





class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   pass






class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   pass






class     SCREAMING_SNAKE_CASE       :


   def __init__(   self      :    Dict	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  [
         [],
         [],
         [],
     ]


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    int      ,					a      :    int	)->       None:





     """simple docstring"""







     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 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       int:





     """simple docstring"""







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


   def __str__(   self      :    Dict	)->       str:





     """simple docstring"""







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






class     SCREAMING_SNAKE_CASE       :


   def __init__(   self      :    Optional[Any]	)->       Any:





     """simple docstring"""







     lowercase__           =  []


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    int	)->       None:





     """simple docstring"""







     if len(self.queue	) == 100:
       raise OverFlowError('Maximum queue size is 100'	)
     self.queue.append(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       int:





     """simple docstring"""







     if not self.queue:
       raise UnderFlowError('The queue is empty'	)
     else:
       lowercase__           =  min(self.queue	)
       self.queue.remove(a	)
       return data


   def __str__(   self      :    Union[str, Any]	)->       str:





     """simple docstring"""







     return str(self.queue	)






def       __UpperCamelCase  ()    ->       str:
  lowercase__           =  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(_SCREAMING_SNAKE_CASE		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(_SCREAMING_SNAKE_CASE		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)




def       __UpperCamelCase  ()    ->       List[str]:
  lowercase__           =  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(_SCREAMING_SNAKE_CASE		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(_SCREAMING_SNAKE_CASE		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)


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









import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      import jax.numpy as jnp

      from transformers.models.distilbert.modeling_flax_distilbert import (
          FlaxDistilBertForMaskedLM,
          FlaxDistilBertForMultipleChoice,
          FlaxDistilBertForQuestionAnswering,
          FlaxDistilBertForSequenceClassification,
          FlaxDistilBertForTokenClassification,
          FlaxDistilBertModel,
      )
class     SCREAMING_SNAKE_CASE       (unittest.TestCase				):


   def __init__(   self      :    List[str]      ,					a      :    Dict      ,					a      :    Optional[int]=13      ,					a      :    int=7      ,					a      :    List[str]=True      ,					a      :    Any=True      ,					a      :    Dict=True      ,					a      :    List[Any]=True      ,					a      :    List[str]=99      ,					a      :    Dict=32      ,					a      :    List[str]=5      ,					a      :    Tuple=4      ,					a      :    Optional[int]=37      ,					a      :    Union[str, Any]="gelu"      ,					a      :    Optional[Any]=0.1      ,					a      :    Optional[int]=0.1      ,					a      :    Optional[Any]=512      ,					a      :    Dict=16      ,					a      :    Any=2      ,					a      :    Tuple=0.02      ,					a      :    Optional[Any]=4      ,					)->       int:





     """simple docstring"""







     lowercase__           =  parent
     lowercase__           =  batch_size
     lowercase__           =  seq_length
     lowercase__           =  is_training
     lowercase__           =  use_attention_mask
     lowercase__           =  use_token_type_ids
     lowercase__           =  use_labels
     lowercase__           =  vocab_size
     lowercase__           =  hidden_size
     lowercase__           =  num_hidden_layers
     lowercase__           =  num_attention_heads
     lowercase__           =  intermediate_size
     lowercase__           =  hidden_act
     lowercase__           =  hidden_dropout_prob
     lowercase__           =  attention_probs_dropout_prob
     lowercase__           =  max_position_embeddings
     lowercase__           =  type_vocab_size
     lowercase__           =  type_sequence_label_size
     lowercase__           =  initializer_range
     lowercase__           =  num_choices


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       Union[str, Any]:





     """simple docstring"""







     lowercase__           =  ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size	)

     lowercase__           =  None
     if self.use_attention_mask:
       lowercase__           =  random_attention_mask([self.batch_size, self.seq_length]	)

     lowercase__           =  DistilBertConfig(
         vocab_size=self.vocab_size      ,					dim=self.hidden_size      ,					n_layers=self.num_hidden_layers      ,					n_heads=self.num_attention_heads      ,					hidden_dim=self.intermediate_size      ,					hidden_act=self.hidden_act      ,					dropout=self.hidden_dropout_prob      ,					attention_dropout=self.attention_probs_dropout_prob      ,					max_position_embeddings=self.max_position_embeddings      ,					initializer_range=self.initializer_range      ,					tie_weights_=a      ,					)

     return config, input_ids, attention_mask


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       Any:





     """simple docstring"""







     lowercase__           =  self.prepare_config_and_inputs()
     lowercase__      ,   lowercase__      ,   lowercase__           =  config_and_inputs
     lowercase__           =  {'input_ids': input_ids, 'attention_mask': attention_mask}
     return config, inputs_dict






@require_flax
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     unittest.TestCase				):
   _UpperCamelCase   :		List[Any]			 =	(
       (
           FlaxDistilBertModel,
           FlaxDistilBertForMaskedLM,
           FlaxDistilBertForMultipleChoice,
           FlaxDistilBertForQuestionAnswering,
           FlaxDistilBertForSequenceClassification,
           FlaxDistilBertForTokenClassification,
           FlaxDistilBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       List[str]:





     """simple docstring"""







     lowercase__           =  FlaxDistilBertModelTester(self	)


   @slow
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       int:





     """simple docstring"""







     for model_class_name in self.all_model_classes:
       lowercase__           =  model_class_name.from_pretrained('distilbert-base-uncased'	)
       lowercase__           =  model(np.ones((1, 1)	)	)
       self.assertIsNotNone(a	)






@require_flax
class     SCREAMING_SNAKE_CASE       (unittest.TestCase				):


   @slow
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       List[str]:





     """simple docstring"""







     lowercase__           =  FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'	)
     lowercase__           =  np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]	)
     lowercase__           =  np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
     lowercase__           =  model(a      ,					attention_mask=a	)[0]
     lowercase__           =  (1, 11, 768)
     self.assertEqual(output.shape      ,					a	)
     lowercase__           =  np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]	)

     self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]      ,					a      ,					atol=1E-4	)	)


