code: 
  """simple docstring"""
  
  
  
  
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DiffusionPipeline,
      EulerDiscreteScheduler,
      StableDiffusionXLPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  class A_			(lowercase__       ,lowercase__       ,unittest.TestCase   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		Optional[Any]						  =	StableDiffusionXLPipeline
    SCREAMING_SNAKE_CASE__    :		List[str]						  =	TEXT_TO_IMAGE_PARAMS
    SCREAMING_SNAKE_CASE__    :		Tuple						  =	TEXT_TO_IMAGE_BATCH_PARAMS
    SCREAMING_SNAKE_CASE__    :		int						  =	TEXT_TO_IMAGE_IMAGE_PARAMS
    SCREAMING_SNAKE_CASE__    :		List[str]						  =	TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       torch.manual_seed(0						)
       UpperCAmelCase_      :      Optional[Any]      =					UNetaDConditionModel(
           block_out_channels=(32, 64)       ,     layers_per_block=2       ,     sample_size=32       ,     in_channels=4       ,     out_channels=4       ,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,     attention_head_dim=(2, 4)       ,     use_linear_projection=lowercase_       ,     addition_embed_type="text_time"       ,     addition_time_embed_dim=8       ,     transformer_layers_per_block=(1, 2)       ,     projection_class_embeddings_input_dim=80       ,     cross_attention_dim=64       ,     )
       UpperCAmelCase_      :      Union[str, Any]      =					EulerDiscreteScheduler(
           beta_start=0.0_00_85       ,     beta_end=0.0_12       ,     steps_offset=1       ,     beta_schedule="scaled_linear"       ,     timestep_spacing="leading"       ,     )
       torch.manual_seed(0						)
       UpperCAmelCase_      :      Tuple      =					AutoencoderKL(
           block_out_channels=[32, 64]       ,     in_channels=3       ,     out_channels=3       ,     down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]       ,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]       ,     latent_channels=4       ,     sample_size=128       ,     )
       torch.manual_seed(0						)
       UpperCAmelCase_      :      Dict      =					CLIPTextConfig(
           bos_token_id=0       ,     eos_token_id=2       ,     hidden_size=32       ,     intermediate_size=37       ,     layer_norm_eps=1E-0_5       ,     num_attention_heads=4       ,     num_hidden_layers=5       ,     pad_token_id=1       ,     vocab_size=1000       ,     hidden_act="gelu"       ,     projection_dim=32       ,     )
       UpperCAmelCase_      :      Optional[int]      =					CLIPTextModel(lowercase_						)
       UpperCAmelCase_      :      Any      =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       ,     local_files_only=lowercase_						)
  
       UpperCAmelCase_      :      Union[str, Any]      =					CLIPTextModelWithProjection(lowercase_						)
       UpperCAmelCase_      :      int      =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       ,     local_files_only=lowercase_						)
  
       UpperCAmelCase_      :      List[str]      =					{
           "unet": unet,
           "scheduler": scheduler,
           "vae": vae,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "text_encoder_2": text_encoder_a,
           "tokenizer_2": tokenizer_a,
           # "safety_checker": None,
           # "feature_extractor": None,
       }
       return components
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_=0						):
  
       """simple docstring"""
       if str(lowercase_						).startswith("mps"						):
          UpperCAmelCase_      :      Optional[Any]      =					torch.manual_seed(lowercase_						)
       else:
          UpperCAmelCase_      :      Optional[int]      =					torch.Generator(device=lowercase_						).manual_seed(lowercase_						)
       UpperCAmelCase_      :      List[str]      =					{
           "prompt": "A painting of a squirrel eating a burger",
           "generator": generator,
           "num_inference_steps": 2,
           "guidance_scale": 5.0,
           "output_type": "numpy",
       }
       return inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      int      =					"cpu"  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_      :      Optional[Any]      =					self.get_dummy_components()
       UpperCAmelCase_      :      Dict      =					StableDiffusionXLPipeline(**lowercase_						)
       UpperCAmelCase_      :      List[Any]      =					sd_pipe.to(lowercase_						)
       sd_pipe.set_progress_bar_config(disable=lowercase_						)
  
       UpperCAmelCase_      :      Dict      =					self.get_dummy_inputs(lowercase_						)
       UpperCAmelCase_      :      Tuple      =					sd_pipe(**lowercase_						).images
       UpperCAmelCase_      :      int      =					image[0, -3:, -3:, -1]
  
       assert image.shape == (1, 64, 64, 3)
       UpperCAmelCase_      :      int      =					np.array([0.58_73, 0.61_28, 0.47_97, 0.51_22, 0.56_74, 0.46_39, 0.52_27, 0.51_49, 0.47_47]						)
  
       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      int      =					self.get_dummy_components()
       UpperCAmelCase_      :      Union[str, Any]      =					StableDiffusionXLPipeline(**lowercase_						)
       UpperCAmelCase_      :      Optional[Any]      =					sd_pipe.to(lowercase_						)
       UpperCAmelCase_      :      Any      =					sd_pipe.to(lowercase_						)
       sd_pipe.set_progress_bar_config(disable=lowercase_						)
  
       # forward without prompt embeds
       UpperCAmelCase_      :      List[Any]      =					self.get_dummy_inputs(lowercase_						)
       UpperCAmelCase_      :      int      =					3 * ["this is a negative prompt"]
       UpperCAmelCase_      :      str      =					negative_prompt
       UpperCAmelCase_      :      Union[str, Any]      =					3 * [inputs["prompt"]]
  
       UpperCAmelCase_      :      str      =					sd_pipe(**lowercase_						)
       UpperCAmelCase_      :      List[Any]      =					output.images[0, -3:, -3:, -1]
  
       # forward with prompt embeds
       UpperCAmelCase_      :      Dict      =					self.get_dummy_inputs(lowercase_						)
       UpperCAmelCase_      :      Union[str, Any]      =					3 * ["this is a negative prompt"]
       UpperCAmelCase_      :      Union[str, Any]      =					3 * [inputs.pop("prompt"						)]
  
       (
           (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     (
           UpperCAmelCase_
       )  ,     
       )      :      Dict      =					sd_pipe.encode_prompt(lowercase_       ,     negative_prompt=lowercase_						)
  
       UpperCAmelCase_      :      str      =					sd_pipe(
           **lowercase_       ,     prompt_embeds=lowercase_       ,     negative_prompt_embeds=lowercase_       ,     pooled_prompt_embeds=lowercase_       ,     negative_pooled_prompt_embeds=lowercase_       ,     )
       UpperCAmelCase_      :      Any      =					output.images[0, -3:, -3:, -1]
  
       # make sure that it's equal
       assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()						).max() < 1E-4
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       super().test_attention_slicing_forward_pass(expected_max_diff=3E-3						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       super().test_inference_batch_single_identical(expected_max_diff=3E-3						)
  
  
  
  
  @slow
  @require_torch_gpu
  class A_			(unittest.TestCase   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_="cpu"       ,     lowercase_=torch.floataa       ,     lowercase_=0						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Any      =					torch.Generator(device=lowercase_						).manual_seed(lowercase_						)
       UpperCAmelCase_      :      Optional[Any]      =					np.random.RandomState(lowercase_						).standard_normal((1, 4, 64, 64)						)
       UpperCAmelCase_      :      int      =					torch.from_numpy(lowercase_						).to(device=lowercase_       ,     dtype=lowercase_						)
       UpperCAmelCase_      :      int      =					{
           "prompt": "a photograph of an astronaut riding a horse",
           "latents": latents,
           "generator": generator,
           "num_inference_steps": 3,
           "guidance_scale": 7.5,
           "output_type": "numpy",
       }
       return inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      int      =					DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"						)
       pipe.to(lowercase_						)
       pipe.set_progress_bar_config(disable=lowercase_						)
  
       UpperCAmelCase_      :      Optional[int]      =					self.get_inputs(lowercase_						)
       UpperCAmelCase_      :      Optional[Any]      =					pipe(**lowercase_						).images
       UpperCAmelCase_      :      int      =					image[0, -3:, -3:, -1].flatten()
  
       assert image.shape == (1, 512, 512, 3)
       UpperCAmelCase_      :      Optional[int]      =					np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]						)
       assert np.abs(image_slice - expected_slice						).max() < 7E-3
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from string import ascii_uppercase
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase = False,	__lowerCamelCase = ascii_uppercase						):
     UpperCAmelCase_      :      List[str]      =					keyword.upper()
     UpperCAmelCase_      :      str      =					plaintext.upper()
     UpperCAmelCase_      :      Tuple      =					set(__lowerCamelCase						)
  
     # create a list of unique characters in the keyword - their order matters
     # it determines how we will map plaintext characters to the ciphertext
     UpperCAmelCase_      :      Tuple      =					[]
     for char in keyword:
        if char in alphabet_set and char not in unique_chars:
           unique_chars.append(__lowerCamelCase						)
      # the number of those unique characters will determine the number of rows
     UpperCAmelCase_      :      Union[str, Any]      =					len(__lowerCamelCase						)
  
     # create a shifted version of the alphabet
     UpperCAmelCase_      :      Tuple      =					unique_chars + [
         char for char in alphabet if char not in unique_chars
     ]
  
     # create a modified alphabet by splitting the shifted alphabet into rows
     UpperCAmelCase_      :      List[str]      =					[
         shifted_alphabet[k : k + num_unique_chars_in_keyword]
         for k in range(0,	26,	__lowerCamelCase						)
     ]
  
     # map the alphabet characters to the modified alphabet characters
     # going 'vertically' through the modified alphabet - consider columns first
     UpperCAmelCase_      :      Tuple      =					{}
     UpperCAmelCase_      :      Dict      =					0
     for column in range(__lowerCamelCase						):
        for row in modified_alphabet:
           # if current row (the last one) is too short, break out of loop
           if len(__lowerCamelCase						) <= column:
              break
  
           # map current letter to letter in modified alphabet
           UpperCAmelCase_      :      Tuple      =					row[column]
           letter_index += 1
  
     if verbose:
        print(__lowerCamelCase						)
     # create the encrypted text by mapping the plaintext to the modified alphabet
     return "".join(mapping[char] if char in mapping else char for char in plaintext						)
  
  
  if __name__ == "__main__":
      # example use
      print(mixed_keyword('college', 'UNIVERSITY'))
  
  
  