"""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 lowerCamelCase		:






					'''simple docstring'''


					def __init__(						self			:				List[str]   , _snake_case			:				List[str]   , _snake_case			:				List[str]=13   , _snake_case			:				Dict=7   , _snake_case			:				List[Any]=True   , _snake_case			:				List[Any]=True   , _snake_case			:				Union[str, Any]=True   , _snake_case			:				Optional[Any]=True   , _snake_case			:				Optional[int]=99   , _snake_case			:				List[Any]=16   , _snake_case			:				Optional[Any]=36   , _snake_case			:				Optional[int]=6   , _snake_case			:				Union[str, Any]=6   , _snake_case			:				Optional[int]=6   , _snake_case			:				Dict=37   , _snake_case			:				List[Any]="gelu"   , _snake_case			:				Tuple=0.1   , _snake_case			:				Optional[Any]=0.1   , _snake_case			:				List[Any]=512   , _snake_case			:				Dict=16   , _snake_case			:				Union[str, Any]=2   , _snake_case			:				Dict=0.02   , _snake_case			:				Optional[Any]=3   , _snake_case			:				Dict=4   , _snake_case			:				Dict=None   , )							->       Optional[int]:
									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 			lowerCAmelCase_					(						self			:				Dict		)							->       Any:
									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 			lowerCAmelCase_					(						self			:				str		)							->       List[str]:
									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 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				Optional[Any]   , _snake_case			:				Dict   , _snake_case			:				int   , _snake_case			:				List[str]   , _snake_case			:				List[str]   , _snake_case			:				Any   , _snake_case			:				List[Any]		)							->       Union[str, Any]:
									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 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				Union[str, Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Dict   , _snake_case			:				str   , _snake_case			:				Union[str, Any]   , _snake_case			:				int   , _snake_case			:				Tuple		)							->       Optional[int]:
									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 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				str   , _snake_case			:				List[Any]   , _snake_case			:				Any   , _snake_case			:				str   , _snake_case			:				List[Any]   , _snake_case			:				Optional[int]   , _snake_case			:				Tuple		)							->       List[str]:
									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 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				Optional[int]   , _snake_case			:				List[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Any   , _snake_case			:				int		)							->       str:
									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 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				Tuple   , _snake_case			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Optional[int]   , _snake_case			:				Optional[int]   , _snake_case			:				Union[str, Any]   , _snake_case			:				Dict		)							->       Dict:
									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 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				Dict   , _snake_case			:				List[str]   , _snake_case			:				List[str]   , _snake_case			:				Any   , _snake_case			:				Dict   , _snake_case			:				Any   , _snake_case			:				List[Any]		)							->       Union[str, Any]:
									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 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Any   , _snake_case			:				Optional[Any]   , _snake_case			:				List[Any]   , _snake_case			:				List[Any]   , _snake_case			:				Any   , _snake_case			:				Any   , _snake_case			:				Union[str, Any]		)							->       List[str]:
									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 			lowerCAmelCase_					(						self			:				str		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.prepare_config_and_inputs()
									(
									    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 lowerCamelCase		(lowercase__    ,    lowercase__    ,    unittest.TestCase      ):






					'''simple docstring'''


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



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				int   , _snake_case			:				Tuple   , _snake_case			:				Dict=False		)							->       Optional[int]:
									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 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				AlbertModelTester(self		)
									SCREAMING_SNAKE_CASE__		  =				ConfigTester(self   , config_class=__lowerCamelCase   , hidden_size=37		)



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Union[str, Any]:
									self.config_tester.run_common_tests()



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCamelCase		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*__lowerCamelCase		)



					def 			lowerCAmelCase_					(						self			:				Any		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase		)



					def 			lowerCAmelCase_					(						self			:				Any		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase		)



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase		)



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



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



@require_torch
class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					@slow
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				AlbertModel.from_pretrained("albert-base-v2"		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 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, 11, 768)		)
									self.assertEqual(output.shape   , __lowerCamelCase		)
									SCREAMING_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]   , __lowerCamelCase   , atol=1e-4		)		)



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A   					=    {
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['VisionEncoderDecoderModel']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['TFVisionEncoderDecoderModel']

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
						from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
						import sys

						_A   					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




