"""simple docstring"""






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		LDMTextToImagePipeline
					a   	=		TEXT_TO_IMAGE_PARAMS - {
					    "negative_prompt",
					    "negative_prompt_embeds",
					    "cross_attention_kwargs",
					    "prompt_embeds",
					}
					a   	=		PipelineTesterMixin.required_optional_params - {
					    "num_images_per_prompt",
					    "callback",
					    "callback_steps",
					}
					a   	=		TEXT_TO_IMAGE_BATCH_PARAMS
					a   	=		False



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Optional[int]:
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__		  =				UNetaDConditionModel(
									    block_out_channels=(32, 64)   , layers_per_block=2   , sample_size=32   , in_channels=4   , out_channels=4   , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")   , cross_attention_dim=32   , )
									SCREAMING_SNAKE_CASE__		  =				DDIMScheduler(
									    beta_start=0.0_0085   , beta_end=0.012   , beta_schedule="scaled_linear"   , clip_sample=_snake_case   , set_alpha_to_one=_snake_case   , )
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__		  =				AutoencoderKL(
									    block_out_channels=(32, 64)   , in_channels=3   , out_channels=3   , down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D")   , up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D")   , latent_channels=4   , )
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__		  =				CLIPTextConfig(
									    bos_token_id=0   , eos_token_id=2   , hidden_size=32   , intermediate_size=37   , layer_norm_eps=1e-05   , num_attention_heads=4   , num_hidden_layers=5   , pad_token_id=1   , vocab_size=1000   , )
									SCREAMING_SNAKE_CASE__		  =				CLIPTextModel(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

									SCREAMING_SNAKE_CASE__		  =				{
									    "unet": unet,
									    "scheduler": scheduler,
									    "vqvae": vae,
									    "bert": text_encoder,
									    "tokenizer": tokenizer,
									}
									return components



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				List[str]   , _snake_case			:				List[Any]=0		)							->       str:
									if str(_snake_case		).startswith("mps"		):
													SCREAMING_SNAKE_CASE__		  =				torch.manual_seed(_snake_case		)
									else:
													SCREAMING_SNAKE_CASE__		  =				torch.Generator(device=_snake_case		).manual_seed(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				{
									    "prompt": "A painting of a squirrel eating a burger",
									    "generator": generator,
									    "num_inference_steps": 2,
									    "guidance_scale": 6.0,
									    "output_type": "numpy",
									}
									return inputs



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				"cpu"  # ensure determinism for the device-dependent torch.Generator

									SCREAMING_SNAKE_CASE__		  =				self.get_dummy_components()
									SCREAMING_SNAKE_CASE__		  =				LDMTextToImagePipeline(**_snake_case		)
									pipe.to(_snake_case		)
									pipe.set_progress_bar_config(disable=_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				self.get_dummy_inputs(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				pipe(**_snake_case		).images
									SCREAMING_SNAKE_CASE__		  =				image[0, -3:, -3:, -1]

									assert image.shape == (1, 16, 16, 3)
									SCREAMING_SNAKE_CASE__		  =				np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3



@slow
@require_torch_gpu
class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Dict:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				List[str]   , _snake_case			:				Tuple=torch.floataa   , _snake_case			:				str=0		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				torch.manual_seed(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				np.random.RandomState(_snake_case		).standard_normal((1, 4, 32, 32)		)
									SCREAMING_SNAKE_CASE__		  =				torch.from_numpy(_snake_case		).to(device=_snake_case   , dtype=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				{
									    "prompt": "A painting of a squirrel eating a burger",
									    "latents": latents,
									    "generator": generator,
									    "num_inference_steps": 3,
									    "guidance_scale": 6.0,
									    "output_type": "numpy",
									}
									return inputs



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256"		).to(_snake_case		)
									pipe.set_progress_bar_config(disable=_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				self.get_inputs(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				pipe(**_snake_case		).images
									SCREAMING_SNAKE_CASE__		  =				image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 256, 256, 3)
									SCREAMING_SNAKE_CASE__		  =				np.array([0.5_1825, 0.5_2850, 0.5_2543, 0.5_4258, 0.5_2304, 0.5_2569, 0.5_4363, 0.5_5276, 0.5_6878]		)
									SCREAMING_SNAKE_CASE__		  =				np.abs(expected_slice - image_slice		).max()
									assert max_diff < 1e-3



@nightly
@require_torch_gpu
class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Union[str, Any]:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				Dict   , _snake_case			:				List[str]=torch.floataa   , _snake_case			:				Tuple=0		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				torch.manual_seed(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				np.random.RandomState(_snake_case		).standard_normal((1, 4, 32, 32)		)
									SCREAMING_SNAKE_CASE__		  =				torch.from_numpy(_snake_case		).to(device=_snake_case   , dtype=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				{
									    "prompt": "A painting of a squirrel eating a burger",
									    "latents": latents,
									    "generator": generator,
									    "num_inference_steps": 50,
									    "guidance_scale": 6.0,
									    "output_type": "numpy",
									}
									return inputs



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256"		).to(_snake_case		)
									pipe.set_progress_bar_config(disable=_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				self.get_inputs(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				pipe(**_snake_case		).images[0]

									SCREAMING_SNAKE_CASE__		  =				load_numpy(
									    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy"		)
									SCREAMING_SNAKE_CASE__		  =				np.abs(expected_image - image		).max()
									assert max_diff < 1e-3





"""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			:				str   , _snake_case			:				List[str]   , _snake_case			:				List[Any]=13   , _snake_case			:				List[str]=7   , _snake_case			:				Dict=True   , _snake_case			:				str=True   , _snake_case			:				Optional[Any]=True   , _snake_case			:				Tuple=True   , _snake_case			:				List[Any]=99   , _snake_case			:				Dict=16   , _snake_case			:				Tuple=36   , _snake_case			:				Optional[int]=6   , _snake_case			:				Optional[int]=6   , _snake_case			:				Tuple=6   , _snake_case			:				Optional[int]=37   , _snake_case			:				Dict="gelu"   , _snake_case			:				str=0.1   , _snake_case			:				Tuple=0.1   , _snake_case			:				List[str]=512   , _snake_case			:				Any=16   , _snake_case			:				Optional[int]=2   , _snake_case			:				Optional[int]=0.02   , _snake_case			:				Union[str, Any]=3   , _snake_case			:				int=4   , _snake_case			:				Optional[int]=None   , )							->       List[str]:
									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			:				Tuple		)							->       Tuple:
									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			:				Dict		)							->       Union[str, Any]:
									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			:				int   , _snake_case			:				Tuple   , _snake_case			:				Optional[Any]   , _snake_case			:				Union[str, Any]   , _snake_case			:				int   , _snake_case			:				Union[str, Any]   , _snake_case			:				str   , _snake_case			:				int		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				AlbertModel(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , token_type_ids=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_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 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Optional[Any]   , _snake_case			:				int   , _snake_case			:				str   , _snake_case			:				List[Any]   , _snake_case			:				Dict   , _snake_case			:				Union[str, Any]   , _snake_case			:				str		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				AlbertForPreTraining(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case   , sentence_order_label=_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 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				int   , _snake_case			:				Any   , _snake_case			:				List[Any]   , _snake_case			:				Any   , _snake_case			:				str   , _snake_case			:				Tuple   , _snake_case			:				List[Any]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				AlbertForMaskedLM(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Any   , _snake_case			:				Optional[int]   , _snake_case			:				Optional[Any]   , _snake_case			:				Tuple		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				AlbertForQuestionAnswering(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , start_positions=_snake_case   , end_positions=_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 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				str   , _snake_case			:				Optional[int]   , _snake_case			:				int   , _snake_case			:				List[Any]   , _snake_case			:				List[Any]   , _snake_case			:				str		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				AlbertForSequenceClassification(_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				Any   , _snake_case			:				str   , _snake_case			:				List[Any]   , _snake_case			:				Any   , _snake_case			:				Union[str, Any]   , _snake_case			:				Dict   , _snake_case			:				Tuple		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				AlbertForTokenClassification(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				str   , _snake_case			:				Any   , _snake_case			:				Any   , _snake_case			:				int   , _snake_case			:				List[Any]   , _snake_case			:				List[str]   , _snake_case			:				int		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.num_choices
									SCREAMING_SNAKE_CASE__		  =				AlbertForMultipleChoice(config=_snake_case		)
									model.to(_snake_case		)
									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(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case   , )
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_choices)		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Optional[int]:
									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 lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    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			:				Union[str, Any]   , _snake_case			:				Dict   , _snake_case			:				str   , _snake_case			:				str=False		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				super()._prepare_for_class(_snake_case   , _snake_case   , return_labels=_snake_case		)

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



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				AlbertModelTester(self		)
									SCREAMING_SNAKE_CASE__		  =				ConfigTester(self   , config_class=_snake_case   , hidden_size=37		)



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       List[str]:
									self.config_tester.run_common_tests()



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



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



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



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



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Optional[int]:
									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(*_snake_case		)



					@slow
					def 			lowerCAmelCase_					(						self			:				Any		)							->       str:
									for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE__		  =				AlbertModel.from_pretrained(_snake_case		)
													self.assertIsNotNone(_snake_case		)



@require_torch
class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					@slow
					def 			lowerCAmelCase_					(						self			:				Any		)							->       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(_snake_case   , attention_mask=_snake_case		)[0]
									SCREAMING_SNAKE_CASE__		  =				torch.Size((1, 11, 768)		)
									self.assertEqual(output.shape   , _snake_case		)
									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]   , _snake_case   , atol=1e-4		)		)




