
'''simple docstring'''


import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_MAPPING,
					    SegformerForImageClassification,
					    SegformerForSemanticSegmentation,
					    SegformerModel,
					)
					from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import SegformerImageProcessor

class 	__lowercase				(  _lowercase    ):

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		self.config_class(**self.inputs_dict      )
								self.parent.assertTrue(hasattr(A			,   '''hidden_sizes'''      )      )
								self.parent.assertTrue(hasattr(A			,   '''num_attention_heads'''      )      )
								self.parent.assertTrue(hasattr(A			,   '''num_encoder_blocks'''      )      )

class 	__lowercase				:

			def __init__(self			,   A			,   A=1_3			,   A=6_4			,   A=3			,   A=4			,   A=[2, 2, 2, 2]			,   A=[8, 4, 2, 1]			,   A=[1_6, 3_2, 6_4, 1_2_8]			,   A=[1, 4, 8, 1_6]			,   A=[1, 2, 4, 8]			,   A=True			,   A=True			,   A="gelu"			,   A=0.1			,   A=0.1			,   A=0.02			,   A=3			,   A=None			,   ):
								lowerCamelCase_					:      Any     =		parent
								lowerCamelCase_					:      Optional[int]     =		batch_size
								lowerCamelCase_					:      str     =		image_size
								lowerCamelCase_					:      int     =		num_channels
								lowerCamelCase_					:      Tuple     =		num_encoder_blocks
								lowerCamelCase_					:      int     =		sr_ratios
								lowerCamelCase_					:      List[str]     =		depths
								lowerCamelCase_					:      Union[str, Any]     =		hidden_sizes
								lowerCamelCase_					:      List[str]     =		downsampling_rates
								lowerCamelCase_					:      Any     =		num_attention_heads
								lowerCamelCase_					:      Union[str, Any]     =		is_training
								lowerCamelCase_					:      Dict     =		use_labels
								lowerCamelCase_					:      Optional[Any]     =		hidden_act
								lowerCamelCase_					:      Tuple     =		hidden_dropout_prob
								lowerCamelCase_					:      List[Any]     =		attention_probs_dropout_prob
								lowerCamelCase_					:      int     =		initializer_range
								lowerCamelCase_					:      Tuple     =		num_labels
								lowerCamelCase_					:      str     =		scope

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

								lowerCamelCase_					:      Optional[Any]     =		None
								if self.use_labels:
													lowerCamelCase_					:      List[Any]     =		ids_tensor([self.batch_size, self.image_size, self.image_size]			,   self.num_labels      )

								lowerCamelCase_					:      List[str]     =		self.get_config()
								return config, pixel_values, labels

			def        UpperCAmelCase__ (self      ):
								return SegformerConfig(
								    image_size=self.image_size			,   num_channels=self.num_channels			,   num_encoder_blocks=self.num_encoder_blocks			,   depths=self.depths			,   hidden_sizes=self.hidden_sizes			,   num_attention_heads=self.num_attention_heads			,   hidden_act=self.hidden_act			,   hidden_dropout_prob=self.hidden_dropout_prob			,   attention_probs_dropout_prob=self.attention_probs_dropout_prob			,   initializer_range=self.initializer_range			,   )

			def        UpperCAmelCase__ (self			,   A			,   A			,   A      ):
								lowerCamelCase_					:      Tuple     =		SegformerModel(config=A      )
								model.to(A      )
								model.eval()
								lowerCamelCase_					:      str     =		model(A      )
								lowerCamelCase_					:      Optional[Any]     =		self.image_size // (self.downsampling_rates[-1] * 2)
								self.parent.assertEqual(
								    result.last_hidden_state.shape			,   (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)      )

			def        UpperCAmelCase__ (self			,   A			,   A			,   A      ):
								lowerCamelCase_					:      Union[str, Any]     =		self.num_labels
								lowerCamelCase_					:      Tuple     =		SegformerForSemanticSegmentation(A      )
								model.to(A      )
								model.eval()
								lowerCamelCase_					:      str     =		model(A      )
								self.parent.assertEqual(
								    result.logits.shape			,   (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)      )
								lowerCamelCase_					:      Optional[int]     =		model(A			,   labels=A      )
								self.parent.assertEqual(
								    result.logits.shape			,   (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)      )
								self.parent.assertGreater(result.loss			,   0.0      )

			def        UpperCAmelCase__ (self			,   A			,   A			,   A      ):
								lowerCamelCase_					:      int     =		1
								lowerCamelCase_					:      str     =		SegformerForSemanticSegmentation(config=A      )
								model.to(A      )
								model.eval()
								lowerCamelCase_					:      List[str]     =		torch.randint(0			,   1			,   (self.batch_size, self.image_size, self.image_size)      ).to(A      )
								lowerCamelCase_					:      int     =		model(A			,   labels=A      )
								self.parent.assertGreater(result.loss			,   0.0      )






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Union[str, Any]     =		self.prepare_config_and_inputs()
								lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      int     =		config_and_inputs
								lowerCamelCase_					:      List[Any]     =		{'''pixel_values''': pixel_values}
								return config, inputs_dict

@require_torch
class 	__lowercase				(  _lowercase      ,     _lowercase      ,     unittest.TestCase    ):
			lowerCamelCase			:		Tuple	      =			(
			    (
			        SegformerModel,
			        SegformerForSemanticSegmentation,
			        SegformerForImageClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase			:		Union[str, Any]	      =			(
			    {
			        "feature-extraction": SegformerModel,
			        "image-classification": SegformerForImageClassification,
			        "image-segmentation": SegformerForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase			:		Optional[Any]	      =			True
			lowerCamelCase			:		Tuple	      =			False
			lowerCamelCase			:		Any	      =			False
			lowerCamelCase			:		Optional[int]	      =			False

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		SegformerModelTester(self      )
								lowerCamelCase_					:      List[Any]     =		SegformerConfigTester(self			,   config_class=A      )

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

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_binary_image_segmentation(*A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Union[str, Any]     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_segmentation(*A      )

			@unittest.skip('''SegFormer does not use inputs_embeds'''      )
			def        UpperCAmelCase__ (self      ):
								pass

			@unittest.skip('''SegFormer does not have get_input_embeddings method and get_output_embeddings methods'''      )
			def        UpperCAmelCase__ (self      ):
								pass

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_,      lowerCamelCase_					:      Optional[Any]     =		self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													lowerCamelCase_					:      str     =		model_class(A      )
													lowerCamelCase_					:      Optional[Any]     =		inspect.signature(model.forward      )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCamelCase_					:      List[Any]     =		[*signature.parameters.keys()]

													lowerCamelCase_					:      Tuple     =		['''pixel_values''']
													self.assertListEqual(arg_names[:1]			,   A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_,      lowerCamelCase_					:      str     =		self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase_					:      str     =		True

								for model_class in self.all_model_classes:
													lowerCamelCase_					:      int     =		True
													lowerCamelCase_					:      Tuple     =		False
													lowerCamelCase_					:      List[str]     =		True
													lowerCamelCase_					:      Optional[Any]     =		model_class(A      )
													model.to(A      )
													model.eval()
													with torch.no_grad():
																		lowerCamelCase_					:      Optional[int]     =		model(**self._prepare_for_class(A			,   A      )      )
													lowerCamelCase_					:      Any     =		outputs.attentions

													lowerCamelCase_					:      Dict     =		sum(self.model_tester.depths      )
													self.assertEqual(len(A      )			,   A      )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													lowerCamelCase_					:      Any     =		True
													lowerCamelCase_					:      Dict     =		model_class(A      )
													model.to(A      )
													model.eval()
													with torch.no_grad():
																		lowerCamelCase_					:      Tuple     =		model(**self._prepare_for_class(A			,   A      )      )
													lowerCamelCase_					:      Any     =		outputs.attentions

													self.assertEqual(len(A      )			,   A      )

													# verify the first attentions (first block, first layer)
													lowerCamelCase_					:      Dict     =		(self.model_tester.image_size // 4) ** 2
													lowerCamelCase_					:      Union[str, Any]     =		(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(attentions[0].shape[-3:]      )			,   [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]			,   )

													# verify the last attentions (last block, last layer)
													lowerCamelCase_					:      int     =		(self.model_tester.image_size // 3_2) ** 2
													lowerCamelCase_					:      List[Any]     =		(self.model_tester.image_size // (3_2 * self.model_tester.sr_ratios[-1])) ** 2
													self.assertListEqual(
													    list(attentions[-1].shape[-3:]      )			,   [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]			,   )
													lowerCamelCase_					:      Union[str, Any]     =		len(A      )

													# Check attention is always last and order is fine
													lowerCamelCase_					:      Optional[int]     =		True
													lowerCamelCase_					:      Tuple     =		True
													lowerCamelCase_					:      Dict     =		model_class(A      )
													model.to(A      )
													model.eval()
													with torch.no_grad():
																		lowerCamelCase_					:      List[Any]     =		model(**self._prepare_for_class(A			,   A      )      )

													self.assertEqual(out_len + 1			,   len(A      )      )

													lowerCamelCase_					:      List[Any]     =		outputs.attentions

													self.assertEqual(len(A      )			,   A      )
													# verify the first attentions (first block, first layer)
													lowerCamelCase_					:      List[str]     =		(self.model_tester.image_size // 4) ** 2
													lowerCamelCase_					:      Tuple     =		(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]      )			,   [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]			,   )

			def        UpperCAmelCase__ (self      ):
								def check_hidden_states_output(A			,   A			,   A      ):
													lowerCamelCase_					:      Dict     =		model_class(A      )
													model.to(A      )
													model.eval()

													with torch.no_grad():
																		lowerCamelCase_					:      int     =		model(**self._prepare_for_class(A			,   A      )      )

													lowerCamelCase_					:      int     =		outputs.hidden_states

													lowerCamelCase_					:      Optional[int]     =		self.model_tester.num_encoder_blocks
													self.assertEqual(len(A      )			,   A      )

													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]      )			,   [
													        self.model_tester.hidden_sizes[0],
													        self.model_tester.image_size // 4,
													        self.model_tester.image_size // 4,
													    ]			,   )

								lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													lowerCamelCase_					:      Any     =		True
													check_hidden_states_output(A			,   A			,   A      )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowerCamelCase_					:      Tuple     =		True

													check_hidden_states_output(A			,   A			,   A      )

			def        UpperCAmelCase__ (self      ):
								if not self.model_tester.is_training:
													return

								lowerCamelCase_,      lowerCamelCase_					:      Union[str, Any]     =		self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase_					:      Optional[int]     =		True

								for model_class in self.all_model_classes:
													if model_class in get_values(A      ):
																		continue

													lowerCamelCase_					:      int     =		model_class(A      )
													model.to(A      )
													model.train()
													lowerCamelCase_					:      Dict     =		self._prepare_for_class(A			,   A			,   return_labels=A      )
													lowerCamelCase_					:      str     =		model(**A      ).loss
													loss.backward()

			@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
			def        UpperCAmelCase__ (self      ):
								pass






			@slow
			def        UpperCAmelCase__ (self      ):
								for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase_					:      Tuple     =		SegformerModel.from_pretrained(A      )
													self.assertIsNotNone(A      )




def lowercase_	(   )     ->							Optional[int]:
					'''simple docstring'''

					lowerCamelCase_					:      List[Any]     =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
					return image






@require_torch
class 	__lowercase				(  unittest.TestCase    ):

			@slow
			def        UpperCAmelCase__ (self      ):
								# only resize + normalize
								lowerCamelCase_					:      Dict     =		SegformerImageProcessor(
								    image_scale=(5_1_2, 5_1_2)			,   keep_ratio=A			,   align=A			,   do_random_crop=A      )
								lowerCamelCase_					:      Optional[int]     =		SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''      ).to(
								    A      )

								lowerCamelCase_					:      Optional[Any]     =		prepare_img()
								lowerCamelCase_					:      Any     =		image_processor(images=A			,   return_tensors='''pt'''      )
								lowerCamelCase_					:      List[str]     =		encoded_inputs.pixel_values.to(A      )

								with torch.no_grad():
													lowerCamelCase_					:      Any     =		model(A      )

								lowerCamelCase_					:      List[str]     =		torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)      )
								self.assertEqual(outputs.logits.shape			,   A      )

								lowerCamelCase_					:      List[Any]     =		torch.tensor(
								    [
								        [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
								        [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
								        [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
								    ]      ).to(A      )
								self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]			,   A			,   atol=1E-4      )      )

			@slow
			def        UpperCAmelCase__ (self      ):
								# only resize + normalize
								lowerCamelCase_					:      Dict     =		SegformerImageProcessor(
								    image_scale=(5_1_2, 5_1_2)			,   keep_ratio=A			,   align=A			,   do_random_crop=A      )
								lowerCamelCase_					:      List[Any]     =		SegformerForSemanticSegmentation.from_pretrained(
								    '''nvidia/segformer-b1-finetuned-cityscapes-1024-1024'''      ).to(A      )

								lowerCamelCase_					:      Optional[int]     =		prepare_img()
								lowerCamelCase_					:      Union[str, Any]     =		image_processor(images=A			,   return_tensors='''pt'''      )
								lowerCamelCase_					:      Optional[int]     =		encoded_inputs.pixel_values.to(A      )

								with torch.no_grad():
													lowerCamelCase_					:      Dict     =		model(A      )

								lowerCamelCase_					:      List[str]     =		torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)      )
								self.assertEqual(outputs.logits.shape			,   A      )

								lowerCamelCase_					:      Optional[int]     =		torch.tensor(
								    [
								        [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
								        [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
								        [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
								    ]      ).to(A      )
								self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]			,   A			,   atol=1E-1      )      )






			@slow
			def        UpperCAmelCase__ (self      ):
								# only resize + normalize
								lowerCamelCase_					:      Optional[int]     =		SegformerImageProcessor(
								    image_scale=(5_1_2, 5_1_2)			,   keep_ratio=A			,   align=A			,   do_random_crop=A      )
								lowerCamelCase_					:      List[Any]     =		SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''      ).to(
								    A      )

								lowerCamelCase_					:      List[Any]     =		prepare_img()
								lowerCamelCase_					:      Optional[int]     =		image_processor(images=A			,   return_tensors='''pt'''      )
								lowerCamelCase_					:      Tuple     =		encoded_inputs.pixel_values.to(A      )

								with torch.no_grad():
													lowerCamelCase_					:      Union[str, Any]     =		model(A      )

								lowerCamelCase_					:      List[str]     =		outputs.logits.detach().cpu()

								lowerCamelCase_					:      Dict     =		image_processor.post_process_semantic_segmentation(outputs=A			,   target_sizes=[(5_0_0, 3_0_0)]      )
								lowerCamelCase_					:      Optional[int]     =		torch.Size((5_0_0, 3_0_0)      )
								self.assertEqual(segmentation[0].shape			,   A      )

								lowerCamelCase_					:      str     =		image_processor.post_process_semantic_segmentation(outputs=A      )
								lowerCamelCase_					:      int     =		torch.Size((1_2_8, 1_2_8)      )
								self.assertEqual(segmentation[0].shape			,   A      )




'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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 	__lowercase				(  _lowercase      ,     unittest.TestCase    ):
			lowerCamelCase			:		Optional[Any]	      =			KandinskyVaaInpaintPipeline
			lowerCamelCase			:		Tuple	      =			["image_embeds", "negative_image_embeds", "image", "mask_image"]
			lowerCamelCase			:		List[str]	      =			[
			    "image_embeds",
			    "negative_image_embeds",
			    "image",
			    "mask_image",
			]
			lowerCamelCase			:		Optional[int]	      =			[
			    "generator",
			    "height",
			    "width",
			    "latents",
			    "guidance_scale",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			lowerCamelCase			:		List[Any]	      =			False

			@property
			def        UpperCAmelCase__ (self      ):
								return 3_2

			@property
			def        UpperCAmelCase__ (self      ):
								return 3_2

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

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

			@property
			def        UpperCAmelCase__ (self      ):
								return 1_0_0

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

								lowerCamelCase_					:      Optional[Any]     =		{
								    '''in_channels''': 9,
								    # 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,
								}

								lowerCamelCase_					:      str     =		UNetaDConditionModel(**A      )
								return model

			@property
			def        UpperCAmelCase__ (self      ):
								return {
								    "block_out_channels": [3_2, 6_4],
								    "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": 1_2,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}

			@property
			def        UpperCAmelCase__ (self      ):
								torch.manual_seed(0      )
								lowerCamelCase_					:      Optional[Any]     =		VQModel(**self.dummy_movq_kwargs      )
								return model

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[str]     =		self.dummy_unet
								lowerCamelCase_					:      Optional[int]     =		self.dummy_movq

								lowerCamelCase_					:      Union[str, Any]     =		DDIMScheduler(
								    num_train_timesteps=1_0_0_0			,   beta_schedule='''linear'''			,   beta_start=0.0_00_85			,   beta_end=0.0_12			,   clip_sample=A			,   set_alpha_to_one=A			,   steps_offset=1			,   prediction_type='''epsilon'''			,   thresholding=A			,   )

								lowerCamelCase_					:      Dict     =		{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}

								return components

			def        UpperCAmelCase__ (self			,   A			,   A=0      ):
								lowerCamelCase_					:      List[Any]     =		floats_tensor((1, self.text_embedder_hidden_size)			,   rng=random.Random(A      )      ).to(A      )
								lowerCamelCase_					:      Tuple     =		floats_tensor((1, self.text_embedder_hidden_size)			,   rng=random.Random(seed + 1      )      ).to(
								    A      )
								# create init_image
								lowerCamelCase_					:      Dict     =		floats_tensor((1, 3, 6_4, 6_4)			,   rng=random.Random(A      )      ).to(A      )
								lowerCamelCase_					:      List[str]     =		image.cpu().permute(0			,   2			,   3			,   1      )[0]
								lowerCamelCase_					:      Any     =		Image.fromarray(np.uinta(A      )      ).convert('''RGB'''      ).resize((2_5_6, 2_5_6)      )
								# create mask
								lowerCamelCase_					:      List[str]     =		np.ones((6_4, 6_4)			,   dtype=np.floataa      )
								lowerCamelCase_					:      Union[str, Any]     =		0

								if str(A      ).startswith('''mps'''      ):
													lowerCamelCase_					:      int     =		torch.manual_seed(A      )
								else:
													lowerCamelCase_					:      Union[str, Any]     =		torch.Generator(device=A      ).manual_seed(A      )
								lowerCamelCase_					:      Optional[Any]     =		{
								    '''image''': init_image,
								    '''mask_image''': mask,
								    '''image_embeds''': image_embeds,
								    '''negative_image_embeds''': negative_image_embeds,
								    '''generator''': generator,
								    '''height''': 6_4,
								    '''width''': 6_4,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 4.0,
								    '''output_type''': '''np''',
								}
								return inputs

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[Any]     =		'''cpu'''

								lowerCamelCase_					:      str     =		self.get_dummy_components()

								lowerCamelCase_					:      Tuple     =		self.pipeline_class(**A      )
								lowerCamelCase_					:      Union[str, Any]     =		pipe.to(A      )

								pipe.set_progress_bar_config(disable=A      )

								lowerCamelCase_					:      str     =		pipe(**self.get_dummy_inputs(A      )      )
								lowerCamelCase_					:      Any     =		output.images

								lowerCamelCase_					:      Union[str, Any]     =		pipe(
								    **self.get_dummy_inputs(A      )			,   return_dict=A			,   )[0]

								lowerCamelCase_					:      List[Any]     =		image[0, -3:, -3:, -1]
								lowerCamelCase_					:      Tuple     =		image_from_tuple[0, -3:, -3:, -1]

								print(F"""image.shape {image.shape}"""      )

								assert image.shape == (1, 6_4, 6_4, 3)

								lowerCamelCase_					:      Any     =		np.array(
								    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]      )

								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()}"""






			def        UpperCAmelCase__ (self      ):
								super().test_inference_batch_single_identical(expected_max_diff=3E-3      )

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

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






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''      )

								lowerCamelCase_					:      Optional[Any]     =		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
								lowerCamelCase_					:      List[str]     =		np.ones((7_6_8, 7_6_8)			,   dtype=np.floataa      )
								lowerCamelCase_					:      int     =		0

								lowerCamelCase_					:      Union[str, Any]     =		'''a hat'''

								lowerCamelCase_					:      Optional[int]     =		KandinskyVaaPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-prior'''			,   torch_dtype=torch.floataa      )
								pipe_prior.to(A      )

								lowerCamelCase_					:      Any     =		KandinskyVaaInpaintPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''			,   torch_dtype=torch.floataa      )
								lowerCamelCase_					:      Any     =		pipeline.to(A      )
								pipeline.set_progress_bar_config(disable=A      )

								lowerCamelCase_					:      Any     =		torch.Generator(device='''cpu'''      ).manual_seed(0      )
								lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		pipe_prior(
								    A			,   generator=A			,   num_inference_steps=5			,   negative_prompt=''''''			,   ).to_tuple()

								lowerCamelCase_					:      Union[str, Any]     =		pipeline(
								    image=A			,   mask_image=A			,   image_embeds=A			,   negative_image_embeds=A			,   generator=A			,   num_inference_steps=1_0_0			,   height=7_6_8			,   width=7_6_8			,   output_type='''np'''			,   )

								lowerCamelCase_					:      List[str]     =		output.images[0]

								assert image.shape == (7_6_8, 7_6_8, 3)

								assert_mean_pixel_difference(A			,   A      )


