code: 
  
  
  '''simple docstring'''
  
  
  
  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_						(_UpperCAmelCase      ,	unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Dict							  =    KandinskyPipeline
      lowerCamelCase						:      Tuple							  =    [
          'prompt',
          'image_embeds',
          'negative_image_embeds',
      ]
      lowerCamelCase						:      Optional[Any]							  =    ['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
      lowerCamelCase						:      Tuple							  =    [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'negative_prompt',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      lowerCamelCase						:      Union[str, Any]							  =    False
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  int:
           return 32
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  Dict:
           return 32
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  str:
           return self.time_input_dim
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  Tuple:
           return self.time_input_dim * 4
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  Dict:
           return 1_00
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  str:
           __lowerCamelCase     :			int              =	XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'			)
           return tokenizer
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  int:
           torch.manual_seed(0			)
           __lowerCamelCase     :			int              =	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=10_05    ,  )
  
           __lowerCamelCase     :			Any              =	MultilingualCLIP(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			str              =	text_encoder.eval()
  
           return text_encoder
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           torch.manual_seed(0			)
  
           __lowerCamelCase     :			Optional[int]              =	{
               '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,
           }
  
           __lowerCamelCase     :			str              =	UNetaDConditionModel(**SCREAMING_SNAKE_CASE_			)
           return model
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  List[str]:
           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    lowercase_       (							self			)       ->  Any:
           torch.manual_seed(0			)
           __lowerCamelCase     :			Optional[int]              =	VQModel(**self.dummy_movq_kwargs			)
           return model
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Tuple:
           __lowerCamelCase     :			List[str]              =	self.dummy_text_encoder
           __lowerCamelCase     :			int              =	self.dummy_tokenizer
           __lowerCamelCase     :			List[str]              =	self.dummy_unet
           __lowerCamelCase     :			Dict              =	self.dummy_movq
  
           __lowerCamelCase     :			Optional[Any]              =	DDIMScheduler(
               num_train_timesteps=10_00    ,  beta_schedule='linear'    ,  beta_start=0.0_0_0_8_5    ,  beta_end=0.0_1_2    ,  clip_sample=SCREAMING_SNAKE_CASE_    ,  set_alpha_to_one=SCREAMING_SNAKE_CASE_    ,  steps_offset=1    ,  prediction_type='epsilon'    ,  thresholding=SCREAMING_SNAKE_CASE_    ,  )
  
           __lowerCamelCase     :			List[str]              =	{
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'unet': unet,
               'scheduler': scheduler,
               'movq': movq,
           }
           return components
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=0			)       ->  Dict:
           __lowerCamelCase     :			Any              =	floats_tensor((1, self.cross_attention_dim)    ,  rng=random.Random(SCREAMING_SNAKE_CASE_			)			).to(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Optional[int]              =	floats_tensor((1, self.cross_attention_dim)    ,  rng=random.Random(seed + 1			)			).to(SCREAMING_SNAKE_CASE_			)
           if str(SCREAMING_SNAKE_CASE_			).startswith('mps'			):
                __lowerCamelCase     :			Tuple              =	torch.manual_seed(SCREAMING_SNAKE_CASE_			)
           else:
                __lowerCamelCase     :			str              =	torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Optional[Any]              =	{
               '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    lowercase_       (							self			)       ->  Optional[int]:
           __lowerCamelCase     :			Any              =	'cpu'
  
           __lowerCamelCase     :			List[str]              =	self.get_dummy_components()
  
           __lowerCamelCase     :			List[str]              =	self.pipeline_class(**SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Tuple              =	pipe.to(SCREAMING_SNAKE_CASE_			)
  
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			str              =	pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			List[str]              =	output.images
  
           __lowerCamelCase     :			Optional[int]              =	pipe(
               **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)    ,  return_dict=SCREAMING_SNAKE_CASE_    ,  )[0]
  
           __lowerCamelCase     :			List[Any]              =	image[0, -3:, -3:, -1]
           __lowerCamelCase     :			List[str]              =	image_from_tuple[0, -3:, -3:, -1]
  
           assert image.shape == (1, 64, 64, 3)
  
           __lowerCamelCase     :			Tuple              =	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						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           __lowerCamelCase     :			str              =	load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/kandinsky/kandinsky_text2img_cat_fp16.npy'			)
  
           __lowerCamelCase     :			Optional[int]              =	KandinskyPriorPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-1-prior'    ,  torch_dtype=torch.floataa			)
           pipe_prior.to(SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Dict              =	KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'    ,  torch_dtype=torch.floataa			)
           __lowerCamelCase     :			int              =	pipeline.to(SCREAMING_SNAKE_CASE_			)
           pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Optional[int]              =	'red cat, 4k photo'
  
           __lowerCamelCase     :			Any              =	torch.Generator(device='cuda'			).manual_seed(0			)
           __lowerCamelCase       , __lowerCamelCase     :			int              =	pipe_prior(
               SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_    ,  num_inference_steps=5    ,  negative_prompt=''    ,  ).to_tuple()
  
           __lowerCamelCase     :			Tuple              =	torch.Generator(device='cuda'			).manual_seed(0			)
           __lowerCamelCase     :			Tuple              =	pipeline(
               SCREAMING_SNAKE_CASE_    ,  image_embeds=SCREAMING_SNAKE_CASE_    ,  negative_image_embeds=SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_    ,  num_inference_steps=1_00    ,  output_type='np'    ,  )
  
           __lowerCamelCase     :			Optional[int]              =	output.images[0]
  
           assert image.shape == (5_12, 5_12, 3)
  
           assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import Callable, Optional, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  A__	:      Any				=						logging.get_logger(__name__)
  
  A__	:      str				=						{
      """microsoft/prophetnet-large-uncased""": (
          """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
      ),
  }
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Dict							  =    'prophetnet'
      lowerCamelCase						:      List[str]							  =    ['past_key_values']
      lowerCamelCase						:      List[str]							  =    {
          'num_attention_heads': 'num_encoder_attention_heads',
      }
  
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = "gelu"    ,  SCREAMING_SNAKE_CASE_ = 3_05_22    ,  SCREAMING_SNAKE_CASE_ = 10_24    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 5_12    ,  SCREAMING_SNAKE_CASE_ = 0.0_2    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 2    ,  SCREAMING_SNAKE_CASE_ = 32    ,  SCREAMING_SNAKE_CASE_ = 1_28    ,  SCREAMING_SNAKE_CASE_ = False    ,  SCREAMING_SNAKE_CASE_ = 0.0    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 1    ,  SCREAMING_SNAKE_CASE_ = 2    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  List[Any]:
           __lowerCamelCase     :			List[Any]              =	vocab_size
           __lowerCamelCase     :			Union[str, Any]              =	hidden_size
           __lowerCamelCase     :			Tuple              =	encoder_ffn_dim
           __lowerCamelCase     :			Any              =	num_encoder_layers
           __lowerCamelCase     :			int              =	num_encoder_attention_heads
           __lowerCamelCase     :			Optional[Any]              =	decoder_ffn_dim
           __lowerCamelCase     :			str              =	num_decoder_layers
           __lowerCamelCase     :			int              =	num_decoder_attention_heads
           __lowerCamelCase     :			Union[str, Any]              =	max_position_embeddings
           __lowerCamelCase     :			Optional[Any]              =	init_std  # Normal(0, this parameter)
           __lowerCamelCase     :			Union[str, Any]              =	activation_function
  
           # parameters for prophetnet
           __lowerCamelCase     :			int              =	ngram
           __lowerCamelCase     :			Optional[int]              =	num_buckets
           __lowerCamelCase     :			str              =	relative_max_distance
           __lowerCamelCase     :			Optional[Any]              =	disable_ngram_loss
           __lowerCamelCase     :			str              =	eps
  
           # 3 Types of Dropout
           __lowerCamelCase     :			Optional[Any]              =	attention_dropout
           __lowerCamelCase     :			str              =	activation_dropout
           __lowerCamelCase     :			Optional[Any]              =	dropout
  
           __lowerCamelCase     :			Optional[Any]              =	use_cache
  
           super().__init__(
               pad_token_id=SCREAMING_SNAKE_CASE_    ,  bos_token_id=SCREAMING_SNAKE_CASE_    ,  eos_token_id=SCREAMING_SNAKE_CASE_    ,  is_encoder_decoder=SCREAMING_SNAKE_CASE_    ,  add_cross_attention=SCREAMING_SNAKE_CASE_    ,  decoder_start_token_id=SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_    ,  )
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  int:
           return self.num_encoder_layers + self.num_decoder_layers
  
  
  
  
  
  
  
      @num_hidden_layers.setter
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Union[str, Any]:
           raise NotImplementedError(
               'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
               ' `num_decoder_layers`.'			)
  
  
  
  
  
  
  