


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:


					def __init__(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		Optional[int]=1_3  ,     SCREAMING_SNAKE_CASE						:		str=7  ,     SCREAMING_SNAKE_CASE						:		int=True  ,     SCREAMING_SNAKE_CASE						:		Tuple=True  ,     SCREAMING_SNAKE_CASE						:		Any=True  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=True  ,     SCREAMING_SNAKE_CASE						:		int=9_9  ,     SCREAMING_SNAKE_CASE						:		Any=1_6  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=3_6  ,     SCREAMING_SNAKE_CASE						:		Any=6  ,     SCREAMING_SNAKE_CASE						:		List[str]=6  ,     SCREAMING_SNAKE_CASE						:		Dict=6  ,     SCREAMING_SNAKE_CASE						:		Optional[int]=3_7  ,     SCREAMING_SNAKE_CASE						:		str="gelu"  ,     SCREAMING_SNAKE_CASE						:		Any=0.1  ,     SCREAMING_SNAKE_CASE						:		Optional[int]=0.1  ,     SCREAMING_SNAKE_CASE						:		List[Any]=5_1_2  ,     SCREAMING_SNAKE_CASE						:		Optional[int]=1_6  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=2  ,     SCREAMING_SNAKE_CASE						:		Tuple=0.02  ,     SCREAMING_SNAKE_CASE						:		Dict=3  ,     SCREAMING_SNAKE_CASE						:		Optional[int]=4  ,     SCREAMING_SNAKE_CASE						:		Any=None  ,     )      ->						List[Any]:




											'''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 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						int:




											'''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 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Optional[int]:




											'''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 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		Dict  ,     SCREAMING_SNAKE_CASE						:		Dict  ,     SCREAMING_SNAKE_CASE						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		AlbertModel(config=SCREAMING_SNAKE_CASE    )
											model.to(SCREAMING_SNAKE_CASE    )
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE  ,     attention_mask=SCREAMING_SNAKE_CASE  ,     token_type_ids=SCREAMING_SNAKE_CASE    )
											__snake_case				=		model(SCREAMING_SNAKE_CASE  ,     token_type_ids=SCREAMING_SNAKE_CASE    )
											__snake_case				=		model(SCREAMING_SNAKE_CASE    )
											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 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Tuple    )      ->						Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		AlbertForPreTraining(config=SCREAMING_SNAKE_CASE    )
											model.to(SCREAMING_SNAKE_CASE    )
											model.eval()
											__snake_case				=		model(
											    SCREAMING_SNAKE_CASE  ,     attention_mask=SCREAMING_SNAKE_CASE  ,     token_type_ids=SCREAMING_SNAKE_CASE  ,     labels=SCREAMING_SNAKE_CASE  ,     sentence_order_label=SCREAMING_SNAKE_CASE  ,     )
											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 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Any  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Dict  ,     SCREAMING_SNAKE_CASE						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		Tuple    )      ->						Dict:




											'''simple docstring'''

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		Any  ,     SCREAMING_SNAKE_CASE						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Any  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		Any    )      ->						int:




											'''simple docstring'''

											__snake_case				=		AlbertForQuestionAnswering(config=SCREAMING_SNAKE_CASE    )
											model.to(SCREAMING_SNAKE_CASE    )
											model.eval()
											__snake_case				=		model(
											    SCREAMING_SNAKE_CASE  ,     attention_mask=SCREAMING_SNAKE_CASE  ,     token_type_ids=SCREAMING_SNAKE_CASE  ,     start_positions=SCREAMING_SNAKE_CASE  ,     end_positions=SCREAMING_SNAKE_CASE  ,     )
											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 				SCREAMING_SNAKE_CASE_	(    self						:		Any  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						Union[str, Any]:




											'''simple docstring'''

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		Any  ,     SCREAMING_SNAKE_CASE						:		Any    )      ->						Optional[Any]:




											'''simple docstring'''

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str  ,     SCREAMING_SNAKE_CASE						:		Dict  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		self.num_choices
											__snake_case				=		AlbertForMultipleChoice(config=SCREAMING_SNAKE_CASE    )
											model.to(SCREAMING_SNAKE_CASE    )
											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(
											    SCREAMING_SNAKE_CASE  ,     attention_mask=SCREAMING_SNAKE_CASE  ,     token_type_ids=SCREAMING_SNAKE_CASE  ,     labels=SCREAMING_SNAKE_CASE  ,     )
											self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_choices)    )






					def 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.prepare_config_and_inputs()
											(
											    (
											    __snake_case
											)		,       (
											    __snake_case
											)		,       (
											    __snake_case
											)		,       (
											    __snake_case
											)		,       (
											    __snake_case
											)		,       (
											    __snake_case
											)		,       (
											    __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(	_a  ,				_a  ,				unittest.TestCase ):
					snake_case_		:					Tuple    					=						(
					    (
					        AlbertModel,
					        AlbertForPreTraining,
					        AlbertForMaskedLM,
					        AlbertForMultipleChoice,
					        AlbertForSequenceClassification,
					        AlbertForTokenClassification,
					        AlbertForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					snake_case_		:					Dict    					=						(
					    {
					        """feature-extraction""": AlbertModel,
					        """fill-mask""": AlbertForMaskedLM,
					        """question-answering""": AlbertForQuestionAnswering,
					        """text-classification""": AlbertForSequenceClassification,
					        """token-classification""": AlbertForTokenClassification,
					        """zero-shot""": AlbertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					snake_case_		:					str    					=						True


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=False    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		super()._prepare_for_class(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     return_labels=SCREAMING_SNAKE_CASE    )

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		AlbertModelTester(self    )
											__snake_case				=		ConfigTester(self  ,     config_class=SCREAMING_SNAKE_CASE  ,     hidden_size=3_7    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						Tuple:




											'''simple docstring'''

											self.config_tester.run_common_tests()


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						int:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						int:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						str:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						List[Any]:




											'''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(*SCREAMING_SNAKE_CASE    )






					@slow
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Optional[int]:




											'''simple docstring'''

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






@require_torch
class      UpperCamelCase(	unittest.TestCase ):


					@slow
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						Any:




											'''simple docstring'''

											__snake_case				=		AlbertModel.from_pretrained("albert-base-v2"    )
											__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]]    )
											__snake_case				=		torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
											with torch.no_grad():
																	__snake_case				=		model(SCREAMING_SNAKE_CASE  ,     attention_mask=SCREAMING_SNAKE_CASE    )[0]
											__snake_case				=		torch.Size((1, 1_1, 7_6_8)    )
											self.assertEqual(output.shape  ,     SCREAMING_SNAKE_CASE    )
											__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]  ,     SCREAMING_SNAKE_CASE  ,     atol=1e-4    )    )









import inspect
import unittest






class      UpperCamelCase(	unittest.TestCase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						List[Any]:




											'''simple docstring'''

											try:
																	import diffusers  # noqa: F401
											except ImportError:
																	assert False






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Any:




											'''simple docstring'''

											import diffusers
											from diffusers.dependency_versions_table import deps

											__snake_case				=		inspect.getmembers(SCREAMING_SNAKE_CASE  ,     inspect.isclass    )

											for cls_name, cls_module in all_classes:
																	if "dummy_" in cls_module.__module__:
																							for backend in cls_module._backends:
																													if backend == "k_diffusion":
																																			__snake_case				=		"k-diffusion"
																													elif backend == "invisible_watermark":
																																			__snake_case				=		"invisible-watermark"
																													assert backend in deps, f'''{backend} is not in the deps table!'''





