



from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
							from transformers.models.albert.modeling_tf_albert import (
							    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFAlbertForMaskedLM,
							    TFAlbertForMultipleChoice,
							    TFAlbertForPreTraining,
							    TFAlbertForQuestionAnswering,
							    TFAlbertForSequenceClassification,
							    TFAlbertForTokenClassification,
							    TFAlbertModel,
							)







class 				a_    :

						def __init__(       self      :Optional[Any]  ,		_lowercase      :str  ,		_lowercase      :List[Any]=13  ,		_lowercase      :List[Any]=7  ,		_lowercase      :List[Any]=True  ,		_lowercase      :Optional[int]=True  ,		_lowercase      :str=True  ,		_lowercase      :List[Any]=True  ,		_lowercase      :List[Any]=99  ,		_lowercase      :int=16  ,		_lowercase      :Dict=32  ,		_lowercase      :Tuple=2  ,		_lowercase      :Optional[int]=4  ,		_lowercase      :int=37  ,		_lowercase      :List[str]="gelu"  ,		_lowercase      :Tuple=0.1  ,		_lowercase      :Any=0.1  ,		_lowercase      :List[Any]=512  ,		_lowercase      :Optional[int]=16  ,		_lowercase      :int=2  ,		_lowercase      :Tuple=0.02  ,		_lowercase      :Optional[int]=3  ,		_lowercase      :Any=4  ,		_lowercase      :List[Any]=None  ,		)						->   Optional[Any]:
												UpperCAmelCase_					=	parent
												UpperCAmelCase_					=	13
												UpperCAmelCase_					=	7
												UpperCAmelCase_					=	True
												UpperCAmelCase_					=	True
												UpperCAmelCase_					=	True
												UpperCAmelCase_					=	True
												UpperCAmelCase_					=	99
												UpperCAmelCase_					=	16
												UpperCAmelCase_					=	32
												UpperCAmelCase_					=	2
												UpperCAmelCase_					=	4
												UpperCAmelCase_					=	37
												UpperCAmelCase_					=	'''gelu'''
												UpperCAmelCase_					=	0.1
												UpperCAmelCase_					=	0.1
												UpperCAmelCase_					=	512
												UpperCAmelCase_					=	16
												UpperCAmelCase_					=	2
												UpperCAmelCase_					=	0.02
												UpperCAmelCase_					=	3
												UpperCAmelCase_					=	4
												UpperCAmelCase_					=	None

						def       __a			(       self      :int)						->   Tuple:
												UpperCAmelCase_					=	ids_tensor([self.batch_size, self.seq_length]  ,		self.vocab_size)

												UpperCAmelCase_					=	None
												if self.use_input_mask:
																		UpperCAmelCase_					=	random_attention_mask([self.batch_size, self.seq_length])

												UpperCAmelCase_					=	None
												if self.use_token_type_ids:
																		UpperCAmelCase_					=	ids_tensor([self.batch_size, self.seq_length]  ,		self.type_vocab_size)

												UpperCAmelCase_					=	None
												UpperCAmelCase_					=	None
												UpperCAmelCase_					=	None
												if self.use_labels:
																		UpperCAmelCase_					=	ids_tensor([self.batch_size]  ,		self.type_sequence_label_size)
																		UpperCAmelCase_					=	ids_tensor([self.batch_size, self.seq_length]  ,		self.num_labels)
																		UpperCAmelCase_					=	ids_tensor([self.batch_size]  ,		self.num_choices)

												UpperCAmelCase_					=	AlbertConfig(
												    vocab_size=self.vocab_size  ,		hidden_size=self.hidden_size  ,		embedding_size=self.embedding_size  ,		num_hidden_layers=self.num_hidden_layers  ,		num_attention_heads=self.num_attention_heads  ,		intermediate_size=self.intermediate_size  ,		hidden_act=self.hidden_act  ,		hidden_dropout_prob=self.hidden_dropout_prob  ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,		max_position_embeddings=self.max_position_embeddings  ,		type_vocab_size=self.type_vocab_size  ,		initializer_range=self.initializer_range  ,		)

												return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

						def       __a			(       self      :Optional[Any]  ,		_lowercase      :Optional[Any]  ,		_lowercase      :Tuple  ,		_lowercase      :List[Any]  ,		_lowercase      :List[str]  ,		_lowercase      :List[str]  ,		_lowercase      :int  ,		_lowercase      :Dict)						->   List[str]:
												UpperCAmelCase_					=	TFAlbertModel(config=lowerCAmelCase__)
												# inputs = {'input_ids': input_ids,
												#           'attention_mask': input_mask,
												#           'token_type_ids': token_type_ids}
												# sequence_output, pooled_output = model(**inputs)
												UpperCAmelCase_					=	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
												UpperCAmelCase_					=	model(lowerCAmelCase__)

												UpperCAmelCase_					=	[input_ids, input_mask]
												UpperCAmelCase_					=	model(lowerCAmelCase__)

												UpperCAmelCase_					=	model(lowerCAmelCase__)

												self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, self.seq_length, self.hidden_size))
												self.parent.assertEqual(result.pooler_output.shape  ,		(self.batch_size, self.hidden_size))

						def       __a			(       self      :List[str]  ,		_lowercase      :int  ,		_lowercase      :Optional[Any]  ,		_lowercase      :Optional[int]  ,		_lowercase      :Any  ,		_lowercase      :int  ,		_lowercase      :Optional[Any]  ,		_lowercase      :Optional[int])						->   Any:
												UpperCAmelCase_					=	self.num_labels
												UpperCAmelCase_					=	TFAlbertForPreTraining(config=lowerCAmelCase__)
												UpperCAmelCase_					=	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
												UpperCAmelCase_					=	model(lowerCAmelCase__)
												self.parent.assertEqual(result.prediction_logits.shape  ,		(self.batch_size, self.seq_length, self.vocab_size))
												self.parent.assertEqual(result.sop_logits.shape  ,		(self.batch_size, self.num_labels))

						def       __a			(       self      :Union[str, Any]  ,		_lowercase      :Dict  ,		_lowercase      :Any  ,		_lowercase      :List[str]  ,		_lowercase      :Any  ,		_lowercase      :Optional[int]  ,		_lowercase      :Dict  ,		_lowercase      :Union[str, Any])						->   Optional[int]:
												UpperCAmelCase_					=	TFAlbertForMaskedLM(config=lowerCAmelCase__)
												UpperCAmelCase_					=	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
												UpperCAmelCase_					=	model(lowerCAmelCase__)
												self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length, self.vocab_size))

						def       __a			(       self      :Union[str, Any]  ,		_lowercase      :Any  ,		_lowercase      :Union[str, Any]  ,		_lowercase      :Dict  ,		_lowercase      :Union[str, Any]  ,		_lowercase      :List[str]  ,		_lowercase      :Optional[int]  ,		_lowercase      :int)						->   str:
												UpperCAmelCase_					=	self.num_labels
												UpperCAmelCase_					=	TFAlbertForSequenceClassification(config=lowerCAmelCase__)
												UpperCAmelCase_					=	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
												UpperCAmelCase_					=	model(lowerCAmelCase__)
												self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.num_labels))

						def       __a			(       self      :Union[str, Any]  ,		_lowercase      :int  ,		_lowercase      :Union[str, Any]  ,		_lowercase      :List[str]  ,		_lowercase      :Tuple  ,		_lowercase      :List[Any]  ,		_lowercase      :List[str]  ,		_lowercase      :int)						->   Dict:
												UpperCAmelCase_					=	TFAlbertForQuestionAnswering(config=lowerCAmelCase__)
												UpperCAmelCase_					=	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
												UpperCAmelCase_					=	model(lowerCAmelCase__)
												self.parent.assertEqual(result.start_logits.shape  ,		(self.batch_size, self.seq_length))
												self.parent.assertEqual(result.end_logits.shape  ,		(self.batch_size, self.seq_length))

						def       __a			(       self      :List[Any]  ,		_lowercase      :int  ,		_lowercase      :Optional[Any]  ,		_lowercase      :int  ,		_lowercase      :str  ,		_lowercase      :Tuple  ,		_lowercase      :List[Any]  ,		_lowercase      :int)						->   Union[str, Any]:
												UpperCAmelCase_					=	self.num_choices
												UpperCAmelCase_					=	TFAlbertForMultipleChoice(config=lowerCAmelCase__)
												UpperCAmelCase_					=	tf.tile(tf.expand_dims(lowerCAmelCase__  ,		1)  ,		(1, self.num_choices, 1))
												UpperCAmelCase_					=	tf.tile(tf.expand_dims(lowerCAmelCase__  ,		1)  ,		(1, self.num_choices, 1))
												UpperCAmelCase_					=	tf.tile(tf.expand_dims(lowerCAmelCase__  ,		1)  ,		(1, self.num_choices, 1))
												UpperCAmelCase_					=	{
												    '''input_ids''': multiple_choice_inputs_ids,
												    '''attention_mask''': multiple_choice_input_mask,
												    '''token_type_ids''': multiple_choice_token_type_ids,
												}
												UpperCAmelCase_					=	model(lowerCAmelCase__)
												self.parent.assertListEqual(list(result['''logits'''].shape)  ,		[self.batch_size, self.num_choices])

						def       __a			(       self      :Union[str, Any]  ,		_lowercase      :Any  ,		_lowercase      :Dict  ,		_lowercase      :Any  ,		_lowercase      :str  ,		_lowercase      :int  ,		_lowercase      :List[Any]  ,		_lowercase      :Any)						->   Optional[Any]:
												UpperCAmelCase_					=	self.num_labels
												UpperCAmelCase_					=	TFAlbertForTokenClassification(config=lowerCAmelCase__)
												UpperCAmelCase_					=	{
												    '''input_ids''': input_ids,
												    '''attention_mask''': input_mask,
												    '''token_type_ids''': token_type_ids,
												}
												UpperCAmelCase_					=	model(lowerCAmelCase__)
												self.parent.assertListEqual(list(result['''logits'''].shape)  ,		[self.batch_size, self.seq_length, self.num_labels])




						def       __a			(       self      :Union[str, Any])						->   Tuple:
												UpperCAmelCase_					=	self.prepare_config_and_inputs()
												(
												    (
												    UpperCAmelCase_      
												)				, (
												    UpperCAmelCase_      
												)				, (
												    UpperCAmelCase_      
												)				, (
												    UpperCAmelCase_      
												)				, (
												    UpperCAmelCase_      
												)				, (
												    UpperCAmelCase_      
												)				, (
												    UpperCAmelCase_      
												)				,       
												)					=	config_and_inputs
												UpperCAmelCase_					=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
												return config, inputs_dict







@require_tf
class 				a_    (						_a   ,					_a   ,					unittest.TestCase							):
						UpperCamelCase__ :		Union[str, Any]										=(
						    (
						        TFAlbertModel,
						        TFAlbertForPreTraining,
						        TFAlbertForMaskedLM,
						        TFAlbertForSequenceClassification,
						        TFAlbertForQuestionAnswering,
						        TFAlbertForTokenClassification,
						        TFAlbertForMultipleChoice,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCamelCase__ :		Any										=(
						    {
						        """feature-extraction""": TFAlbertModel,
						        """fill-mask""": TFAlbertForMaskedLM,
						        """question-answering""": TFAlbertForQuestionAnswering,
						        """text-classification""": TFAlbertForSequenceClassification,
						        """token-classification""": TFAlbertForTokenClassification,
						        """zero-shot""": TFAlbertForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						UpperCamelCase__ :		str										=False
						UpperCamelCase__ :		Optional[int]										=False

						def       __a			(       self      :int  ,		_lowercase      :Optional[Any]  ,		_lowercase      :str  ,		_lowercase      :int=False)						->   Union[str, Any]:
												UpperCAmelCase_					=	super()._prepare_for_class(lowerCAmelCase__  ,		lowerCAmelCase__  ,		return_labels=lowerCAmelCase__)

												if return_labels:
																		if model_class in get_values(lowerCAmelCase__):
																								UpperCAmelCase_					=	tf.zeros(self.model_tester.batch_size  ,		dtype=tf.intaa)

												return inputs_dict

						def       __a			(       self      :Tuple)						->   List[str]:
												UpperCAmelCase_					=	TFAlbertModelTester(self)
												UpperCAmelCase_					=	ConfigTester(self  ,		config_class=lowerCAmelCase__  ,		hidden_size=37)

						def       __a			(       self      :Optional[int])						->   List[str]:
												self.config_tester.run_common_tests()

						def       __a			(       self      :Optional[int])						->   List[Any]:
												UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_albert_model(*lowerCAmelCase__)

						def       __a			(       self      :List[str])						->   Dict:
												UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_albert_for_pretraining(*lowerCAmelCase__)

						def       __a			(       self      :Tuple)						->   Union[str, Any]:
												UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_albert_for_masked_lm(*lowerCAmelCase__)

						def       __a			(       self      :List[Any])						->   Union[str, Any]:
												UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_albert_for_multiple_choice(*lowerCAmelCase__)

						def       __a			(       self      :int)						->   str:
												UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_albert_for_sequence_classification(*lowerCAmelCase__)

						def       __a			(       self      :Dict)						->   Union[str, Any]:
												UpperCAmelCase_					=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_albert_for_question_answering(*lowerCAmelCase__)




						@slow
						def       __a			(       self      :Any)						->   Dict:
												for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		UpperCAmelCase_					=	TFAlbertModel.from_pretrained(lowerCAmelCase__)
																		self.assertIsNotNone(lowerCAmelCase__)







@require_tf
class 				a_    (						unittest.TestCase							):

						@slow
						def       __a			(       self      :Union[str, Any])						->   List[str]:
												UpperCAmelCase_					=	TFAlbertForPreTraining.from_pretrained('''albert-base-v2''')
												UpperCAmelCase_					=	tf.constant([[0, 1, 2, 3, 4, 5]])
												UpperCAmelCase_					=	model(lowerCAmelCase__)[0]

												UpperCAmelCase_					=	[1, 6, 30000]
												self.assertEqual(output.shape  ,		lowerCAmelCase__)

												UpperCAmelCase_					=	tf.constant(
												    [
												        [
												            [4.595_668, 0.74_462_754, -1.818_147],
												            [4.5_954_347, 0.7_454_184, -1.8_188_258],
												            [4.5_954_905, 0.7_448_235, -1.8_182_316],
												        ]
												    ])
												tf.debugging.assert_near(output[:, :3, :3]  ,		lowerCAmelCase__  ,		atol=1E-4)






import heapq

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )		->    Dict:




       '''simple docstring'''






       _A=						[(0, start)]  # cost from start node,end node
       _A=						set()
       while heap:
              ((_A),      (_A))=						heapq.heappop(lowerCAmelCase_ )
              if u in visited:
                     continue
              visited.add(lowerCAmelCase_ )
              if u == end:
                     return cost
              for v, c in graph[u]:
                     if v in visited:
                            continue
                     _A=						cost + c
                     heapq.heappush(lowerCAmelCase_							,       (next_item, v) )
       return -1


UpperCAmelCase_    		=				{
    '''A''': [['''B''', 2], ['''C''', 5]],
    '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
    '''C''': [['''A''', 5], ['''F''', 3]],
    '''D''': [['''B''', 3]],
    '''E''': [['''B''', 4], ['''F''', 3]],
    '''F''': [['''C''', 3], ['''E''', 3]],
}
UpperCAmelCase_    		=				{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''F''', 3]],
    '''F''': [],
}
UpperCAmelCase_    		=				{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}

UpperCAmelCase_    		=				dijkstra(G, '''E''', '''C''')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

UpperCAmelCase_    		=				dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 3 --> F == 3

UpperCAmelCase_    		=				dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
      import doctest

      doctest.testmod()