





"""simple docstring"""




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 					_UpperCAmelCase    (  UpperCAmelCase__     ,  unittest.TestCase						):



      '''simple docstring'''
      lowercase_ :					Tuple             =  UnCLIPPipeline
      lowercase_ :					Optional[Any]             =  TEXT_TO_IMAGE_PARAMS - {
          """negative_prompt""",
          """height""",
          """width""",
          """negative_prompt_embeds""",
          """guidance_scale""",
          """prompt_embeds""",
          """cross_attention_kwargs""",
      }
      lowercase_ :					Optional[int]             =  TEXT_TO_IMAGE_BATCH_PARAMS
      lowercase_ :					Any             =  [
          """generator""",
          """return_dict""",
          """prior_num_inference_steps""",
          """decoder_num_inference_steps""",
          """super_res_num_inference_steps""",
      ]
      lowercase_ :					Union[str, Any]             =  False

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return 3_2

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return 3_2

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self.time_input_dim

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self.time_input_dim * 4

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return 1_0_0

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )
        return tokenizer

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )
        A_    :			int									=    CLIPTextConfig(
            bos_token_id=0   , eos_token_id=2   , hidden_size=self.text_embedder_hidden_size   , projection_dim=self.text_embedder_hidden_size   , intermediate_size=3_7   , layer_norm_eps=1E-05   , num_attention_heads=4   , num_hidden_layers=5   , pad_token_id=1   , vocab_size=1_0_0_0   , )
        return CLIPTextModelWithProjection(snake_case_    )

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )

        A_    :			int									=    {
            'num_attention_heads': 2,
            'attention_head_dim': 1_2,
            'embedding_dim': self.text_embedder_hidden_size,
            'num_layers': 1,
        }

        A_    :			int									=    PriorTransformer(**snake_case_    )
        return model

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )

        A_    :			Optional[Any]									=    {
            'clip_embeddings_dim': self.text_embedder_hidden_size,
            'time_embed_dim': self.time_embed_dim,
            'cross_attention_dim': self.cross_attention_dim,
        }

        A_    :			Tuple									=    UnCLIPTextProjModel(**snake_case_    )
        return model

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )

        A_    :			str									=    {
            'sample_size': 3_2,
            # RGB in channels
            'in_channels': 3,
            # Out channels is double in channels because predicts mean and variance
            'out_channels': 6,
            '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,
            'cross_attention_dim': self.cross_attention_dim,
            'attention_head_dim': 4,
            'resnet_time_scale_shift': 'scale_shift',
            'class_embed_type': 'identity',
        }

        A_    :			Tuple									=    UNetaDConditionModel(**snake_case_    )
        return model

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return {
            "sample_size": 6_4,
            "layers_per_block": 1,
            "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
            "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "in_channels": 6,
            "out_channels": 3,
        }

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )

        A_    :			Union[str, Any]									=    UNetaDModel(**self.dummy_super_res_kwargs    )
        return model

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(1    )

        A_    :			Optional[int]									=    UNetaDModel(**self.dummy_super_res_kwargs    )
        return model

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    self.dummy_prior
        A_    :			Tuple									=    self.dummy_decoder
        A_    :			int									=    self.dummy_text_proj
        A_    :			Tuple									=    self.dummy_text_encoder
        A_    :			Optional[Any]									=    self.dummy_tokenizer
        A_    :			Any									=    self.dummy_super_res_first
        A_    :			Optional[Any]									=    self.dummy_super_res_last

        A_    :			Optional[int]									=    UnCLIPScheduler(
            variance_type='fixed_small_log'   , prediction_type='sample'   , num_train_timesteps=1_0_0_0   , clip_sample_range=5.0   , )

        A_    :			Tuple									=    UnCLIPScheduler(
            variance_type='learned_range'   , prediction_type='epsilon'   , num_train_timesteps=1_0_0_0   , )

        A_    :			Any									=    UnCLIPScheduler(
            variance_type='fixed_small_log'   , prediction_type='epsilon'   , num_train_timesteps=1_0_0_0   , )

        A_    :			int									=    {
            'prior': prior,
            'decoder': decoder,
            'text_proj': text_proj,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'super_res_first': super_res_first,
            'super_res_last': super_res_last,
            'prior_scheduler': prior_scheduler,
            'decoder_scheduler': decoder_scheduler,
            'super_res_scheduler': super_res_scheduler,
        }

        return components

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_=0    ):

        """simple docstring"""

        if str(snake_case_    ).startswith('mps'    ):
          A_    :			Tuple									=    torch.manual_seed(snake_case_    )
        else:
          A_    :			Dict									=    torch.Generator(device=snake_case_    ).manual_seed(snake_case_    )
        A_    :			Optional[int]									=    {
            'prompt': 'horse',
            'generator': generator,
            'prior_num_inference_steps': 2,
            'decoder_num_inference_steps': 2,
            'super_res_num_inference_steps': 2,
            'output_type': 'numpy',
        }
        return inputs

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[Any]									=    'cpu'

        A_    :			Optional[Any]									=    self.get_dummy_components()

        A_    :			Any									=    self.pipeline_class(**snake_case_    )
        A_    :			Dict									=    pipe.to(snake_case_    )

        pipe.set_progress_bar_config(disable=snake_case_    )

        A_    :			List[str]									=    pipe(**self.get_dummy_inputs(snake_case_    )    )
        A_    :			int									=    output.images

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

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

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

        A_    :			Union[str, Any]									=    np.array(
            [
                0.99_97,
                0.99_88,
                0.00_28,
                0.99_97,
                0.99_84,
                0.99_65,
                0.00_29,
                0.99_86,
                0.00_25,
            ]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[Any]									=    torch.device('cpu'    )

        class 					_UpperCAmelCase    :



              '''simple docstring'''
              lowercase_ :					Any             =  1


        A_    :			List[Any]									=    self.get_dummy_components()

        A_    :			List[str]									=    self.pipeline_class(**snake_case_    )
        A_    :			Any									=    pipe.to(snake_case_    )

        A_    :			int									=    components['prior']
        A_    :			Optional[int]									=    components['decoder']
        A_    :			Any									=    components['super_res_first']
        A_    :			List[Any]									=    components['tokenizer']
        A_    :			List[str]									=    components['text_encoder']

        A_    :			Any									=    torch.Generator(device=snake_case_    ).manual_seed(0    )
        A_    :			Optional[Any]									=    prior.dtype
        A_    :			Dict									=    1

        A_    :			Any									=    (batch_size, prior.config.embedding_dim)
        A_    :			List[Any]									=    pipe.prepare_latents(
            snake_case_   , dtype=snake_case_   , device=snake_case_   , generator=snake_case_   , latents=snake_case_   , scheduler=DummyScheduler()    )
        A_    :			Tuple									=    (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
        A_    :			Any									=    pipe.prepare_latents(
            snake_case_   , dtype=snake_case_   , device=snake_case_   , generator=snake_case_   , latents=snake_case_   , scheduler=DummyScheduler()    )

        A_    :			Any									=    (
            batch_size,
            super_res_first.config.in_channels // 2,
            super_res_first.config.sample_size,
            super_res_first.config.sample_size,
        )
        A_    :			Union[str, Any]									=    pipe.prepare_latents(
            snake_case_   , dtype=snake_case_   , device=snake_case_   , generator=snake_case_   , latents=snake_case_   , scheduler=DummyScheduler()    )

        pipe.set_progress_bar_config(disable=snake_case_    )

        A_    :			Union[str, Any]									=    'this is a prompt example'

        A_    :			int									=    torch.Generator(device=snake_case_    ).manual_seed(0    )
        A_    :			str									=    pipe(
            [prompt]   , generator=snake_case_   , prior_num_inference_steps=2   , decoder_num_inference_steps=2   , super_res_num_inference_steps=2   , prior_latents=snake_case_   , decoder_latents=snake_case_   , super_res_latents=snake_case_   , output_type='np'   , )
        A_    :			Any									=    output.images

        A_    :			Any									=    tokenizer(
            snake_case_   , padding='max_length'   , max_length=tokenizer.model_max_length   , return_tensors='pt'   , )
        A_    :			List[str]									=    text_encoder(text_inputs.input_ids    )
        A_    :			List[str]									=    text_inputs.attention_mask

        A_    :			int									=    torch.Generator(device=snake_case_    ).manual_seed(0    )
        A_    :			Optional[int]									=    pipe(
            generator=snake_case_   , prior_num_inference_steps=2   , decoder_num_inference_steps=2   , super_res_num_inference_steps=2   , prior_latents=snake_case_   , decoder_latents=snake_case_   , super_res_latents=snake_case_   , text_model_output=snake_case_   , text_attention_mask=snake_case_   , output_type='np'   , )[0]

        # make sure passing text embeddings manually is identical
        assert np.abs(image - image_from_text    ).max() < 1E-4

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    torch_device == 'cpu'

        self._test_attention_slicing_forward_pass(test_max_difference=snake_case_   , expected_max_diff=0.01    )

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    torch_device == 'cpu'
        A_    :			Optional[Any]									=    True
        A_    :			List[str]									=    [
            'prior_num_inference_steps',
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        self._test_inference_batch_single_identical(
            test_max_difference=snake_case_   , relax_max_difference=snake_case_   , additional_params_copy_to_batched_inputs=snake_case_   , )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    [
            'prior_num_inference_steps',
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        if torch_device == "mps":
          # TODO: MPS errors with larger batch sizes
          A_    :			List[str]									=    [2, 3]
          self._test_inference_batch_consistent(
              batch_sizes=snake_case_   , additional_params_copy_to_batched_inputs=snake_case_   , )
        else:
          self._test_inference_batch_consistent(
              additional_params_copy_to_batched_inputs=snake_case_    )

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return super().test_dict_tuple_outputs_equivalent()

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return super().test_save_load_local()

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return super().test_save_load_optional_components()


@nightly
class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/unclip/karlo_v1_alpha_horse_cpu.npy'    )

        A_    :			str									=    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'    )
        pipeline.set_progress_bar_config(disable=snake_case_    )

        A_    :			Any									=    torch.manual_seed(0    )
        A_    :			str									=    pipeline(
            'horse'   , num_images_per_prompt=1   , generator=snake_case_   , output_type='np'   , )

        A_    :			int									=    output.images[0]

        assert image.shape == (2_5_6, 2_5_6, 3)
        assert np.abs(expected_image - image    ).max() < 1E-1


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



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/unclip/karlo_v1_alpha_horse_fp16.npy'    )

        A_    :			int									=    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'   , torch_dtype=torch.floataa    )
        A_    :			str									=    pipeline.to(snake_case_    )
        pipeline.set_progress_bar_config(disable=snake_case_    )

        A_    :			List[str]									=    torch.Generator(device='cpu'    ).manual_seed(0    )
        A_    :			Any									=    pipeline(
            'horse'   , generator=snake_case_   , output_type='np'   , )

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

        assert image.shape == (2_5_6, 2_5_6, 3)

        assert_mean_pixel_difference(snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        A_    :			List[Any]									=    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'   , torch_dtype=torch.floataa    )
        A_    :			Dict									=    pipe.to(snake_case_    )
        pipe.set_progress_bar_config(disable=snake_case_    )
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        A_    :			Union[str, Any]									=    pipe(
            'horse'   , num_images_per_prompt=1   , prior_num_inference_steps=2   , decoder_num_inference_steps=2   , super_res_num_inference_steps=2   , output_type='np'   , )

        A_    :			str									=    torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 1_0**9






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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 	_UpperCAmelCase						( _lowercase			,							unittest.TestCase       ):
					__A        = KandinskyPipeline
					__A        = [
					    "prompt",
					    "image_embeds",
					    "negative_image_embeds",
					]
					__A        = ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
					__A        = [
					    "generator",
					    "height",
					    "width",
					    "latents",
					    "guidance_scale",
					    "negative_prompt",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					__A        = False




					@property
					def 						snake_case_		(    self							)    ->	Any:
							return 32




					@property
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							return 32




					@property
					def 						snake_case_		(    self							)    ->	List[Any]:
							return self.time_input_dim




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							return self.time_input_dim * 4




					@property
					def 						snake_case_		(    self							)    ->	Dict:
							return 100




					@property
					def 						snake_case_		(    self							)    ->	List[str]:
							UpperCamelCase__								=						XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""							)
							return tokenizer




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							torch.manual_seed(0							)
							UpperCamelCase__								=						MCLIPConfig(
							    numDims=self.cross_attention_dim     ,   transformerDimensions=self.text_embedder_hidden_size     ,   hidden_size=self.text_embedder_hidden_size     ,   intermediate_size=37     ,   num_attention_heads=4     ,   num_hidden_layers=5     ,   vocab_size=1_005     ,   )

							UpperCamelCase__								=						MultilingualCLIP(__lowerCAmelCase							)
							UpperCamelCase__								=						text_encoder.eval()

							return text_encoder




					@property
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							torch.manual_seed(0							)

							UpperCamelCase__								=						{
							    """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,
							}

							UpperCamelCase__								=						UNetaDConditionModel(**__lowerCAmelCase							)
							return model




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							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 						snake_case_		(    self							)    ->	Optional[int]:
							torch.manual_seed(0							)
							UpperCamelCase__								=						VQModel(**self.dummy_movq_kwargs							)
							return model




					def 						snake_case_		(    self							)    ->	Dict:
							UpperCamelCase__								=						self.dummy_text_encoder
							UpperCamelCase__								=						self.dummy_tokenizer
							UpperCamelCase__								=						self.dummy_unet
							UpperCamelCase__								=						self.dummy_movq

							UpperCamelCase__								=						DDIMScheduler(
							    num_train_timesteps=1_000     ,   beta_schedule="""linear"""     ,   beta_start=0.0_0_0_8_5     ,   beta_end=0.0_1_2     ,   clip_sample=__lowerCAmelCase     ,   set_alpha_to_one=__lowerCAmelCase     ,   steps_offset=1     ,   prediction_type="""epsilon"""     ,   thresholding=__lowerCAmelCase     ,   )

							UpperCamelCase__								=						{
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}
							return components




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=0							)    ->	str:
							UpperCamelCase__								=						floats_tensor((1, self.cross_attention_dim)     ,   rng=random.Random(__lowerCAmelCase							)							).to(__lowerCAmelCase							)
							UpperCamelCase__								=						floats_tensor((1, self.cross_attention_dim)     ,   rng=random.Random(seed + 1							)							).to(__lowerCAmelCase							)
							if str(__lowerCAmelCase							).startswith("""mps"""							):
									UpperCamelCase__								=						torch.manual_seed(__lowerCAmelCase							)
							else:
									UpperCamelCase__								=						torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)
							UpperCamelCase__								=						{
							    """prompt""": """horse""",
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """guidance_scale""": 4.0,
							    """num_inference_steps""": 2,
							    """output_type""": """np""",
							}
							return inputs




					def 						snake_case_		(    self							)    ->	Tuple:
							UpperCamelCase__								=						"""cpu"""

							UpperCamelCase__								=						self.get_dummy_components()

							UpperCamelCase__								=						self.pipeline_class(**__lowerCAmelCase							)
							UpperCamelCase__								=						pipe.to(__lowerCAmelCase							)

							pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

							UpperCamelCase__								=						pipe(**self.get_dummy_inputs(__lowerCAmelCase							)							)
							UpperCamelCase__								=						output.images

							UpperCamelCase__								=						pipe(
							    **self.get_dummy_inputs(__lowerCAmelCase							)     ,   return_dict=__lowerCAmelCase     ,   )[0]

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

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

							UpperCamelCase__								=						np.array(
							    [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_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 	_UpperCAmelCase						( unittest.TestCase       ):




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




					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__								=						load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""							)

							UpperCamelCase__								=						KandinskyPriorPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-prior"""     ,   torch_dtype=torch.floataa							)
							pipe_prior.to(__lowerCAmelCase							)

							UpperCamelCase__								=						KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""     ,   torch_dtype=torch.floataa							)
							UpperCamelCase__								=						pipeline.to(__lowerCAmelCase							)
							pipeline.set_progress_bar_config(disable=__lowerCAmelCase							)

							UpperCamelCase__								=						"""red cat, 4k photo"""

							UpperCamelCase__								=						torch.Generator(device="""cuda"""							).manual_seed(0							)
							UpperCamelCase__	,      UpperCamelCase__								=						pipe_prior(
							    __lowerCAmelCase     ,   generator=__lowerCAmelCase     ,   num_inference_steps=5     ,   negative_prompt=""""""     ,   ).to_tuple()

							UpperCamelCase__								=						torch.Generator(device="""cuda"""							).manual_seed(0							)
							UpperCamelCase__								=						pipeline(
							    __lowerCAmelCase     ,   image_embeds=__lowerCAmelCase     ,   negative_image_embeds=__lowerCAmelCase     ,   generator=__lowerCAmelCase     ,   num_inference_steps=100     ,   output_type="""np"""     ,   )

							UpperCamelCase__								=						output.images[0]

							assert image.shape == (512, 512, 3)

							assert_mean_pixel_difference(__lowerCAmelCase     ,   __lowerCAmelCase							)





