code: 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_				(							_a     ,     unittest.TestCase    ):
      UpperCAmelCase_			:       Optional[Any]						 =							KandinskyPipeline
      UpperCAmelCase_			:       Dict						 =							[
          'prompt',
          'image_embeds',
          'negative_image_embeds',
      ]
      UpperCAmelCase_			:       List[Any]						 =							['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
      UpperCAmelCase_			:       int						 =							[
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'negative_prompt',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      UpperCAmelCase_			:       Optional[Any]						 =							False
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   str   )	-> Tuple:
        return 32
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   List[str]   )	-> str:
        return 32
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   str   )	-> str:
        return self.time_input_dim
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   List[str]   )	-> Tuple:
        return self.time_input_dim * 4
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   Optional[int]   )	-> Dict:
        return 100
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   List[Any]   )	-> Any:
        a__     		=     XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"   )
        return tokenizer
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   str   )	-> Tuple:
        torch.manual_seed(0   )
        a__     		=     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=1005      ,      )
  
        a__     		=     MultilingualCLIP(lowerCAmelCase   )
        a__     		=     text_encoder.eval()
  
        return text_encoder
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   int   )	-> List[Any]:
        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": "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,
        }
  
        a__     		=     UNetaDConditionModel(**lowerCAmelCase   )
        return model
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   List[Any]   )	-> Optional[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 						__lowerCamelCase (					self  :   str   )	-> int:
        torch.manual_seed(0   )
        a__     		=     VQModel(**self.dummy_movq_kwargs   )
        return model
  
  
  
  
      def 						__lowerCamelCase (					self  :   Dict   )	-> int:
        a__     		=     self.dummy_text_encoder
        a__     		=     self.dummy_tokenizer
        a__     		=     self.dummy_unet
        a__     		=     self.dummy_movq
  
        a__     		=     DDIMScheduler(
            num_train_timesteps=1000      ,      beta_schedule="linear"      ,      beta_start=0.00_085      ,      beta_end=0.012      ,      clip_sample=lowerCAmelCase      ,      set_alpha_to_one=lowerCAmelCase      ,      steps_offset=1      ,      prediction_type="epsilon"      ,      thresholding=lowerCAmelCase      ,      )
  
        a__     		=     {
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "unet": unet,
            "scheduler": scheduler,
            "movq": movq,
        }
        return components
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[Any]      ,      lowerCAmelCase  :   int      ,      lowerCAmelCase  :   Optional[Any]=0   )	-> Union[str, Any]:
        a__     		=     floats_tensor((1, self.cross_attention_dim)      ,      rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
        a__     		=     floats_tensor((1, self.cross_attention_dim)      ,      rng=random.Random(seed + 1   )   ).to(lowerCAmelCase   )
        if str(lowerCAmelCase   ).startswith("mps"   ):
          a__     		=     torch.manual_seed(lowerCAmelCase   )
        else:
          a__     		=     torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
        a__     		=     {
            "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 						__lowerCamelCase (					self  :   str   )	-> Optional[int]:
        a__     		=     "cpu"
  
        a__     		=     self.get_dummy_components()
  
        a__     		=     self.pipeline_class(**lowerCAmelCase   )
        a__     		=     pipe.to(lowerCAmelCase   )
  
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )
  
        a__     		=     pipe(**self.get_dummy_inputs(lowerCAmelCase   )   )
        a__     		=     output.images
  
        a__     		=     pipe(
            **self.get_dummy_inputs(lowerCAmelCase   )      ,      return_dict=lowerCAmelCase      ,      )[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(
            [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]   )
  
        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 						__lowerCamelCase (					self  :   str   )	-> List[str]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> Optional[Any]:
        a__     		=     load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinsky/kandinsky_text2img_cat_fp16.npy"   )
  
        a__     		=     KandinskyPriorPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-1-prior"      ,      torch_dtype=torch.floataa   )
        pipe_prior.to(lowerCAmelCase   )
  
        a__     		=     KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"      ,      torch_dtype=torch.floataa   )
        a__     		=     pipeline.to(lowerCAmelCase   )
        pipeline.set_progress_bar_config(disable=lowerCAmelCase   )
  
        a__     		=     "red cat, 4k photo"
  
        a__     		=     torch.Generator(device="cuda"   ).manual_seed(0   )
        a__		,a__     		=     pipe_prior(
            lowerCAmelCase      ,      generator=lowerCAmelCase      ,      num_inference_steps=5      ,      negative_prompt=""      ,      ).to_tuple()
  
        a__     		=     torch.Generator(device="cuda"   ).manual_seed(0   )
        a__     		=     pipeline(
            lowerCAmelCase      ,      image_embeds=lowerCAmelCase      ,      negative_image_embeds=lowerCAmelCase      ,      generator=lowerCAmelCase      ,      num_inference_steps=100      ,      output_type="np"      ,      )
  
        a__     		=     output.images[0]
  
        assert image.shape == (512, 512, 3)
  
        assert_mean_pixel_difference(lowerCAmelCase      ,      lowerCAmelCase   )
  
  
style_context: from unittest.mock import patch
  
  import datasets
  from datasets import Dataset
  def 				__lowercase	(						)				->Union[str, Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     Dataset.from_dict({"col_1": [3, 2, 0, 1]}					)
  
    with patch("tqdm.auto.tqdm"					) as mock_tqdm:
      datasets.disable_progress_bar()
      dset.map(lambda lowerCamelCase__					: {"col_2": x["col_1"] + 1}					)
      mock_tqdm.assert_not_called()
  
      mock_tqdm.reset_mock()
  
      datasets.enable_progress_bar()
      dset.map(lambda lowerCamelCase__					: {"col_2": x["col_1"] + 1}					)
      mock_tqdm.assert_called()
  
  