
"""simple docstring"""





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
							from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline






"""simple docstring"""





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 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__=1_3							,  lowerCAmelCase__=7							,  lowerCAmelCase__=True							,  lowerCAmelCase__=True							,  lowerCAmelCase__=True							,  lowerCAmelCase__=True							,  lowerCAmelCase__=9_9							,  lowerCAmelCase__=1_6							,  lowerCAmelCase__=3_6							,  lowerCAmelCase__=6							,  lowerCAmelCase__=6							,  lowerCAmelCase__=6							,  lowerCAmelCase__=3_7							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=5_1_2							,  lowerCAmelCase__=1_6							,  lowerCAmelCase__=2							,  lowerCAmelCase__=0.02							,  lowerCAmelCase__=3							,  lowerCAmelCase__=4							,  lowerCAmelCase__=None							,  ):
				__SCREAMING_SNAKE_CASE            = parent
				__SCREAMING_SNAKE_CASE            = batch_size
				__SCREAMING_SNAKE_CASE            = seq_length
				__SCREAMING_SNAKE_CASE            = is_training
				__SCREAMING_SNAKE_CASE            = use_input_mask
				__SCREAMING_SNAKE_CASE            = use_token_type_ids
				__SCREAMING_SNAKE_CASE            = use_labels
				__SCREAMING_SNAKE_CASE            = vocab_size
				__SCREAMING_SNAKE_CASE            = embedding_size
				__SCREAMING_SNAKE_CASE            = hidden_size
				__SCREAMING_SNAKE_CASE            = num_hidden_layers
				__SCREAMING_SNAKE_CASE            = num_hidden_groups
				__SCREAMING_SNAKE_CASE            = num_attention_heads
				__SCREAMING_SNAKE_CASE            = intermediate_size
				__SCREAMING_SNAKE_CASE            = hidden_act
				__SCREAMING_SNAKE_CASE            = hidden_dropout_prob
				__SCREAMING_SNAKE_CASE            = attention_probs_dropout_prob
				__SCREAMING_SNAKE_CASE            = max_position_embeddings
				__SCREAMING_SNAKE_CASE            = type_vocab_size
				__SCREAMING_SNAKE_CASE            = type_sequence_label_size
				__SCREAMING_SNAKE_CASE            = initializer_range
				__SCREAMING_SNAKE_CASE            = num_labels
				__SCREAMING_SNAKE_CASE            = num_choices
				__SCREAMING_SNAKE_CASE            = scope




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

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

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

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

				__SCREAMING_SNAKE_CASE            = self.get_config()

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




		def 	snake_case_     (     self):
				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							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = AlbertModel(config=lowerCAmelCase__)
				model.to(lowerCAmelCase__)
				model.eval()
				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  token_type_ids=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  token_type_ids=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = 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 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = AlbertForPreTraining(config=lowerCAmelCase__)
				model.to(lowerCAmelCase__)
				model.eval()
				__SCREAMING_SNAKE_CASE            = model(
				    lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  token_type_ids=lowerCAmelCase__							,  labels=lowerCAmelCase__							,  sentence_order_label=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, config.num_labels))




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




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = AlbertForQuestionAnswering(config=lowerCAmelCase__)
				model.to(lowerCAmelCase__)
				model.eval()
				__SCREAMING_SNAKE_CASE            = model(
				    lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  token_type_ids=lowerCAmelCase__							,  start_positions=lowerCAmelCase__							,  end_positions=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 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self.num_labels
				__SCREAMING_SNAKE_CASE            = AlbertForSequenceClassification(lowerCAmelCase__)
				model.to(lowerCAmelCase__)
				model.eval()
				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  token_type_ids=lowerCAmelCase__							,  labels=lowerCAmelCase__)
				self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_labels))




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




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




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



@require_torch
class 						SCREAMING_SNAKE_CASE_ (						__a     ,				__a     ,				unittest.TestCase ):




		"""simple docstring"""





		__lowercase							:  Union[str, Any]						   =		(
		    (
		        AlbertModel,
		        AlbertForPreTraining,
		        AlbertForMaskedLM,
		        AlbertForMultipleChoice,
		        AlbertForSequenceClassification,
		        AlbertForTokenClassification,
		        AlbertForQuestionAnswering,
		    )
		    if is_torch_available()
		    else ()
		)
		__lowercase							:  Optional[int]						   =		(
		    {
		        '''feature-extraction''': AlbertModel,
		        '''fill-mask''': AlbertForMaskedLM,
		        '''question-answering''': AlbertForQuestionAnswering,
		        '''text-classification''': AlbertForSequenceClassification,
		        '''token-classification''': AlbertForTokenClassification,
		        '''zero-shot''': AlbertForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		__lowercase							:  List[Any]						   =		True




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__=False):
				__SCREAMING_SNAKE_CASE            = super()._prepare_for_class(lowerCAmelCase__							,  lowerCAmelCase__							,  return_labels=lowerCAmelCase__)

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




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = AlbertModelTester(self)
				__SCREAMING_SNAKE_CASE            = ConfigTester(self							,  config_class=lowerCAmelCase__							,  hidden_size=3_7)




		def 	snake_case_     (     self):
				self.config_tester.run_common_tests()




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__)




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




		@slow
		def 	snake_case_     (     self):
				for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__SCREAMING_SNAKE_CASE            = AlbertModel.from_pretrained(lowerCAmelCase__)
						self.assertIsNotNone(lowerCAmelCase__)



@require_torch
class 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





		@slow
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = AlbertModel.from_pretrained("""albert-base-v2""")
				__SCREAMING_SNAKE_CASE            = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]])
				__SCREAMING_SNAKE_CASE            = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
				with torch.no_grad():
						__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__)[0]
				__SCREAMING_SNAKE_CASE            = torch.Size((1, 1_1, 7_6_8))
				self.assertEqual(output.shape							,  lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = torch.tensor(
				    [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]])

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




