


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
 import torch

 from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

 if is_vision_available():
  from transformers import MaskaFormerImageProcessor

if is_vision_available():
 from PIL import Image

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case=2      , __snake_case=True      , __snake_case=False      , __snake_case=1_0      , __snake_case=3      , __snake_case=3_2 * 8      , __snake_case=3_2 * 8      , __snake_case=4      , __snake_case=6_4      , ):
         snake_case					=    parent
         snake_case					=    batch_size
         snake_case					=    is_training
         snake_case					=    use_auxiliary_loss
         snake_case					=    num_queries
         snake_case					=    num_channels
         snake_case					=    min_size
         snake_case					=    max_size
         snake_case					=    num_labels
         snake_case					=    hidden_dim
         snake_case					=    hidden_dim
    def 		a_      (	self   ):
         snake_case					=    floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]   ).to(
             __snake_case   )

         snake_case					=    torch.ones([self.batch_size, self.min_size, self.max_size]      , device=__snake_case   )

         snake_case					=    (
             torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]      , device=__snake_case   ) > 0.5
         ).float()
         snake_case					=    (torch.rand((self.batch_size, self.num_labels)      , device=__snake_case   ) > 0.5).long()

         snake_case					=    self.get_config()
         return config, pixel_values, pixel_mask, mask_labels, class_labels
    def 		a_      (	self   ):
         snake_case					=    MaskaFormerConfig(
             hidden_size=self.hidden_dim      , )
         snake_case					=    self.num_queries
         snake_case					=    self.num_labels

         snake_case					=    [1, 1, 1, 1]
         snake_case					=    self.num_channels

         snake_case					=    6_4
         snake_case					=    1_2_8
         snake_case					=    self.hidden_dim
         snake_case					=    self.hidden_dim
         snake_case					=    self.hidden_dim
         return config
    def 		a_      (	self   ):
         snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.prepare_config_and_inputs()
         snake_case					=    {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
         return config, inputs_dict
    def 		a_      (	self      , __snake_case      , __snake_case   ):
         snake_case					=    output.encoder_hidden_states
         snake_case					=    output.pixel_decoder_hidden_states
         snake_case					=    output.transformer_decoder_hidden_states

         self.parent.assertTrue(len(__snake_case   )      , len(config.backbone_config.depths   )   )
         self.parent.assertTrue(len(__snake_case   )      , len(config.backbone_config.depths   )   )
         self.parent.assertTrue(len(__snake_case   )      , config.decoder_layers   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case=False   ):
         with torch.no_grad():
              snake_case					=    MaskaFormerModel(config=__snake_case   )
              model.to(__snake_case   )
              model.eval()

              snake_case					=    model(pixel_values=__snake_case      , pixel_mask=__snake_case   )
              snake_case					=    model(__snake_case      , output_hidden_states=__snake_case   )

         self.parent.assertEqual(
             output.transformer_decoder_last_hidden_state.shape      , (self.batch_size, self.num_queries, self.hidden_dim)      , )
         # let's ensure the other two hidden state exists
         self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None   )
         self.parent.assertTrue(output.encoder_last_hidden_state is not None   )

         if output_hidden_states:
              self.check_output_hidden_state(__snake_case      , __snake_case   )




    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         snake_case					=    MaskaFormerForUniversalSegmentation(config=__snake_case   )
         model.to(__snake_case   )
         model.eval()

         def comm_check_on_output(__snake_case   ):
              # let's still check that all the required stuff is there
              self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None   )
              self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None   )
              self.parent.assertTrue(result.encoder_last_hidden_state is not None   )
              # okay, now we need to check the logits shape
              # due to the encoder compression, masks have a //4 spatial size
              self.parent.assertEqual(
                  result.masks_queries_logits.shape      , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)      , )
              # + 1 for null class
              self.parent.assertEqual(
                  result.class_queries_logits.shape      , (self.batch_size, self.num_queries, self.num_labels + 1)   )

         with torch.no_grad():
              snake_case					=    model(pixel_values=__snake_case      , pixel_mask=__snake_case   )
              snake_case					=    model(__snake_case   )

              comm_check_on_output(__snake_case   )

              snake_case					=    model(
                  pixel_values=__snake_case      , pixel_mask=__snake_case      , mask_labels=__snake_case      , class_labels=__snake_case   )

         comm_check_on_output(__snake_case   )

         self.parent.assertTrue(result.loss is not None   )
         self.parent.assertEqual(result.loss.shape      , torch.Size([1]   )   )






@require_torch
class   A__ (   snake_case__	, snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
    __magic_name__        =		{'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    __magic_name__        =		False
    def 		a_      (	self   ):
         snake_case					=    MaskaFormerModelTester(self   )
         snake_case					=    ConfigTester(self      , config_class=__snake_case      , has_text_modality=__snake_case   )
    def 		a_      (	self   ):
         self.config_tester.run_common_tests()
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(__snake_case      , **__snake_case      , output_hidden_states=__snake_case   )
    def 		a_      (	self   ):
         snake_case					=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__snake_case   )
    @unittest.skip(reason='''Mask2Former does not use inputs_embeds'''   )
    def 		a_      (	self   ):
         pass
    @unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''   )
    def 		a_      (	self   ):
         pass
    @unittest.skip(reason='''Mask2Former is not a generative model'''   )
    def 		a_      (	self   ):
         pass
    @unittest.skip(reason='''Mask2Former does not use token embeddings'''   )
    def 		a_      (	self   ):
         pass
    @require_torch_multi_gpu
    @unittest.skip(
        reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''   )
    def 		a_      (	self   ):
         pass
    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
    def 		a_      (	self   ):
         pass
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()

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

              snake_case					=    ['''pixel_values''']
              self.assertListEqual(arg_names[:1]      , __snake_case   )
    @slow
    def 		a_      (	self   ):
         for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
              snake_case					=    MaskaFormerModel.from_pretrained(__snake_case   )
              self.assertIsNotNone(__snake_case   )
    def 		a_      (	self   ):
         snake_case					=    (self.model_tester.min_size,) * 2
         snake_case					=    {
             '''pixel_values''': torch.randn((2, 3, *size)      , device=__snake_case   ),
             '''mask_labels''': torch.randn((2, 1_0, *size)      , device=__snake_case   ),
             '''class_labels''': torch.zeros(2      , 1_0      , device=__snake_case   ).long(),
         }
         snake_case					=    self.model_tester.get_config()

         snake_case					=    MaskaFormerForUniversalSegmentation(__snake_case   ).to(__snake_case   )
         snake_case					=    model(**__snake_case   )
         self.assertTrue(outputs.loss is not None   )
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(__snake_case      , **__snake_case      , output_hidden_states=__snake_case   )
    def 		a_      (	self   ):
         snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case					=    model_class(__snake_case   ).to(__snake_case   )
              snake_case					=    model(**__snake_case      , output_attentions=__snake_case   )
              self.assertTrue(outputs.attentions is not None   )
    def 		a_      (	self   ):
         if not self.model_tester.is_training:
              return

         snake_case					=    self.all_model_classes[1]
         snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs()

         snake_case					=    model_class(__snake_case   )
         model.to(__snake_case   )
         model.train()

         snake_case					=    model(__snake_case      , mask_labels=__snake_case      , class_labels=__snake_case   ).loss
         loss.backward()




    def 		a_      (	self   ):
         snake_case					=    self.all_model_classes[1]
         snake_case			,  snake_case			,  snake_case			,  snake_case			,  snake_case					=    self.model_tester.prepare_config_and_inputs()
         snake_case					=    True
         snake_case					=    True

         snake_case					=    model_class(__snake_case   ).to(__snake_case   )
         model.train()

         snake_case					=    model(__snake_case      , mask_labels=__snake_case      , class_labels=__snake_case   )

         snake_case					=    outputs.encoder_hidden_states[0]
         encoder_hidden_states.retain_grad()

         snake_case					=    outputs.pixel_decoder_hidden_states[0]
         pixel_decoder_hidden_states.retain_grad()

         snake_case					=    outputs.transformer_decoder_hidden_states[0]
         transformer_decoder_hidden_states.retain_grad()

         snake_case					=    outputs.attentions[0]
         attentions.retain_grad()

         outputs.loss.backward(retain_graph=__snake_case   )

         self.assertIsNotNone(encoder_hidden_states.grad   )
         self.assertIsNotNone(pixel_decoder_hidden_states.grad   )
         self.assertIsNotNone(transformer_decoder_hidden_states.grad   )
         self.assertIsNotNone(attentions.grad   )






_SCREAMING_SNAKE_CASE      				=   1e-4
def       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
     return image

@require_vision
@slow
class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    @cached_property
    def 		a_      (	self   ):
         return "facebook/mask2former-swin-small-coco-instance"
    @cached_property
    def 		a_      (	self   ):
         return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints   ) if is_vision_available() else None
    def 		a_      (	self   ):
         snake_case					=    MaskaFormerModel.from_pretrained(self.model_checkpoints   ).to(__snake_case   )
         snake_case					=    self.default_image_processor
         snake_case					=    prepare_img()
         snake_case					=    image_processor(__snake_case      , return_tensors='''pt'''   ).to(__snake_case   )
         snake_case					=    inputs['''pixel_values'''].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0   )
         # check size
         self.assertEqual(__snake_case      , (1, 3, 3_8_4, 3_8_4)   )

         with torch.no_grad():
              snake_case					=    model(**__snake_case   )

         snake_case					=    torch.tensor(
             [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]   ).to(__snake_case   )
         self.assertTrue(
             torch.allclose(
                 outputs.encoder_last_hidden_state[0, 0, :3, :3]      , __snake_case      , atol=__snake_case   )   )

         snake_case					=    torch.tensor(
             [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]   ).to(__snake_case   )
         self.assertTrue(
             torch.allclose(
                 outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]      , __snake_case      , atol=__snake_case   )   )

         snake_case					=    torch.tensor(
             [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]   ).to(__snake_case   )
         self.assertTrue(
             torch.allclose(
                 outputs.transformer_decoder_last_hidden_state[0, :3, :3]      , __snake_case      , atol=__snake_case   )   )
    def 		a_      (	self   ):
         snake_case					=    MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(__snake_case   ).eval()
         snake_case					=    self.default_image_processor
         snake_case					=    prepare_img()
         snake_case					=    image_processor(__snake_case      , return_tensors='''pt'''   ).to(__snake_case   )
         snake_case					=    inputs['''pixel_values'''].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0   )
         # check size
         self.assertEqual(__snake_case      , (1, 3, 3_8_4, 3_8_4)   )

         with torch.no_grad():
              snake_case					=    model(**__snake_case   )
         # masks_queries_logits
         snake_case					=    outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape      , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)   )
         snake_case					=    [
             [-8.7839, -9.0056, -8.8121],
             [-7.4104, -7.0313, -6.5401],
             [-6.6105, -6.3427, -6.4675],
         ]
         snake_case					=    torch.tensor(__snake_case   ).to(__snake_case   )
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      , __snake_case      , atol=__snake_case   )   )
         # class_queries_logits
         snake_case					=    outputs.class_queries_logits
         self.assertEqual(class_queries_logits.shape      , (1, model.config.num_queries, model.config.num_labels + 1)   )
         snake_case					=    torch.tensor(
             [
                 [1.8324, -8.0835, -4.1922],
                 [0.8450, -9.0050, -3.6053],
                 [0.3045, -7.7293, -3.0275],
             ]   ).to(__snake_case   )
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      , __snake_case      , atol=__snake_case   )   )




    def 		a_      (	self   ):
         snake_case					=    MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(__snake_case   ).eval()
         snake_case					=    self.default_image_processor

         snake_case					=    image_processor(
             [np.zeros((3, 8_0_0, 1_3_3_3)   ), np.zeros((3, 8_0_0, 1_3_3_3)   )]      , segmentation_maps=[np.zeros((3_8_4, 3_8_4)   ).astype(np.floataa   ), np.zeros((3_8_4, 3_8_4)   ).astype(np.floataa   )]      , return_tensors='''pt'''      , )

         snake_case					=    inputs['''pixel_values'''].to(__snake_case   )
         snake_case					=    [el.to(__snake_case   ) for el in inputs['''mask_labels''']]
         snake_case					=    [el.to(__snake_case   ) for el in inputs['''class_labels''']]

         with torch.no_grad():
              snake_case					=    model(**__snake_case   )

         self.assertTrue(outputs.loss is not None   )








import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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__ (   snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		KandinskyImgaImgPipeline
    __magic_name__        =		['prompt', 'image_embeds', 'negative_image_embeds', 'image']
    __magic_name__        =		[
        'prompt',
        'negative_prompt',
        'image_embeds',
        'negative_image_embeds',
        'image',
    ]
    __magic_name__        =		[
        'generator',
        'height',
        'width',
        'strength',
        'guidance_scale',
        'negative_prompt',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    __magic_name__        =		False
    @property
    def 		a_      (	self   ):
         return 3_2
    @property
    def 		a_      (	self   ):
         return 3_2
    @property
    def 		a_      (	self   ):
         return self.time_input_dim
    @property
    def 		a_      (	self   ):
         return self.time_input_dim * 4
    @property
    def 		a_      (	self   ):
         return 1_0_0
    @property
    def 		a_      (	self   ):
         snake_case					=    XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''   )
         return tokenizer
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    MCLIPConfig(
             numDims=self.cross_attention_dim      , transformerDimensions=self.text_embedder_hidden_size      , hidden_size=self.text_embedder_hidden_size      , intermediate_size=3_7      , num_attention_heads=4      , num_hidden_layers=5      , vocab_size=1_0_0_5      , )

         snake_case					=    MultilingualCLIP(__snake_case   )
         snake_case					=    text_encoder.eval()

         return text_encoder
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )

         snake_case					=    {
             '''in_channels''': 4,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 8,
             '''addition_embed_type''': '''text_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''': '''text_image_proj''',
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': None,
         }

         snake_case					=    UNetaDConditionModel(**__snake_case   )
         return model
    @property
    def 		a_      (	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 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    VQModel(**self.dummy_movq_kwargs   )
         return model
    def 		a_      (	self   ):
         snake_case					=    self.dummy_text_encoder
         snake_case					=    self.dummy_tokenizer
         snake_case					=    self.dummy_unet
         snake_case					=    self.dummy_movq

         snake_case					=    {
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_schedule''': '''linear''',
             '''beta_start''': 0.0_0085,
             '''beta_end''': 0.012,
             '''clip_sample''': False,
             '''set_alpha_to_one''': False,
             '''steps_offset''': 0,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
         }

         snake_case					=    DDIMScheduler(**__snake_case   )

         snake_case					=    {
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components
    def 		a_      (	self      , __snake_case      , __snake_case=0   ):
         snake_case					=    floats_tensor((1, self.cross_attention_dim)      , rng=random.Random(__snake_case   )   ).to(__snake_case   )
         snake_case					=    floats_tensor((1, self.cross_attention_dim)      , rng=random.Random(seed + 1   )   ).to(__snake_case   )
         # create init_image
         snake_case					=    floats_tensor((1, 3, 6_4, 6_4)      , rng=random.Random(__snake_case   )   ).to(__snake_case   )
         snake_case					=    image.cpu().permute(0      , 2      , 3      , 1   )[0]
         snake_case					=    Image.fromarray(np.uinta(__snake_case   )   ).convert('''RGB'''   ).resize((2_5_6, 2_5_6)   )

         if str(__snake_case   ).startswith('''mps'''   ):
              snake_case					=    torch.manual_seed(__snake_case   )
         else:
              snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(__snake_case   )
         snake_case					=    {
             '''prompt''': '''horse''',
             '''image''': init_image,
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''generator''': generator,
             '''height''': 6_4,
             '''width''': 6_4,
             '''num_inference_steps''': 1_0,
             '''guidance_scale''': 7.0,
             '''strength''': 0.2,
             '''output_type''': '''np''',
         }
         return inputs




    def 		a_      (	self   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    pipe(**self.get_dummy_inputs(__snake_case   )   )
         snake_case					=    output.images

         snake_case					=    pipe(
             **self.get_dummy_inputs(__snake_case   )      , return_dict=__snake_case      , )[0]

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

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

         snake_case					=    np.array(
             [0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233]   )
         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   ):
         snake_case					=    load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinsky/kandinsky_img2img_frog.npy'''   )

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

         snake_case					=    KandinskyPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-prior'''      , torch_dtype=torch.floataa   )
         pipe_prior.to(__snake_case   )

         snake_case					=    KandinskyImgaImgPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1'''      , torch_dtype=torch.floataa   )
         snake_case					=    pipeline.to(__snake_case   )

         pipeline.set_progress_bar_config(disable=__snake_case   )

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

         snake_case					=    pipeline(
             __snake_case      , image=__snake_case      , image_embeds=__snake_case      , negative_image_embeds=__snake_case      , generator=__snake_case      , num_inference_steps=1_0_0      , height=7_6_8      , width=7_6_8      , strength=0.2      , output_type='''np'''      , )

         snake_case					=    output.images[0]

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

         assert_mean_pixel_difference(__snake_case      , __snake_case   )




