

import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
	    FlaubertForMultipleChoice,
	    FlaubertForQuestionAnswering,
	    FlaubertForQuestionAnsweringSimple,
	    FlaubertForSequenceClassification,
	    FlaubertForTokenClassification,
	    FlaubertModel,
	    FlaubertWithLMHeadModel,
	)
	from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST



class    _a						(__magic_name__    ):


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__=13   ,	A__=7   ,	A__=True   ,	A__=True   ,	A__=True   ,	A__=True   ,	A__=True   ,	A__=False   ,	A__=False   ,	A__=False   ,	A__=2   ,	A__=99   ,	A__=0   ,	A__=32   ,	A__=5   ,	A__=4   ,	A__=0.1   ,	A__=0.1   ,	A__=512   ,	A__=12   ,	A__=2   ,	A__=0.0_2   ,	A__=3   ,	A__=4   ,	A__="last"   ,	A__=None   ,	A__=None   ,	):
						A__							:   List[str]					       =		parent
						A__							:   int					       =		batch_size
						A__							:   Optional[int]					       =		seq_length
						A__							:   int					       =		is_training
						A__							:   Union[str, Any]					       =		use_input_lengths
						A__							:   Dict					       =		use_token_type_ids
						A__							:   Dict					       =		use_labels
						A__							:   Any					       =		gelu_activation
						A__							:   Union[str, Any]					       =		sinusoidal_embeddings
						A__							:   Dict					       =		causal
						A__							:   str					       =		asm
						A__							:   List[Any]					       =		n_langs
						A__							:   Any					       =		vocab_size
						A__							:   Dict					       =		n_special
						A__							:   Union[str, Any]					       =		hidden_size
						A__							:   Dict					       =		num_hidden_layers
						A__							:   str					       =		num_attention_heads
						A__							:   Union[str, Any]					       =		hidden_dropout_prob
						A__							:   List[Any]					       =		attention_probs_dropout_prob
						A__							:   Optional[Any]					       =		max_position_embeddings
						A__							:   Union[str, Any]					       =		type_vocab_size
						A__							:   Optional[int]					       =		type_sequence_label_size
						A__							:   Union[str, Any]					       =		initializer_range
						A__							:   Union[str, Any]					       =		num_labels
						A__							:   Tuple					       =		num_choices
						A__							:   Optional[Any]					       =		summary_type
						A__							:   List[Any]					       =		use_proj
						A__							:   Any					       =		scope






					def   __A      (			self      ):
						A__							:   str					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )
						A__							:   Dict					       =		random_attention_mask([self.batch_size, self.seq_length]      )

						A__							:   Optional[Any]					       =		None
						if self.use_input_lengths:
							A__							:   Union[str, Any]					       =		(
							    ids_tensor([self.batch_size]   ,	vocab_size=2      ) + self.seq_length - 2
							)  # small variation of seq_length

						A__							:   int					       =		None
						if self.use_token_type_ids:
							A__							:   List[str]					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.n_langs      )

						A__							:   Dict					       =		None
						A__							:   Dict					       =		None
						A__							:   Dict					       =		None
						if self.use_labels:
							A__							:   List[str]					       =		ids_tensor([self.batch_size]   ,	self.type_sequence_label_size      )
							A__							:   Tuple					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.num_labels      )
							A__							:   Union[str, Any]					       =		ids_tensor([self.batch_size]   ,	2      ).float()
							A__							:   List[Any]					       =		ids_tensor([self.batch_size]   ,	self.num_choices      )

						A__							:   Union[str, Any]					       =		self.get_config()

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_lengths,
						    sequence_labels,
						    token_labels,
						    is_impossible_labels,
						    choice_labels,
						    input_mask,
						)






					def   __A      (			self      ):
						return FlaubertConfig(
						    vocab_size=self.vocab_size   ,	n_special=self.n_special   ,	emb_dim=self.hidden_size   ,	n_layers=self.num_hidden_layers   ,	n_heads=self.num_attention_heads   ,	dropout=self.hidden_dropout_prob   ,	attention_dropout=self.attention_probs_dropout_prob   ,	gelu_activation=self.gelu_activation   ,	sinusoidal_embeddings=self.sinusoidal_embeddings   ,	asm=self.asm   ,	causal=self.causal   ,	n_langs=self.n_langs   ,	max_position_embeddings=self.max_position_embeddings   ,	initializer_range=self.initializer_range   ,	summary_type=self.summary_type   ,	use_proj=self.use_proj   ,	)






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   List[Any]					       =		FlaubertModel(config=A__      )
						model.to(A__      )
						model.eval()
						A__							:   Tuple					       =		model(A__   ,	lengths=A__   ,	langs=A__      )
						A__							:   str					       =		model(A__   ,	langs=A__      )
						A__							:   Tuple					       =		model(A__      )
						self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   Any					       =		FlaubertWithLMHeadModel(A__      )
						model.to(A__      )
						model.eval()

						A__							:   Union[str, Any]					       =		model(A__   ,	token_type_ids=A__   ,	labels=A__      )
						self.parent.assertEqual(result.loss.shape   ,	()      )
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   Union[str, Any]					       =		FlaubertForQuestionAnsweringSimple(A__      )
						model.to(A__      )
						model.eval()

						A__							:   int					       =		model(A__      )

						A__							:   List[Any]					       =		model(A__   ,	start_positions=A__   ,	end_positions=A__      )
						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   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   List[str]					       =		FlaubertForQuestionAnswering(A__      )
						model.to(A__      )
						model.eval()

						A__							:   str					       =		model(A__      )

						A__							:   int					       =		model(
						    A__   ,	start_positions=A__   ,	end_positions=A__   ,	cls_index=A__   ,	is_impossible=A__   ,	p_mask=A__   ,	)

						A__							:   Optional[int]					       =		model(
						    A__   ,	start_positions=A__   ,	end_positions=A__   ,	cls_index=A__   ,	is_impossible=A__   ,	)

						(A__       )							:   int					       =		result_with_labels.to_tuple()

						A__							:   Union[str, Any]					       =		model(A__   ,	start_positions=A__   ,	end_positions=A__      )

						(A__       )							:   List[str]					       =		result_with_labels.to_tuple()

						self.parent.assertEqual(result_with_labels.loss.shape   ,	()      )
						self.parent.assertEqual(result.start_top_log_probs.shape   ,	(self.batch_size, model.config.start_n_top)      )
						self.parent.assertEqual(result.start_top_index.shape   ,	(self.batch_size, model.config.start_n_top)      )
						self.parent.assertEqual(
						    result.end_top_log_probs.shape   ,	(self.batch_size, model.config.start_n_top * model.config.end_n_top)      )
						self.parent.assertEqual(
						    result.end_top_index.shape   ,	(self.batch_size, model.config.start_n_top * model.config.end_n_top)      )
						self.parent.assertEqual(result.cls_logits.shape   ,	(self.batch_size,)      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   Any					       =		FlaubertForSequenceClassification(A__      )
						model.to(A__      )
						model.eval()

						A__							:   Optional[int]					       =		model(A__      )
						A__							:   Tuple					       =		model(A__   ,	labels=A__      )

						self.parent.assertEqual(result.loss.shape   ,	()      )
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.type_sequence_label_size)      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   str					       =		self.num_labels
						A__							:   Any					       =		FlaubertForTokenClassification(A__      )
						model.to(A__      )
						model.eval()

						A__							:   Dict					       =		model(A__   ,	attention_mask=A__   ,	labels=A__      )
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.num_labels)      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   Optional[int]					       =		self.num_choices
						A__							:   List[Any]					       =		FlaubertForMultipleChoice(config=A__      )
						model.to(A__      )
						model.eval()
						A__							:   Optional[int]					       =		input_ids.unsqueeze(1      ).expand(-1   ,	self.num_choices   ,	-1      ).contiguous()
						A__							:   List[Any]					       =		token_type_ids.unsqueeze(1      ).expand(-1   ,	self.num_choices   ,	-1      ).contiguous()
						A__							:   Union[str, Any]					       =		input_mask.unsqueeze(1      ).expand(-1   ,	self.num_choices   ,	-1      ).contiguous()
						A__							:   str					       =		model(
						    A__   ,	attention_mask=A__   ,	token_type_ids=A__   ,	labels=A__   ,	)
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_choices)      )






					def   __A      (			self      ):
						A__							:   Union[str, Any]					       =		self.prepare_config_and_inputs()
						(
						    A__       
						)							:   Any					       =		config_and_inputs
						A__							:   Optional[Any]					       =		{
						    """input_ids""": input_ids,
						    """token_type_ids""": token_type_ids,
						    """lengths""": input_lengths,
						    """attention_mask""": input_mask,
						}
						return config, inputs_dict



@require_torch
class    _a						(__magic_name__		,       __magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[Any]  							=	(
					    (
					        FlaubertModel,
					        FlaubertWithLMHeadModel,
					        FlaubertForQuestionAnswering,
					        FlaubertForQuestionAnsweringSimple,
					        FlaubertForSequenceClassification,
					        FlaubertForTokenClassification,
					        FlaubertForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					UpperCAmelCase__:       Optional[int]  							=	(
					    {
					        '''feature-extraction''': FlaubertModel,
					        '''fill-mask''': FlaubertWithLMHeadModel,
					        '''question-answering''': FlaubertForQuestionAnsweringSimple,
					        '''text-classification''': FlaubertForSequenceClassification,
					        '''token-classification''': FlaubertForTokenClassification,
					        '''zero-shot''': FlaubertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__      ):
						if (
						    pipeline_test_casse_name == "QAPipelineTests"
						    and tokenizer_name is not None
						    and not tokenizer_name.endswith("""Fast"""      )
						):
							# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
							# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
							# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
							return True

						return False






					def   __A      (			self   ,	A__   ,	A__   ,	A__=False      ):
						A__							:   Optional[int]					       =		super()._prepare_for_class(A__   ,	A__   ,	return_labels=A__      )

						if return_labels:
							if model_class.__name__ == "FlaubertForQuestionAnswering":
								A__							:   Tuple					       =		torch.zeros(
								    self.model_tester.batch_size   ,	dtype=torch.long   ,	device=A__      )
								A__							:   Optional[Any]					       =		torch.zeros(
								    self.model_tester.batch_size   ,	dtype=torch.long   ,	device=A__      )

						return inputs_dict






					def   __A      (			self      ):
						A__							:   int					       =		FlaubertModelTester(self      )
						A__							:   Union[str, Any]					       =		ConfigTester(self   ,	config_class=A__   ,	emb_dim=37      )






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






					def   __A      (			self      ):
						A__							:   List[Any]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_model(*A__      )






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_lm_head(*A__      )






					def   __A      (			self      ):
						A__							:   int					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_simple_qa(*A__      )






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_qa(*A__      )






					def   __A      (			self      ):
						A__							:   List[str]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_sequence_classif(*A__      )






					def   __A      (			self      ):
						A__							:   List[Any]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_token_classif(*A__      )






					def   __A      (			self      ):
						A__							:   int					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_multiple_choice(*A__      )






					@slow
					def   __A      (			self      ):
						for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A__							:   Dict					       =		FlaubertModel.from_pretrained(A__      )
							self.assertIsNotNone(A__      )






					@slow
					@require_torch_gpu
					def   __A      (			self      ):
						A__							:   Dict					       =		self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
							# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
							if model_class == FlaubertForMultipleChoice:
								return

							A__							:   Optional[Any]					       =		True
							A__							:   List[str]					       =		model_class(config=A__      )

							A__							:   List[Any]					       =		self._prepare_for_class(A__   ,	A__      )
							A__							:   int					       =		torch.jit.trace(
							    A__   ,	(inputs_dict["""input_ids"""].to("""cpu"""      ), inputs_dict["""attention_mask"""].to("""cpu"""      ))      )

							with tempfile.TemporaryDirectory() as tmp:
								torch.jit.save(A__   ,	os.path.join(A__   ,	"""traced_model.pt"""      )      )
								A__							:   Optional[int]					       =		torch.jit.load(os.path.join(A__   ,	"""traced_model.pt"""      )   ,	map_location=A__      )
								loaded(inputs_dict["""input_ids"""].to(A__      )   ,	inputs_dict["""attention_mask"""].to(A__      )      )



@require_torch
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   List[Any]					       =		FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased"""      )
						A__							:   List[Any]					       =		torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]      )
						with torch.no_grad():
							A__							:   Optional[int]					       =		model(A__      )[0]
						A__							:   Any					       =		torch.Size((1, 11, 768)      )
						self.assertEqual(output.shape   ,	A__      )
						A__							:   int					       =		torch.tensor(
						    [[[-2.6_2_5_1, -1.4_2_9_8, -0.0_2_2_7], [-2.8_5_1_0, -1.6_3_8_7, 0.2_2_5_8], [-2.8_1_1_4, -1.1_8_3_2, -0.3_0_6_6]]]      )

						self.assertTrue(torch.allclose(output[:, :3, :3]   ,	A__   ,	atol=1e-4      )      )









import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class    _a						(__magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Any  							=	KandinskyVaaImgaImgPipeline
					UpperCAmelCase__:       Optional[Any]  							=	['''image_embeds''', '''negative_image_embeds''', '''image''']
					UpperCAmelCase__:       str  							=	[
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					]
					UpperCAmelCase__:       int  							=	[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''strength''',
					    '''guidance_scale''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					UpperCAmelCase__:       Union[str, Any]  							=	False






					@property
					def   __A      (			self      ):
						return 32






					@property
					def   __A      (			self      ):
						return 32






					@property
					def   __A      (			self      ):
						return self.time_input_dim






					@property
					def   __A      (			self      ):
						return self.time_input_dim * 4






					@property
					def   __A      (			self      ):
						return 100






					@property
					def   __A      (			self      ):
						torch.manual_seed(0      )

						A__							:   Dict					       =		{
						    """in_channels""": 4,
						    # Out channels is double in channels because predicts mean and variance
						    """out_channels""": 8,
						    """addition_embed_type""": """image""",
						    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
						    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
						    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
						    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    """layers_per_block""": 1,
						    """encoder_hid_dim""": self.text_embedder_hidden_size,
						    """encoder_hid_dim_type""": """image_proj""",
						    """cross_attention_dim""": self.cross_attention_dim,
						    """attention_head_dim""": 4,
						    """resnet_time_scale_shift""": """scale_shift""",
						    """class_embed_type""": None,
						}

						A__							:   List[str]					       =		UNetaDConditionModel(**A__      )
						return model






					@property
					def   __A      (			self      ):
						return {
						    "block_out_channels": [32, 64],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 12,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}






					@property
					def   __A      (			self      ):
						torch.manual_seed(0      )
						A__							:   Tuple					       =		VQModel(**self.dummy_movq_kwargs      )
						return model






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		self.dummy_unet
						A__							:   Dict					       =		self.dummy_movq

						A__							:   List[Any]					       =		{
						    """num_train_timesteps""": 1000,
						    """beta_schedule""": """linear""",
						    """beta_start""": 0.0_0_0_8_5,
						    """beta_end""": 0.0_1_2,
						    """clip_sample""": False,
						    """set_alpha_to_one""": False,
						    """steps_offset""": 0,
						    """prediction_type""": """epsilon""",
						    """thresholding""": False,
						}

						A__							:   List[str]					       =		DDIMScheduler(**A__      )

						A__							:   List[str]					       =		{
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}

						return components






					def   __A      (			self   ,	A__   ,	A__=0      ):
						A__							:   List[str]					       =		floats_tensor((1, self.text_embedder_hidden_size)   ,	rng=random.Random(A__      )      ).to(A__      )
						A__							:   Any					       =		floats_tensor((1, self.text_embedder_hidden_size)   ,	rng=random.Random(seed + 1      )      ).to(
						    A__      )
						# create init_image
						A__							:   Dict					       =		floats_tensor((1, 3, 64, 64)   ,	rng=random.Random(A__      )      ).to(A__      )
						A__							:   Optional[int]					       =		image.cpu().permute(0   ,	2   ,	3   ,	1      )[0]
						A__							:   Dict					       =		Image.fromarray(np.uinta(A__      )      ).convert("""RGB"""      ).resize((256, 256)      )

						if str(A__      ).startswith("""mps"""      ):
							A__							:   Any					       =		torch.manual_seed(A__      )
						else:
							A__							:   List[Any]					       =		torch.Generator(device=A__      ).manual_seed(A__      )
						A__							:   Optional[int]					       =		{
						    """image""": init_image,
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """generator""": generator,
						    """height""": 64,
						    """width""": 64,
						    """num_inference_steps""": 10,
						    """guidance_scale""": 7.0,
						    """strength""": 0.2,
						    """output_type""": """np""",
						}
						return inputs






					def   __A      (			self      ):
						A__							:   str					       =		"""cpu"""

						A__							:   Any					       =		self.get_dummy_components()

						A__							:   Union[str, Any]					       =		self.pipeline_class(**A__      )
						A__							:   List[str]					       =		pipe.to(A__      )

						pipe.set_progress_bar_config(disable=A__      )

						A__							:   Dict					       =		pipe(**self.get_dummy_inputs(A__      )      )
						A__							:   Any					       =		output.images

						A__							:   List[str]					       =		pipe(
						    **self.get_dummy_inputs(A__      )   ,	return_dict=A__   ,	)[0]

						A__							:   Optional[int]					       =		image[0, -3:, -3:, -1]
						A__							:   str					       =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						A__							:   str					       =		np.array(
						    [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3]      )
						assert (
						    np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
						), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
						assert (
						    np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
						), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""



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


					'''simple docstring'''






					def   __A      (			self      ):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinskyv22/kandinskyv22_img2img_frog.npy"""      )

						A__							:   List[Any]					       =		load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""      )
						A__							:   str					       =		"""A red cartoon frog, 4k"""

						A__							:   int					       =		KandinskyVaaPriorPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-prior"""   ,	torch_dtype=torch.floataa      )
						pipe_prior.to(A__      )

						A__							:   List[Any]					       =		KandinskyVaaImgaImgPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-decoder"""   ,	torch_dtype=torch.floataa      )
						A__							:   List[str]					       =		pipeline.to(A__      )

						pipeline.set_progress_bar_config(disable=A__      )

						A__							:   Tuple					       =		torch.Generator(device="""cpu"""      ).manual_seed(0      )
						A__       ,       A__							:   Optional[Any]					       =		pipe_prior(
						    A__   ,	generator=A__   ,	num_inference_steps=5   ,	negative_prompt=""""""   ,	).to_tuple()

						A__							:   str					       =		pipeline(
						    image=A__   ,	image_embeds=A__   ,	negative_image_embeds=A__   ,	generator=A__   ,	num_inference_steps=100   ,	height=768   ,	width=768   ,	strength=0.2   ,	output_type="""np"""   ,	)

						A__							:   Optional[int]					       =		output.images[0]

						assert image.shape == (768, 768, 3)

						assert_mean_pixel_difference(A__   ,	A__      )



