




import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_FOR_PRETRAINING_MAPPING,
		    AlbertForMaskedLM,
		    AlbertForMultipleChoice,
		    AlbertForPreTraining,
		    AlbertForQuestionAnswering,
		    AlbertForSequenceClassification,
		    AlbertForTokenClassification,
		    AlbertModel,
		)
		from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class 						UpperCAmelCase  :







						'''simple docstring'''





						def __init__(		self				:	str			,      __lowercase				:	Tuple			,      __lowercase				:	Tuple=13			,      __lowercase				:	List[str]=7			,      __lowercase				:	List[str]=True			,      __lowercase				:	Any=True			,      __lowercase				:	List[str]=True			,      __lowercase				:	Optional[int]=True			,      __lowercase				:	Union[str, Any]=99			,      __lowercase				:	str=16			,      __lowercase				:	Optional[Any]=36			,      __lowercase				:	Union[str, Any]=6			,      __lowercase				:	Optional[int]=6			,      __lowercase				:	Optional[int]=6			,      __lowercase				:	List[str]=37			,      __lowercase				:	Optional[int]="gelu"			,      __lowercase				:	List[str]=0.1			,      __lowercase				:	List[str]=0.1			,      __lowercase				:	Tuple=5_12			,      __lowercase				:	List[str]=16			,      __lowercase				:	int=2			,      __lowercase				:	Tuple=0.02			,      __lowercase				:	Optional[int]=3			,      __lowercase				:	List[Any]=4			,      __lowercase				:	int=None			,      ):






							"""simple docstring"""






							snake_case_						       =	parent
							snake_case_						       =	batch_size
							snake_case_						       =	seq_length
							snake_case_						       =	is_training
							snake_case_						       =	use_input_mask
							snake_case_						       =	use_token_type_ids
							snake_case_						       =	use_labels
							snake_case_						       =	vocab_size
							snake_case_						       =	embedding_size
							snake_case_						       =	hidden_size
							snake_case_						       =	num_hidden_layers
							snake_case_						       =	num_hidden_groups
							snake_case_						       =	num_attention_heads
							snake_case_						       =	intermediate_size
							snake_case_						       =	hidden_act
							snake_case_						       =	hidden_dropout_prob
							snake_case_						       =	attention_probs_dropout_prob
							snake_case_						       =	max_position_embeddings
							snake_case_						       =	type_vocab_size
							snake_case_						       =	type_sequence_label_size
							snake_case_						       =	initializer_range
							snake_case_						       =	num_labels
							snake_case_						       =	num_choices
							snake_case_						       =	scope






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






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

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

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

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

							snake_case_						       =	self.get_config()

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






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							return AlbertConfig(
							    vocab_size=self.vocab_size			,      hidden_size=self.hidden_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			,      num_hidden_groups=self.num_hidden_groups			,      )






						def 			snake_case__		(		self				:	int			,      __lowercase				:	Dict			,      __lowercase				:	Dict			,      __lowercase				:	str			,      __lowercase				:	Optional[int]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	str			,      __lowercase				:	int		):






							"""simple docstring"""






							snake_case_						       =	AlbertModel(config=__A		)
							model.to(__A		)
							model.eval()
							snake_case_						       =	model(__A			,      attention_mask=__A			,      token_type_ids=__A		)
							snake_case_						       =	model(__A			,      token_type_ids=__A		)
							snake_case_						       =	model(__A		)
							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 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	str			,      __lowercase				:	Tuple			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	Any			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	Tuple			,      __lowercase				:	List[Any]		):






							"""simple docstring"""






							snake_case_						       =	AlbertForPreTraining(config=__A		)
							model.to(__A		)
							model.eval()
							snake_case_						       =	model(
							    __A			,      attention_mask=__A			,      token_type_ids=__A			,      labels=__A			,      sentence_order_label=__A			,      )
							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, config.num_labels)		)






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	Dict			,      __lowercase				:	int			,      __lowercase				:	Optional[int]			,      __lowercase				:	Optional[int]			,      __lowercase				:	Any			,      __lowercase				:	Dict			,      __lowercase				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	AlbertForMaskedLM(config=__A		)
							model.to(__A		)
							model.eval()
							snake_case_						       =	model(__A			,      attention_mask=__A			,      token_type_ids=__A			,      labels=__A		)
							self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.vocab_size)		)






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	Optional[int]			,      __lowercase				:	Any			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	int			,      __lowercase				:	Any			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	AlbertForQuestionAnswering(config=__A		)
							model.to(__A		)
							model.eval()
							snake_case_						       =	model(
							    __A			,      attention_mask=__A			,      token_type_ids=__A			,      start_positions=__A			,      end_positions=__A			,      )
							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 			snake_case__		(		self				:	Optional[int]			,      __lowercase				:	Tuple			,      __lowercase				:	str			,      __lowercase				:	Tuple			,      __lowercase				:	List[str]			,      __lowercase				:	int			,      __lowercase				:	List[Any]			,      __lowercase				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.num_labels
							snake_case_						       =	AlbertForSequenceClassification(__A		)
							model.to(__A		)
							model.eval()
							snake_case_						       =	model(__A			,      attention_mask=__A			,      token_type_ids=__A			,      labels=__A		)
							self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.num_labels)		)






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	List[Any]			,      __lowercase				:	Tuple			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Optional[int]			,      __lowercase				:	str			,      __lowercase				:	Tuple			,      __lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.num_labels
							snake_case_						       =	AlbertForTokenClassification(config=__A		)
							model.to(__A		)
							model.eval()
							snake_case_						       =	model(__A			,      attention_mask=__A			,      token_type_ids=__A			,      labels=__A		)
							self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.num_labels)		)






						def 			snake_case__		(		self				:	Optional[Any]			,      __lowercase				:	List[Any]			,      __lowercase				:	Any			,      __lowercase				:	Optional[int]			,      __lowercase				:	Optional[int]			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	Dict			,      __lowercase				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.num_choices
							snake_case_						       =	AlbertForMultipleChoice(config=__A		)
							model.to(__A		)
							model.eval()
							snake_case_						       =	input_ids.unsqueeze(1		).expand(-1			,      self.num_choices			,      -1		).contiguous()
							snake_case_						       =	token_type_ids.unsqueeze(1		).expand(-1			,      self.num_choices			,      -1		).contiguous()
							snake_case_						       =	input_mask.unsqueeze(1		).expand(-1			,      self.num_choices			,      -1		).contiguous()
							snake_case_						       =	model(
							    __A			,      attention_mask=__A			,      token_type_ids=__A			,      labels=__A			,      )
							self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.num_choices)		)






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.prepare_config_and_inputs()
							(
							    snake_case_				
							)						       =	config_and_inputs
							snake_case_						       =	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
							return config, inputs_dict







@require_torch
class 						UpperCAmelCase  (							_lowercase			,  _lowercase			,  unittest.TestCase     ):







						'''simple docstring'''





						lowerCAmelCase_							=  (
						    (
						        AlbertModel,
						        AlbertForPreTraining,
						        AlbertForMaskedLM,
						        AlbertForMultipleChoice,
						        AlbertForSequenceClassification,
						        AlbertForTokenClassification,
						        AlbertForQuestionAnswering,
						    )
						    if is_torch_available()
						    else ()
						)
						lowerCAmelCase_							=  (
						    {
						        '''feature-extraction''': AlbertModel,
						        '''fill-mask''': AlbertForMaskedLM,
						        '''question-answering''': AlbertForQuestionAnswering,
						        '''text-classification''': AlbertForSequenceClassification,
						        '''token-classification''': AlbertForTokenClassification,
						        '''zero-shot''': AlbertForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						lowerCAmelCase_							=  True






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Tuple=False		):






							"""simple docstring"""






							snake_case_						       =	super()._prepare_for_class(__A			,      __A			,      return_labels=__A		)

							if return_labels:
								if model_class in get_values(__A		):
									snake_case_						       =	torch.zeros(
									    (self.model_tester.batch_size, self.model_tester.seq_length)			,      dtype=torch.long			,      device=__A		)
									snake_case_						       =	torch.zeros(
									    self.model_tester.batch_size			,      dtype=torch.long			,      device=__A		)
							return inputs_dict






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	AlbertModelTester(self		)
							snake_case_						       =	ConfigTester(self			,      config_class=__A			,      hidden_size=37		)






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							self.config_tester.run_common_tests()






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__A		)






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*__A		)






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__A		)






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*__A		)






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__A		)






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__A		)






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
								snake_case_						       =	type
								self.model_tester.create_and_check_model(*__A		)






						@slow
						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								snake_case_						       =	AlbertModel.from_pretrained(__A		)
								self.assertIsNotNone(__A		)







@require_torch
class 						UpperCAmelCase  (							unittest.TestCase     ):







						'''simple docstring'''





						@slow
						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							snake_case_						       =	AlbertModel.from_pretrained("albert-base-v2"		)
							snake_case_						       =	torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]		)
							snake_case_						       =	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
							with torch.no_grad():
								snake_case_						       =	model(__A			,      attention_mask=__A		)[0]
							snake_case_						       =	torch.Size((1, 11, 7_68)		)
							self.assertEqual(output.shape			,      __A		)
							snake_case_						       =	torch.tensor(
							    [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]		)

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



class      lowerCamelCase_      :




	def __init__(     self						:     Dict					,      __A						:     Tuple					,      __A						:     Optional[int]					,      __A						:     int							):
		__A :			List[str]		    =			name
		__A :			Optional[int]		    =			value
		__A :			Optional[Any]		    =			weight




	def __repr__(     self						:     Any							):
		return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		return self.value




	def        lowerCAmelCase_       (     self						:     str							):
		return self.name




	def        lowerCAmelCase_       (     self						:     str							):
		return self.weight




	def        lowerCAmelCase_       (     self						:     Dict							):
		return self.value / self.weight






def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   Optional[int]			,a__    :   Union[str, Any]       )					->				int:
	__A :			Tuple		    =			[]
	for i in range(len(a__       )       ):
		menu.append(Things(name[i]			,value[i]			,weight[i]       )       )
	return menu
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple			,a__    :   Any			,a__    :   Optional[int]       )					->				Tuple:
	__A :			Optional[int]		    =			sorted(a__			,key=a__			,reverse=a__       )
	__A :			Optional[Any]		    =			[]
	__A    ,				__A :			Tuple		    =			0.0, 0.0
	for i in range(len(a__       )       ):
		if (total_cost + items_copy[i].get_weight()) <= max_cost:
			result.append(items_copy[i]       )
			total_cost += items_copy[i].get_weight()
			total_value += items_copy[i].get_value()
	return (result, total_value)
def 							__SCREAMING_SNAKE_CASE     (   )					->				List[Any]:
	pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()





