
  epq.enqueue(5			)
  epq.enqueue(7			)
  epq.enqueue(4			)
  epq.enqueue(64			)
  epq.enqueue(128			)
  print(_lowerCamelCase			)
  print(epq.dequeue()			)
  print(epq.dequeue()			)
  print(epq.dequeue()			)
  print(epq.dequeue()			)
  print(epq.dequeue()			)
  print(_lowerCamelCase			)
  print(epq.dequeue()			)
  print(epq.dequeue()			)
  print(epq.dequeue()			)
  print(epq.dequeue()			)
  print(epq.dequeue()			)


if __name__ == "__main__":
     fixed_priority_queue()
     element_priority_queue()




