






"""simple docstring"""




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        UpperCamelCase			(	unittest.TestCase      ):


 def __init__(     self		:						int							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						List[str]=13							,						UpperCAmelCase__		:						Optional[int]=7							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						Tuple=True							,						UpperCAmelCase__		:						List[Any]=True							,						UpperCAmelCase__		:						str=True							,						UpperCAmelCase__		:						str=99							,						UpperCAmelCase__		:						Tuple=32							,						UpperCAmelCase__		:						List[Any]=5							,						UpperCAmelCase__		:						str=4							,						UpperCAmelCase__		:						Any=37							,						UpperCAmelCase__		:						Any="gelu"							,						UpperCAmelCase__		:						List[str]=0.1							,						UpperCAmelCase__		:						Optional[int]=0.1							,						UpperCAmelCase__		:						List[Any]=512							,						UpperCAmelCase__		:						Optional[int]=16							,						UpperCAmelCase__		:						List[str]=2							,						UpperCAmelCase__		:						Union[str, Any]=0.0_2							,						UpperCAmelCase__		:						Optional[Any]=4							,						)					->       Tuple:
   _a  :       List[Any]             =	parent
   _a  :       str             =	batch_size
   _a  :       List[Any]             =	seq_length
   _a  :       Dict             =	is_training
   _a  :       List[Any]             =	use_attention_mask
   _a  :       Any             =	use_token_type_ids
   _a  :       Optional[int]             =	use_labels
   _a  :       Tuple             =	vocab_size
   _a  :       List[str]             =	hidden_size
   _a  :       List[str]             =	num_hidden_layers
   _a  :       Optional[Any]             =	num_attention_heads
   _a  :       int             =	intermediate_size
   _a  :       Optional[Any]             =	hidden_act
   _a  :       List[Any]             =	hidden_dropout_prob
   _a  :       Optional[Any]             =	attention_probs_dropout_prob
   _a  :       Any             =	max_position_embeddings
   _a  :       Union[str, Any]             =	type_vocab_size
   _a  :       int             =	type_sequence_label_size
   _a  :       Any             =	initializer_range
   _a  :       List[str]             =	num_choices


 def _lowercase	(     self		:						List[str]							)					->       List[Any]:
   _a  :       List[str]             =	ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size							)

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

   _a  :       Union[str, Any]             =	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_=UpperCAmelCase__							,						)

   return config, input_ids, attention_mask







 def _lowercase	(     self		:						Optional[Any]							)					->       List[str]:
   _a  :       List[str]             =	self.prepare_config_and_inputs()
   _a  :       str             =	config_and_inputs
   _a  :       Any             =	{"""input_ids""": input_ids, """attention_mask""": attention_mask}
   return config, inputs_dict







@require_flax
class        UpperCamelCase			(	__UpperCAmelCase		,     unittest.TestCase      ):
 UpperCamelCase					:       Optional[Any]			=					(
     (
         FlaxDistilBertModel,
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )


 def _lowercase	(     self		:						int							)					->       Tuple:
   _a  :       Tuple             =	FlaxDistilBertModelTester(self							)







 @slow
 def _lowercase	(     self		:						str							)					->       Optional[Any]:
   for model_class_name in self.all_model_classes:
     _a  :       Union[str, Any]             =	model_class_name.from_pretrained("""distilbert-base-uncased"""							)
     _a  :       Any             =	model(np.ones((1, 1)							)							)
     self.assertIsNotNone(UpperCAmelCase__							)







@require_flax
class        UpperCamelCase			(	unittest.TestCase      ):


 @slow
 def _lowercase	(     self		:						List[Any]							)					->       List[str]:
   _a  :       List[str]             =	FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased"""							)
   _a  :       Optional[Any]             =	np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]							)
   _a  :       int             =	np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
   _a  :       Optional[int]             =	model(UpperCAmelCase__							,						attention_mask=UpperCAmelCase__							)[0]
   _a  :       Optional[Any]             =	(1, 11, 768)
   self.assertEqual(output.shape							,						UpperCAmelCase__							)
   _a  :       Union[str, Any]             =	np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]]							)

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






'''simple docstring'''




class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[Any]	=     [
				    [],
				    [],
				    [],
				]


	def     _a	(self       ,      lowercase       ,      lowercase							):
				try:
							if len(self.queues[priority]							) >= 100:
										raise OverflowError("""Maximum queue size is 100"""							)
							self.queues[priority].append(lowercase							)
				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 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[str]	=     []


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


	def     _a	(self							):
				if not self.queue:
							raise UnderFlowError("""The queue is empty"""							)
				else:
							A_	:						List[Any]	=     min(self.queue							)
							self.queue.remove(lowercase							)
							return data


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






def 	a			( ):
			'''simple docstring'''





			A_	:						Optional[int]	=     FixedPriorityQueue()
			fpq.enqueue(0						,      10    )
			fpq.enqueue(1						,      70    )
			fpq.enqueue(0						,      1_00    )
			fpq.enqueue(2						,      1    )
			fpq.enqueue(2						,      5    )
			fpq.enqueue(1						,      7    )
			fpq.enqueue(2						,      4    )
			fpq.enqueue(1						,      64    )
			fpq.enqueue(0						,      1_28    )
			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 	a			( ):
			'''simple docstring'''





			A_	:						int	=     ElementPriorityQueue()
			epq.enqueue(10    )
			epq.enqueue(70    )
			epq.enqueue(1_00    )
			epq.enqueue(1    )
			epq.enqueue(5    )
			epq.enqueue(7    )
			epq.enqueue(4    )
			epq.enqueue(64    )
			epq.enqueue(1_28    )
			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()