



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    _A	(     __UpperCAmelCase   ):



							def 		_lowerCamelCase      (		self							:					Any):


											'''simple docstring'''

											__a							=    self.config_class(**self.inputs_dict)
											self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,		'''hidden_sizes'''))
											self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,		'''num_attention_heads'''))
											self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,		'''num_encoder_blocks'''))


class    _A	:



							def __init__(		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Any=13					,		__SCREAMING_SNAKE_CASE							:					str=64					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=3					,		__SCREAMING_SNAKE_CASE							:					str=4					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=[2, 2, 2, 2]					,		__SCREAMING_SNAKE_CASE							:					List[str]=[8, 4, 2, 1]					,		__SCREAMING_SNAKE_CASE							:					Any=[16, 32, 64, 128]					,		__SCREAMING_SNAKE_CASE							:					List[Any]=[1, 4, 8, 16]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=[1, 2, 4, 8]					,		__SCREAMING_SNAKE_CASE							:					int=True					,		__SCREAMING_SNAKE_CASE							:					List[str]=True					,		__SCREAMING_SNAKE_CASE							:					str="gelu"					,		__SCREAMING_SNAKE_CASE							:					Dict=0.1					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=0.1					,		__SCREAMING_SNAKE_CASE							:					Any=0.02					,		__SCREAMING_SNAKE_CASE							:					int=3					,		__SCREAMING_SNAKE_CASE							:					int=None					,		):


											'''simple docstring'''

											__a							=    parent
											__a							=    batch_size
											__a							=    image_size
											__a							=    num_channels
											__a							=    num_encoder_blocks
											__a							=    sr_ratios
											__a							=    depths
											__a							=    hidden_sizes
											__a							=    downsampling_rates
											__a							=    num_attention_heads
											__a							=    is_training
											__a							=    use_labels
											__a							=    hidden_act
											__a							=    hidden_dropout_prob
											__a							=    attention_probs_dropout_prob
											__a							=    initializer_range
											__a							=    num_labels
											__a							=    scope



							def 		_lowerCamelCase      (		self							:					Tuple):


											'''simple docstring'''

											__a							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

											__a							=    None
											if self.use_labels:
															__a							=    ids_tensor([self.batch_size, self.image_size, self.image_size]					,		self.num_labels)

											__a							=    self.get_config()
											return config, pixel_values, labels



							def 		_lowerCamelCase      (		self							:					Optional[Any]):


											'''simple docstring'''

											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 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Any):


											'''simple docstring'''

											__a							=    SegformerModel(config=__SCREAMING_SNAKE_CASE)
											model.to(__SCREAMING_SNAKE_CASE)
											model.eval()
											__a							=    model(__SCREAMING_SNAKE_CASE)
											__a							=    __a							=    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 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


											'''simple docstring'''

											__a							=    self.num_labels
											__a							=    SegformerForSemanticSegmentation(__SCREAMING_SNAKE_CASE)
											model.to(__SCREAMING_SNAKE_CASE)
											model.eval()
											__a							=    model(__SCREAMING_SNAKE_CASE)
											self.parent.assertEqual(
											    result.logits.shape					,		(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
											__a							=    model(__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE)
											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 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					str):


											'''simple docstring'''

											__a							=    1
											__a							=    SegformerForSemanticSegmentation(config=__SCREAMING_SNAKE_CASE)
											model.to(__SCREAMING_SNAKE_CASE)
											model.eval()
											__a							=    torch.randint(0					,		1					,		(self.batch_size, self.image_size, self.image_size)).to(__SCREAMING_SNAKE_CASE)
											__a							=    model(__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE)
											self.parent.assertGreater(result.loss					,		0.0)





							def 		_lowerCamelCase      (		self							:					Tuple):


											'''simple docstring'''

											__a							=    self.prepare_config_and_inputs()
											__a	,       __a	,       __a							=    config_and_inputs
											__a							=    {'''pixel_values''': pixel_values}
											return config, inputs_dict


@require_torch
class    _A	(     __UpperCAmelCase				,__UpperCAmelCase				,unittest.TestCase   ):
							UpperCamelCase__  :   Any						 =						(
							    (
							        SegformerModel,
							        SegformerForSemanticSegmentation,
							        SegformerForImageClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							UpperCamelCase__  :   Optional[int]						 =						(
							    {
							        '''feature-extraction''': SegformerModel,
							        '''image-classification''': SegformerForImageClassification,
							        '''image-segmentation''': SegformerForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							UpperCamelCase__  :   List[Any]						 =						True
							UpperCamelCase__  :   int						 =						False
							UpperCamelCase__  :   Tuple						 =						False
							UpperCamelCase__  :   Optional[int]						 =						False



							def 		_lowerCamelCase      (		self							:					Optional[int]):


											'''simple docstring'''

											__a							=    SegformerModelTester(self)
											__a							=    SegformerConfigTester(self					,		config_class=__SCREAMING_SNAKE_CASE)



							def 		_lowerCamelCase      (		self							:					Optional[int]):


											'''simple docstring'''

											self.config_tester.run_common_tests()



							def 		_lowerCamelCase      (		self							:					int):


											'''simple docstring'''

											__a							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)



							def 		_lowerCamelCase      (		self							:					Optional[int]):


											'''simple docstring'''

											__a							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_binary_image_segmentation(*__SCREAMING_SNAKE_CASE)



							def 		_lowerCamelCase      (		self							:					int):


											'''simple docstring'''

											__a							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_segmentation(*__SCREAMING_SNAKE_CASE)



							@unittest.skip('''SegFormer does not use inputs_embeds''')
							def 		_lowerCamelCase      (		self							:					int):


											'''simple docstring'''

											pass



							@unittest.skip('''SegFormer does not have get_input_embeddings method and get_output_embeddings methods''')
							def 		_lowerCamelCase      (		self							:					Optional[Any]):


											'''simple docstring'''

											pass



							def 		_lowerCamelCase      (		self							:					Tuple):


											'''simple docstring'''

											__a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															__a							=    model_class(__SCREAMING_SNAKE_CASE)
															__a							=    inspect.signature(model.forward)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															__a							=    [*signature.parameters.keys()]

															__a							=    ['''pixel_values''']
															self.assertListEqual(arg_names[:1]					,		__SCREAMING_SNAKE_CASE)



							def 		_lowerCamelCase      (		self							:					int):


											'''simple docstring'''

											__a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()
											__a							=    True

											for model_class in self.all_model_classes:
															__a							=    True
															__a							=    False
															__a							=    True
															__a							=    model_class(__SCREAMING_SNAKE_CASE)
															model.to(__SCREAMING_SNAKE_CASE)
															model.eval()
															with torch.no_grad():
																			__a							=    model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE))
															__a							=    outputs.attentions

															__a							=    sum(self.model_tester.depths)
															self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE)

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

															self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE)

															# verify the first attentions (first block, first layer)
															__a							=    (self.model_tester.image_size // 4) ** 2
															__a							=    (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)
															__a							=    (self.model_tester.image_size // 32) ** 2
															__a							=    (self.model_tester.image_size // (32 * 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]					,		)
															__a							=    len(__SCREAMING_SNAKE_CASE)

															# Check attention is always last and order is fine
															__a							=    True
															__a							=    True
															__a							=    model_class(__SCREAMING_SNAKE_CASE)
															model.to(__SCREAMING_SNAKE_CASE)
															model.eval()
															with torch.no_grad():
																			__a							=    model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE))

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

															__a							=    outputs.attentions

															self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE)
															# verify the first attentions (first block, first layer)
															__a							=    (self.model_tester.image_size // 4) ** 2
															__a							=    (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 		_lowerCamelCase      (		self							:					Dict):


											'''simple docstring'''

											def check_hidden_states_output(__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]):
															__a							=    model_class(__SCREAMING_SNAKE_CASE)
															model.to(__SCREAMING_SNAKE_CASE)
															model.eval()

															with torch.no_grad():
																			__a							=    model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE))

															__a							=    outputs.hidden_states

															__a							=    self.model_tester.num_encoder_blocks
															self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE)

															# 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,
															    ]					,		)

											__a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															__a							=    True
															check_hidden_states_output(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															__a							=    True

															check_hidden_states_output(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



							def 		_lowerCamelCase      (		self							:					Dict):


											'''simple docstring'''

											if not self.model_tester.is_training:
															return

											__a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()
											__a							=    True

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

															__a							=    model_class(__SCREAMING_SNAKE_CASE)
															model.to(__SCREAMING_SNAKE_CASE)
															model.train()
															__a							=    self._prepare_for_class(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		return_labels=__SCREAMING_SNAKE_CASE)
															__a							=    model(**__SCREAMING_SNAKE_CASE).loss
															loss.backward()



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


											'''simple docstring'''

											pass





							@slow
							def 		_lowerCamelCase      (		self							:					Optional[int]):


											'''simple docstring'''

											for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__a							=    SegformerModel.from_pretrained(__SCREAMING_SNAKE_CASE)
															self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





def 					__snake_case     (      ):
				__a							=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
				return image

@require_torch
class    _A	(     unittest.TestCase   ):



							@slow
							def 		_lowerCamelCase      (		self							:					Any):


											'''simple docstring'''

											__a							=    SegformerImageProcessor(
											    image_scale=(512, 512)					,		keep_ratio=__SCREAMING_SNAKE_CASE					,		align=__SCREAMING_SNAKE_CASE					,		do_random_crop=__SCREAMING_SNAKE_CASE)
											__a							=    SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512''').to(
											    __SCREAMING_SNAKE_CASE)

											__a							=    prepare_img()
											__a							=    image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors='''pt''')
											__a							=    encoded_inputs.pixel_values.to(__SCREAMING_SNAKE_CASE)

											with torch.no_grad():
															__a							=    model(__SCREAMING_SNAKE_CASE)

											__a							=    torch.Size((1, model.config.num_labels, 128, 128))
											self.assertEqual(outputs.logits.shape					,		__SCREAMING_SNAKE_CASE)

											__a							=    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(__SCREAMING_SNAKE_CASE)
											self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]					,		__SCREAMING_SNAKE_CASE					,		atol=1E-4))



							@slow
							def 		_lowerCamelCase      (		self							:					Optional[int]):


											'''simple docstring'''

											__a							=    SegformerImageProcessor(
											    image_scale=(512, 512)					,		keep_ratio=__SCREAMING_SNAKE_CASE					,		align=__SCREAMING_SNAKE_CASE					,		do_random_crop=__SCREAMING_SNAKE_CASE)
											__a							=    SegformerForSemanticSegmentation.from_pretrained(
											    '''nvidia/segformer-b1-finetuned-cityscapes-1024-1024''').to(__SCREAMING_SNAKE_CASE)

											__a							=    prepare_img()
											__a							=    image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors='''pt''')
											__a							=    encoded_inputs.pixel_values.to(__SCREAMING_SNAKE_CASE)

											with torch.no_grad():
															__a							=    model(__SCREAMING_SNAKE_CASE)

											__a							=    torch.Size((1, model.config.num_labels, 128, 128))
											self.assertEqual(outputs.logits.shape					,		__SCREAMING_SNAKE_CASE)

											__a							=    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(__SCREAMING_SNAKE_CASE)
											self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]					,		__SCREAMING_SNAKE_CASE					,		atol=1E-1))





							@slow
							def 		_lowerCamelCase      (		self							:					List[str]):


											'''simple docstring'''

											__a							=    SegformerImageProcessor(
											    image_scale=(512, 512)					,		keep_ratio=__SCREAMING_SNAKE_CASE					,		align=__SCREAMING_SNAKE_CASE					,		do_random_crop=__SCREAMING_SNAKE_CASE)
											__a							=    SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512''').to(
											    __SCREAMING_SNAKE_CASE)

											__a							=    prepare_img()
											__a							=    image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors='''pt''')
											__a							=    encoded_inputs.pixel_values.to(__SCREAMING_SNAKE_CASE)

											with torch.no_grad():
															__a							=    model(__SCREAMING_SNAKE_CASE)

											__a							=    outputs.logits.detach().cpu()

											__a							=    image_processor.post_process_semantic_segmentation(outputs=__SCREAMING_SNAKE_CASE					,		target_sizes=[(500, 300)])
											__a							=    torch.Size((500, 300))
											self.assertEqual(segmentation[0].shape					,		__SCREAMING_SNAKE_CASE)

											__a							=    image_processor.post_process_semantic_segmentation(outputs=__SCREAMING_SNAKE_CASE)
											__a							=    torch.Size((128, 128))
											self.assertEqual(segmentation[0].shape					,		__SCREAMING_SNAKE_CASE)










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	(     __UpperCAmelCase				,unittest.TestCase   ):
       UpperCamelCase__  :   str						 =						KandinskyVaaImgaImgPipeline
       UpperCamelCase__  :   Optional[Any]						 =						['''image_embeds''', '''negative_image_embeds''', '''image''']
       UpperCamelCase__  :   Dict						 =						[
           '''image_embeds''',
           '''negative_image_embeds''',
           '''image''',
       ]
       UpperCamelCase__  :   Any						 =						[
           '''generator''',
           '''height''',
           '''width''',
           '''strength''',
           '''guidance_scale''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       UpperCamelCase__  :   List[Any]						 =						False



       @property
       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           return 32



       @property
       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           return 32



       @property
       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           return self.time_input_dim



       @property
       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           return self.time_input_dim * 4



       @property
       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           return 100



       @property
       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           torch.manual_seed(0)

           __a							=    {
               '''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							=    UNetaDConditionModel(**__SCREAMING_SNAKE_CASE)
           return model



       @property
       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           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 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           torch.manual_seed(0)
           __a							=    VQModel(**self.dummy_movq_kwargs)
           return model



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.dummy_unet
           __a							=    self.dummy_movq

           __a							=    {
               '''num_train_timesteps''': 1_000,
               '''beta_schedule''': '''linear''',
               '''beta_start''': 0.0_00_85,
               '''beta_end''': 0.0_12,
               '''clip_sample''': False,
               '''set_alpha_to_one''': False,
               '''steps_offset''': 0,
               '''prediction_type''': '''epsilon''',
               '''thresholding''': False,
           }

           __a							=    DDIMScheduler(**__SCREAMING_SNAKE_CASE)

           __a							=    {
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''movq''': movq,
           }

           return components



       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=0):


           '''simple docstring'''

           __a							=    floats_tensor((1, self.text_embedder_hidden_size)					,		rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
           __a							=    floats_tensor((1, self.text_embedder_hidden_size)					,		rng=random.Random(seed + 1)).to(
               __SCREAMING_SNAKE_CASE)
           # create init_image
           __a							=    floats_tensor((1, 3, 64, 64)					,		rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
           __a							=    image.cpu().permute(0					,		2					,		3					,		1)[0]
           __a							=    Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE)).convert('''RGB''').resize((256, 256))

           if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
               __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           else:
               __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    {
               '''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 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    '''cpu'''

           __a							=    self.get_dummy_components()

           __a							=    self.pipeline_class(**__SCREAMING_SNAKE_CASE)
           __a							=    pipe.to(__SCREAMING_SNAKE_CASE)

           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE))
           __a							=    output.images

           __a							=    pipe(
               **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)					,		return_dict=__SCREAMING_SNAKE_CASE					,		)[0]

           __a							=    image[0, -3:, -3:, -1]
           __a							=    image_from_tuple[0, -3:, -3:, -1]

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

           __a							=    np.array(
               [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63])
           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   ):



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()





       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/kandinskyv22/kandinskyv22_img2img_frog.npy''')

           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''')
           __a							=    '''A red cartoon frog, 4k'''

           __a							=    KandinskyVaaPriorPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-prior'''					,		torch_dtype=torch.floataa)
           pipe_prior.to(__SCREAMING_SNAKE_CASE)

           __a							=    KandinskyVaaImgaImgPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-decoder'''					,		torch_dtype=torch.floataa)
           __a							=    pipeline.to(__SCREAMING_SNAKE_CASE)

           pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    torch.Generator(device='''cpu''').manual_seed(0)
           __a	,       __a							=    pipe_prior(
               __SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE					,		num_inference_steps=5					,		negative_prompt=''''''					,		).to_tuple()

           __a							=    pipeline(
               image=__SCREAMING_SNAKE_CASE					,		image_embeds=__SCREAMING_SNAKE_CASE					,		negative_image_embeds=__SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE					,		num_inference_steps=100					,		height=768					,		width=768					,		strength=0.2					,		output_type='''np'''					,		)

           __a							=    output.images[0]

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

           assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)





