






"""simple docstring"""
import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
			import torch

			from transformers import (
			    DebertaVaForMaskedLM,
			    DebertaVaForMultipleChoice,
			    DebertaVaForQuestionAnswering,
			    DebertaVaForSequenceClassification,
			    DebertaVaForTokenClassification,
			    DebertaVaModel,
			)
			from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST







class      UpperCamelCase_			(__A	):





	def __init__( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					List[str]=13      ,      lowerCAmelCase_   :					int=7      ,      lowerCAmelCase_   :					str=True      ,      lowerCAmelCase_   :					List[Any]=True      ,      lowerCAmelCase_   :					List[str]=True      ,      lowerCAmelCase_   :					Union[str, Any]=True      ,      lowerCAmelCase_   :					Union[str, Any]=99      ,      lowerCAmelCase_   :					str=32      ,      lowerCAmelCase_   :					Any=5      ,      lowerCAmelCase_   :					Dict=4      ,      lowerCAmelCase_   :					Union[str, Any]=37      ,      lowerCAmelCase_   :					Union[str, Any]="gelu"      ,      lowerCAmelCase_   :					List[Any]=0.1      ,      lowerCAmelCase_   :					str=0.1      ,      lowerCAmelCase_   :					Union[str, Any]=512      ,      lowerCAmelCase_   :					Dict=16      ,      lowerCAmelCase_   :					Optional[int]=2      ,      lowerCAmelCase_   :					int=0.0_2      ,      lowerCAmelCase_   :					Optional[int]=False      ,      lowerCAmelCase_   :					Union[str, Any]=True      ,      lowerCAmelCase_   :					Union[str, Any]="None"      ,      lowerCAmelCase_   :					Union[str, Any]=3      ,      lowerCAmelCase_   :					Optional[int]=4      ,      lowerCAmelCase_   :					Any=None      ,      )		->     List[str]:
				UpperCAmelCase_	:					List[Any]     	=   parent
				UpperCAmelCase_	:					int     	=   batch_size
				UpperCAmelCase_	:					Optional[Any]     	=   seq_length
				UpperCAmelCase_	:					Tuple     	=   is_training
				UpperCAmelCase_	:					str     	=   use_input_mask
				UpperCAmelCase_	:					str     	=   use_token_type_ids
				UpperCAmelCase_	:					Any     	=   use_labels
				UpperCAmelCase_	:					Optional[int]     	=   vocab_size
				UpperCAmelCase_	:					Optional[Any]     	=   hidden_size
				UpperCAmelCase_	:					int     	=   num_hidden_layers
				UpperCAmelCase_	:					Any     	=   num_attention_heads
				UpperCAmelCase_	:					List[Any]     	=   intermediate_size
				UpperCAmelCase_	:					int     	=   hidden_act
				UpperCAmelCase_	:					Union[str, Any]     	=   hidden_dropout_prob
				UpperCAmelCase_	:					List[str]     	=   attention_probs_dropout_prob
				UpperCAmelCase_	:					Dict     	=   max_position_embeddings
				UpperCAmelCase_	:					Dict     	=   type_vocab_size
				UpperCAmelCase_	:					Union[str, Any]     	=   type_sequence_label_size
				UpperCAmelCase_	:					Tuple     	=   initializer_range
				UpperCAmelCase_	:					Dict     	=   num_labels
				UpperCAmelCase_	:					List[str]     	=   num_choices
				UpperCAmelCase_	:					Any     	=   relative_attention
				UpperCAmelCase_	:					List[Any]     	=   position_biased_input
				UpperCAmelCase_	:					int     	=   pos_att_type
				UpperCAmelCase_	:					List[Any]     	=   scope





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Dict:
				UpperCAmelCase_	:					Optional[Any]     	=   ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size		)

				UpperCAmelCase_	:					Any     	=   None
				if self.use_input_mask:
							UpperCAmelCase_	:					List[str]     	=   ids_tensor([self.batch_size, self.seq_length]      ,      vocab_size=2		)

				UpperCAmelCase_	:					Union[str, Any]     	=   None
				if self.use_token_type_ids:
							UpperCAmelCase_	:					Any     	=   ids_tensor([self.batch_size, self.seq_length]      ,      self.type_vocab_size		)

				UpperCAmelCase_	:					List[Any]     	=   None
				UpperCAmelCase_	:					Union[str, Any]     	=   None
				UpperCAmelCase_	:					List[str]     	=   None
				if self.use_labels:
							UpperCAmelCase_	:					List[Any]     	=   ids_tensor([self.batch_size]      ,      self.type_sequence_label_size		)
							UpperCAmelCase_	:					Tuple     	=   ids_tensor([self.batch_size, self.seq_length]      ,      self.num_labels		)
							UpperCAmelCase_	:					List[str]     	=   ids_tensor([self.batch_size]      ,      self.num_choices		)

				UpperCAmelCase_	:					Tuple     	=   self.get_config()

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





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     List[Any]:
				return DebertaVaConfig(
				    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      ,      relative_attention=self.relative_attention      ,      position_biased_input=self.position_biased_input      ,      pos_att_type=self.pos_att_type      ,      )





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Union[str, Any]		)		->     int:
				self.parent.assertListEqual(list(result.loss.size()		)      ,      []		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					Dict		)		->     List[Any]:
				UpperCAmelCase_	:					Optional[int]     	=   DebertaVaModel(config=lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_	:					Dict     	=   model(lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_      ,      token_type_ids=lowerCAmelCase_		)[0]
				UpperCAmelCase_	:					List[Any]     	=   model(lowerCAmelCase_      ,      token_type_ids=lowerCAmelCase_		)[0]
				UpperCAmelCase_	:					Dict     	=   model(lowerCAmelCase_		)[0]

				self.parent.assertListEqual(list(sequence_output.size()		)      ,      [self.batch_size, self.seq_length, self.hidden_size]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					str		)		->     Any:
				UpperCAmelCase_	:					Any     	=   DebertaVaForMaskedLM(config=lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_	:					Tuple     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					int		)		->     List[str]:
				UpperCAmelCase_	:					Optional[int]     	=   self.num_labels
				UpperCAmelCase_	:					Optional[Any]     	=   DebertaVaForSequenceClassification(lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_	:					str     	=   model(lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_      ,      token_type_ids=lowerCAmelCase_      ,      labels=lowerCAmelCase_		)
				self.parent.assertListEqual(list(result.logits.size()		)      ,      [self.batch_size, self.num_labels]		)
				self.check_loss_output(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					Dict      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Dict		)		->     Union[str, Any]:
				UpperCAmelCase_	:					str     	=   self.num_labels
				UpperCAmelCase_	:					List[Any]     	=   DebertaVaForTokenClassification(config=lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_	:					int     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					List[Any]		)		->     Tuple:
				UpperCAmelCase_	:					List[str]     	=   DebertaVaForQuestionAnswering(config=lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_	:					List[str]     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					str		)		->     str:
				UpperCAmelCase_	:					Optional[Any]     	=   DebertaVaForMultipleChoice(config=lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_	:					str     	=   input_ids.unsqueeze(1		).expand(-1      ,      self.num_choices      ,      -1		).contiguous()
				UpperCAmelCase_	:					Union[str, Any]     	=   token_type_ids.unsqueeze(1		).expand(-1      ,      self.num_choices      ,      -1		).contiguous()
				UpperCAmelCase_	:					Optional[Any]     	=   input_mask.unsqueeze(1		).expand(-1      ,      self.num_choices      ,      -1		).contiguous()
				UpperCAmelCase_	:					Tuple     	=   model(
				    lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_      ,      token_type_ids=lowerCAmelCase_      ,      labels=lowerCAmelCase_      ,      )
				self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_choices)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Optional[Any]:
				UpperCAmelCase_	:					List[Any]     	=   self.prepare_config_and_inputs()
				(
				    (
				    UpperCAmelCase_
				)	,      (
				    UpperCAmelCase_
				)	,      (
				    UpperCAmelCase_
				)	,      (
				    UpperCAmelCase_
				)	,      (
				    UpperCAmelCase_
				)	,      (
				    UpperCAmelCase_
				)	,      (
				    UpperCAmelCase_
				)	,      
				)	:					Optional[int]     	=   config_and_inputs
				UpperCAmelCase_	:					List[Any]     	=   {"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	):
	__magic_name__       =							(
	    (
	        DebertaVaModel,
	        DebertaVaForMaskedLM,
	        DebertaVaForSequenceClassification,
	        DebertaVaForTokenClassification,
	        DebertaVaForQuestionAnswering,
	        DebertaVaForMultipleChoice,
	    )
	    if is_torch_available()
	    else ()
	)
	__magic_name__       =							(
	    {
	        '''feature-extraction''': DebertaVaModel,
	        '''fill-mask''': DebertaVaForMaskedLM,
	        '''question-answering''': DebertaVaForQuestionAnswering,
	        '''text-classification''': DebertaVaForSequenceClassification,
	        '''token-classification''': DebertaVaForTokenClassification,
	        '''zero-shot''': DebertaVaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)

	__magic_name__       =							True
	__magic_name__       =							False
	__magic_name__       =							False
	__magic_name__       =							False
	__magic_name__       =							False





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Union[str, Any]     	=   DebertaVaModelTester(self		)
				UpperCAmelCase_	:					Dict     	=   ConfigTester(self      ,      config_class=lowerCAmelCase_      ,      hidden_size=37		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Dict:
				self.config_tester.run_common_tests()





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     str:
				UpperCAmelCase_	:					Tuple     	=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_model(*lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Tuple:
				UpperCAmelCase_	:					Dict     	=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					List[Any]     	=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     Any:
				UpperCAmelCase_	:					List[Any]     	=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_question_answering(*lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     int:
				UpperCAmelCase_	:					str     	=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_token_classification(*lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     List[str]:
				UpperCAmelCase_	:					List[str]     	=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_multiple_choice(*lowerCAmelCase_		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     List[str]:
				for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_	:					Tuple     	=   DebertaVaModel.from_pretrained(lowerCAmelCase_		)
							self.assertIsNotNone(lowerCAmelCase_		)







@require_torch
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_			(unittest.TestCase	):





	@unittest.skip(reason="Model not available yet"		)
	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     int:
				pass





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Dict:
				UpperCAmelCase_	:					Dict     	=   DebertaVaModel.from_pretrained("microsoft/deberta-v2-xlarge"		)

				UpperCAmelCase_	:					Dict     	=   torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]		)
				UpperCAmelCase_	:					Tuple     	=   torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
				with torch.no_grad():
							UpperCAmelCase_	:					List[Any]     	=   model(lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_		)[0]
				# compare the actual values for a slice.
				UpperCAmelCase_	:					str     	=   torch.tensor(
				    [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]		)
				self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,      lowerCAmelCase_      ,      atol=1e-4		)      ,      f"""{output[:, 1:4, 1:4]}"""		)











"""simple docstring"""





import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class       __A		(   SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):
					UpperCAmelCase__								=				StableDiffusionInstructPixaPixPipeline
					UpperCAmelCase__								=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
					UpperCAmelCase__								=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					UpperCAmelCase__								=				IMAGE_TO_IMAGE_IMAGE_PARAMS
					UpperCAmelCase__								=				IMAGE_TO_IMAGE_IMAGE_PARAMS

					def   lowerCamelCase__   (			self  :      str					)			->							Optional[int]:
											torch.manual_seed(0					)
											__magic_name__:	str							=       UNetaDConditionModel(
											    block_out_channels=(3_2, 6_4)     ,		layers_per_block=2     ,		sample_size=3_2     ,		in_channels=8     ,		out_channels=4     ,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")     ,		up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")     ,		cross_attention_dim=3_2     ,		)
											__magic_name__:	Union[str, Any]							=       PNDMScheduler(skip_prk_steps=__snake_case					)
											torch.manual_seed(0					)
											__magic_name__:	Union[str, Any]							=       AutoencoderKL(
											    block_out_channels=[3_2, 6_4]     ,		in_channels=3     ,		out_channels=3     ,		down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]     ,		up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]     ,		latent_channels=4     ,		)
											torch.manual_seed(0					)
											__magic_name__:	int							=       CLIPTextConfig(
											    bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=3_2     ,		intermediate_size=3_7     ,		layer_norm_eps=1E-05     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=1_0_0_0     ,		)
											__magic_name__:	Optional[int]							=       CLIPTextModel(__snake_case					)
											__magic_name__:	Union[str, Any]							=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

											__magic_name__:	Tuple							=       {
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """vae""": vae,
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											    """safety_checker""": None,
											    """feature_extractor""": None,
											}
											return components

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      int     ,		__snake_case  :      List[Any]=0					)			->							Optional[Any]:
											__magic_name__:	Optional[int]							=       floats_tensor((1, 3, 3_2, 3_2)     ,		rng=random.Random(__snake_case					)					).to(__snake_case					)
											__magic_name__:	str							=       image.cpu().permute(0     ,		2     ,		3     ,		1					)[0]
											__magic_name__:	Any							=       Image.fromarray(np.uinta(__snake_case					)					).convert("""RGB"""					)
											if str(__snake_case					).startswith("""mps"""					):
																	__magic_name__:	Optional[Any]							=       torch.manual_seed(__snake_case					)
											else:
																	__magic_name__:	str							=       torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
											__magic_name__:	Optional[int]							=       {
											    """prompt""": """A painting of a squirrel eating a burger""",
											    """image""": image,
											    """generator""": generator,
											    """num_inference_steps""": 2,
											    """guidance_scale""": 6.0,
											    """image_guidance_scale""": 1,
											    """output_type""": """numpy""",
											}
											return inputs

					def   lowerCamelCase__   (			self  :      Any					)			->							Tuple:
											__magic_name__:	int							=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
											__magic_name__:	List[Any]							=       self.get_dummy_components()
											__magic_name__:	int							=       StableDiffusionInstructPixaPixPipeline(**__snake_case					)
											__magic_name__:	Union[str, Any]							=       sd_pipe.to(__snake_case					)
											sd_pipe.set_progress_bar_config(disable=__snake_case					)

											__magic_name__:	List[Any]							=       self.get_dummy_inputs(__snake_case					)
											__magic_name__:	Tuple							=       sd_pipe(**__snake_case					).images
											__magic_name__:	Optional[int]							=       image[0, -3:, -3:, -1]
											assert image.shape == (1, 3_2, 3_2, 3)
											__magic_name__:	List[Any]							=       np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]					)

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

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							List[str]:
											__magic_name__:	Any							=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
											__magic_name__:	str							=       self.get_dummy_components()
											__magic_name__:	Dict							=       StableDiffusionInstructPixaPixPipeline(**__snake_case					)
											__magic_name__:	str							=       sd_pipe.to(__snake_case					)
											sd_pipe.set_progress_bar_config(disable=__snake_case					)

											__magic_name__:	Optional[Any]							=       self.get_dummy_inputs(__snake_case					)
											__magic_name__:	List[Any]							=       """french fries"""
											__magic_name__:	int							=       sd_pipe(**__snake_case     ,		negative_prompt=__snake_case					)
											__magic_name__:	Dict							=       output.images
											__magic_name__:	Any							=       image[0, -3:, -3:, -1]

											assert image.shape == (1, 3_2, 3_2, 3)
											__magic_name__:	str							=       np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]					)

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

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Tuple:
											__magic_name__:	Union[str, Any]							=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
											__magic_name__:	int							=       self.get_dummy_components()
											__magic_name__:	Dict							=       StableDiffusionInstructPixaPixPipeline(**__snake_case					)
											__magic_name__:	Dict							=       sd_pipe.to(__snake_case					)
											sd_pipe.set_progress_bar_config(disable=__snake_case					)

											__magic_name__:	str							=       self.get_dummy_inputs(__snake_case					)
											__magic_name__:	List[str]							=       [inputs["""prompt"""]] * 2

											__magic_name__:	List[str]							=       np.array(inputs["""image"""]					).astype(np.floataa					) / 255.0
											__magic_name__:	Optional[int]							=       torch.from_numpy(__snake_case					).unsqueeze(0					).to(__snake_case					)
											__magic_name__:	Tuple							=       image / 2 + 0.5
											__magic_name__:	Dict							=       image.permute(0     ,		3     ,		1     ,		2					)
											__magic_name__:	List[str]							=       image.repeat(2     ,		1     ,		1     ,		1					)

											__magic_name__:	str							=       sd_pipe(**__snake_case					).images
											__magic_name__:	List[str]							=       image[-1, -3:, -3:, -1]

											assert image.shape == (2, 3_2, 3_2, 3)
											__magic_name__:	Optional[int]							=       np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]					)

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

					def   lowerCamelCase__   (			self  :      Tuple					)			->							Optional[int]:
											__magic_name__:	Union[str, Any]							=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
											__magic_name__:	Union[str, Any]							=       self.get_dummy_components()
											__magic_name__:	Union[str, Any]							=       EulerAncestralDiscreteScheduler(
											    beta_start=0.00085     ,		beta_end=0.012     ,		beta_schedule="""scaled_linear"""					)
											__magic_name__:	Union[str, Any]							=       StableDiffusionInstructPixaPixPipeline(**__snake_case					)
											__magic_name__:	Optional[int]							=       sd_pipe.to(__snake_case					)
											sd_pipe.set_progress_bar_config(disable=__snake_case					)

											__magic_name__:	List[str]							=       self.get_dummy_inputs(__snake_case					)
											__magic_name__:	Tuple							=       sd_pipe(**__snake_case					).images
											__magic_name__:	Any							=       image[0, -3:, -3:, -1]

											__magic_name__:	str							=       [round(__snake_case     ,		4					) for x in image_slice.flatten().tolist()]
											print(""",""".join([str(__snake_case					) for x in slice]					)					)

											assert image.shape == (1, 3_2, 3_2, 3)
											__magic_name__:	Optional[Any]							=       np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]					)

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

					def   lowerCamelCase__   (			self  :      Dict					)			->							Optional[int]:
											super().test_inference_batch_single_identical(expected_max_diff=3E-3					)






					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							List[str]:
											__magic_name__:	Tuple							=       self.get_dummy_components()
											__magic_name__:	Tuple							=       StableDiffusionInstructPixaPixPipeline(**__snake_case					)
											__magic_name__:	str							=       VaeImageProcessor(do_resize=__snake_case     ,		do_normalize=__snake_case					)
											__magic_name__:	Union[str, Any]							=       pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)

											__magic_name__:	Union[str, Any]							=       pipe(**self.get_dummy_inputs_by_type(__snake_case     ,		input_image_type="""pt"""					)					)[0]

											__magic_name__:	Union[str, Any]							=       components["""vae"""]
											__magic_name__:	str							=       self.get_dummy_inputs_by_type(__snake_case     ,		input_image_type="""pt"""					)

											for image_param in self.image_latents_params:
																	if image_param in inputs.keys():
																							__magic_name__:	int							=       vae.encode(inputs[image_param]					).latent_dist.mode()

											__magic_name__:	Dict							=       pipe(**__snake_case					)[0]

											__magic_name__:	Optional[Any]							=       np.abs(out - out_latents_inputs					).max()
											self.assertLess(__snake_case     ,		1E-4     ,		"""passing latents as image input generate different result from passing image"""					)



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

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							Any:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      List[str]=0					)			->							Dict:
											__magic_name__:	Union[str, Any]							=       torch.manual_seed(__snake_case					)
											__magic_name__:	Optional[int]							=       load_image(
											    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""					)
											__magic_name__:	Optional[Any]							=       {
											    """prompt""": """turn him into a cyborg""",
											    """image""": image,
											    """generator""": generator,
											    """num_inference_steps""": 3,
											    """guidance_scale""": 7.5,
											    """image_guidance_scale""": 1.0,
											    """output_type""": """numpy""",
											}
											return inputs

					def   lowerCamelCase__   (			self  :      Any					)			->							Any:
											__magic_name__:	str							=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    """timbrooks/instruct-pix2pix"""     ,		safety_checker=__snake_case					)
											pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)
											pipe.enable_attention_slicing()

											__magic_name__:	Optional[Any]							=       self.get_inputs()
											__magic_name__:	str							=       pipe(**__snake_case					).images
											__magic_name__:	Optional[int]							=       image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__magic_name__:	Any							=       np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]					)

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

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							int:
											__magic_name__:	Tuple							=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    """timbrooks/instruct-pix2pix"""     ,		safety_checker=__snake_case					)
											__magic_name__:	List[str]							=       LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
											pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)
											pipe.enable_attention_slicing()

											__magic_name__:	List[str]							=       self.get_inputs()
											__magic_name__:	Dict							=       pipe(**__snake_case					).images
											__magic_name__:	str							=       image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__magic_name__:	Optional[Any]							=       np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]					)

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

					def   lowerCamelCase__   (			self  :      Any					)			->							List[str]:
											__magic_name__:	str							=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    """timbrooks/instruct-pix2pix"""     ,		safety_checker=__snake_case					)
											__magic_name__:	int							=       DDIMScheduler.from_config(pipe.scheduler.config					)
											pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)
											pipe.enable_attention_slicing()

											__magic_name__:	Union[str, Any]							=       self.get_inputs()
											__magic_name__:	Any							=       pipe(**__snake_case					).images
											__magic_name__:	int							=       image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__magic_name__:	Optional[Any]							=       np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]					)

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

					def   lowerCamelCase__   (			self  :      int					)			->							Dict:
											__magic_name__:	Tuple							=       0

											def callback_fn(__snake_case  :      int     ,		__snake_case  :      int     ,		__snake_case  :      torch.FloatTensor					) -> None:
																	__magic_name__:	Union[str, Any]							=       True
																	nonlocal number_of_steps
																	number_of_steps += 1
																	if step == 1:
																							__magic_name__:	List[str]							=       latents.detach().cpu().numpy()
																							assert latents.shape == (1, 4, 6_4, 6_4)
																							__magic_name__:	int							=       latents[0, -3:, -3:, -1]
																							__magic_name__:	Union[str, Any]							=       np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]					)

																							assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5E-2
																	elif step == 2:
																							__magic_name__:	Union[str, Any]							=       latents.detach().cpu().numpy()
																							assert latents.shape == (1, 4, 6_4, 6_4)
																							__magic_name__:	str							=       latents[0, -3:, -3:, -1]
																							__magic_name__:	Optional[Any]							=       np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]					)

																							assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5E-2

											__magic_name__:	Tuple							=       False

											__magic_name__:	List[Any]							=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    """timbrooks/instruct-pix2pix"""     ,		safety_checker=__snake_case     ,		torch_dtype=torch.floataa					)
											__magic_name__:	Union[str, Any]							=       pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)
											pipe.enable_attention_slicing()

											__magic_name__:	Dict							=       self.get_inputs()
											pipe(**__snake_case     ,		callback=__snake_case     ,		callback_steps=1					)
											assert callback_fn.has_been_called
											assert number_of_steps == 3

					def   lowerCamelCase__   (			self  :      Tuple					)			->							int:
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											__magic_name__:	List[Any]							=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    """timbrooks/instruct-pix2pix"""     ,		safety_checker=__snake_case     ,		torch_dtype=torch.floataa					)
											__magic_name__:	int							=       pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)
											pipe.enable_attention_slicing(1					)
											pipe.enable_sequential_cpu_offload()

											__magic_name__:	Optional[int]							=       self.get_inputs()
											__magic_name__:	Any							=       pipe(**__snake_case					)

											__magic_name__:	List[str]							=       torch.cuda.max_memory_allocated()
											# make sure that less than 2.2 GB is allocated
											assert mem_bytes < 2.2 * 1_0**9






					def   lowerCamelCase__   (			self  :      str					)			->							Optional[int]:
											__magic_name__:	Optional[int]							=       self.get_inputs()
											# resize to resolution that is divisible by 8 but not 16 or 32
											__magic_name__:	Any							=       inputs["""image"""].resize((5_0_4, 5_0_4)					)

											__magic_name__:	List[str]							=       """timbrooks/instruct-pix2pix"""
											__magic_name__:	Union[str, Any]							=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    __snake_case     ,		safety_checker=__snake_case     ,		)
											pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)
											pipe.enable_attention_slicing()

											__magic_name__:	str							=       pipe(**__snake_case					)
											__magic_name__:	Optional[int]							=       output.images[0]

											__magic_name__:	Union[str, Any]							=       image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

											assert image.shape == (5_0_4, 5_0_4, 3)
											__magic_name__:	Optional[Any]							=       np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]					)

											assert np.abs(image_slice.flatten() - expected_slice					).max() < 5E-3



