code: 
  
  
  
  
  
  import unittest
  
  import numpy as np
  import torch
  from torch import nn
  from transformers import (
      CLIPImageProcessor,
      CLIPTextConfig,
      CLIPTextModelWithProjection,
      CLIPTokenizer,
      CLIPVisionConfig,
      CLIPVisionModelWithProjection,
  )
  
  from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
  from diffusers.utils import torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
  
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__		,       unittest.TestCase):
        _lowercase       :      List[Any]        =    KandinskyPriorPipeline
        _lowercase       :      Any        =    ["""prompt"""]
        _lowercase       :      int        =    ["""prompt""", """negative_prompt"""]
        _lowercase       :      Any        =    [
            """num_images_per_prompt""",
            """generator""",
            """num_inference_steps""",
            """latents""",
            """negative_prompt""",
            """guidance_scale""",
            """output_type""",
            """return_dict""",
        ]
        _lowercase       :      List[Any]        =    False
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return 3_2
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return 3_2
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Tuple:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.time_input_dim
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.time_input_dim * 4
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return 1_0_0
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Optional[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Union[str, Any]            =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)
          return tokenizer
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          torch.manual_seed(0							)
          a__			:      str            =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(lowerCAmelCase__							)
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Optional[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          torch.manual_seed(0							)
  
          a__			:      Optional[int]            ={
              "num_attention_heads": 2,
              "attention_head_dim": 1_2,
              "embedding_dim": self.text_embedder_hidden_size,
              "num_layers": 1,
          }
  
          a__			:      Any            =PriorTransformer(**lowerCAmelCase__							)
          # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
          a__			:      List[Any]            =nn.Parameter(torch.ones(model.clip_std.shape							)							)
          return model
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          torch.manual_seed(0							)
          a__			:      List[Any]            =CLIPVisionConfig(
              hidden_size=self.text_embedder_hidden_size	,   image_size=2_2_4	,   projection_dim=self.text_embedder_hidden_size	,   intermediate_size=3_7	,   num_attention_heads=4	,   num_channels=3	,   num_hidden_layers=5	,   patch_size=1_4	,   )
  
          a__			:      List[str]            =CLIPVisionModelWithProjection(lowerCAmelCase__							)
          return model
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Optional[int]            =CLIPImageProcessor(
              crop_size=2_2_4	,   do_center_crop=lowerCAmelCase__	,   do_normalize=lowerCAmelCase__	,   do_resize=lowerCAmelCase__	,   image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]	,   image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]	,   resample=3	,   size=2_2_4	,   )
  
          return image_processor
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Any            =self.dummy_prior
          a__			:      List[Any]            =self.dummy_image_encoder
          a__			:      List[Any]            =self.dummy_text_encoder
          a__			:      Optional[int]            =self.dummy_tokenizer
          a__			:      Union[str, Any]            =self.dummy_image_processor
  
          a__			:      List[str]            =UnCLIPScheduler(
              variance_type="fixed_small_log"	,   prediction_type="sample"	,   num_train_timesteps=1_0_0_0	,   clip_sample=lowerCAmelCase__	,   clip_sample_range=10.0	,   )
  
          a__			:      Optional[Any]            ={
              "prior": prior,
              "image_encoder": image_encoder,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "scheduler": scheduler,
              "image_processor": image_processor,
          }
  
          return components
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=0							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          if str(lowerCAmelCase__							).startswith("mps"							):
            a__			:      List[str]            =torch.manual_seed(lowerCAmelCase__							)
          else:
            a__			:      str            =torch.Generator(device=lowerCAmelCase__							).manual_seed(lowerCAmelCase__							)
          a__			:      Tuple            ={
              "prompt": "horse",
              "generator": generator,
              "guidance_scale": 4.0,
              "num_inference_steps": 2,
              "output_type": "np",
          }
          return inputs
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            ="cpu"
  
          a__			:      Dict            =self.get_dummy_components()
  
          a__			:      Optional[Any]            =self.pipeline_class(**lowerCAmelCase__							)
          a__			:      str            =pipe.to(lowerCAmelCase__							)
  
          pipe.set_progress_bar_config(disable=lowerCAmelCase__							)
  
          a__			:      Dict            =pipe(**self.get_dummy_inputs(lowerCAmelCase__							)							)
          a__			:      str            =output.image_embeds
  
          a__			:      List[Any]            =pipe(
              **self.get_dummy_inputs(lowerCAmelCase__							)	,   return_dict=lowerCAmelCase__	,   )[0]
  
          a__			:      int            =image[0, -1_0:]
          a__			:      Tuple            =image_from_tuple[0, -1_0:]
  
          assert image.shape == (1, 3_2)
  
          a__			:      Any            =np.array(
              [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56]							)
  
          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
  
  
  
  
  
  
  
        @skip_mps
        def      _lowercase						(					self							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Optional[Any]            =torch_device == "cpu"
          a__			:      Any            =True
          a__			:      str            =False
  
          self._test_inference_batch_single_identical(
              test_max_difference=lowerCAmelCase__	,   relax_max_difference=lowerCAmelCase__	,   test_mean_pixel_difference=lowerCAmelCase__	,   )
  
  
  
  
  
  
  
        @skip_mps
        def      _lowercase						(					self							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Union[str, Any]            =torch_device == "cpu"
          a__			:      Optional[Any]            =False
  
          self._test_attention_slicing_forward_pass(
              test_max_difference=lowerCAmelCase__	,   test_mean_pixel_difference=lowerCAmelCase__	,   )
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     dict[str, str]		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__      ,							a__			:      Optional[int]            ="", "", ""
    a__			:      str            =[]
  
    for character in message_part:
      tmp.append(character_to_number[character]		)
  
    for each in tmp:
      one += each[0]
      two += each[1]
      three += each[2]
  
    return one + two + three
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     dict[str, str]		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__			:      Any            ="", ""
    a__			:      Optional[int]            =[]
  
    for character in message_part:
      this_part += character_to_number[character]
  
    for digit in this_part:
      tmp += digit
      if len(SCREAMING_SNAKE_CASE		) == len(SCREAMING_SNAKE_CASE		):
        result.append(SCREAMING_SNAKE_CASE		)
        a__			:      str            =""
  
    return result[0], result[1], result[2]
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Optional[Any]            =alphabet.replace(" " , ""		).upper()
    a__			:      List[str]            =message.replace(" " , ""		).upper()
  
    # Check length and characters
    if len(SCREAMING_SNAKE_CASE		) != 27:
      raise KeyError("Length of alphabet has to be 27."		)
    for each in message:
      if each not in alphabet:
        raise ValueError("Each message character has to be included in alphabet!"		)
  
      # Generate dictionares
    a__			:      Any            =(
        "111",
        "112",
        "113",
        "121",
        "122",
        "123",
        "131",
        "132",
        "133",
        "211",
        "212",
        "213",
        "221",
        "222",
        "223",
        "231",
        "232",
        "233",
        "311",
        "312",
        "313",
        "321",
        "322",
        "323",
        "331",
        "332",
        "333",
    )
    a__			:      Tuple            ={}
    a__			:      List[str]            ={}
    for letter, number in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		):
      a__			:      Dict            =number
      a__			:      Union[str, Any]            =letter
  
    return message, alphabet, character_to_number, number_to_character
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." , SCREAMING_SNAKE_CASE  :     int = 5		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__      ,							a__      ,							a__			:      Union[str, Any]            =__prepare(
        SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
    a__      ,							a__			:      Union[str, Any]            ="", ""
  
    for i in range(0 , len(SCREAMING_SNAKE_CASE		) + 1 , SCREAMING_SNAKE_CASE		):
      encrypted_numeric += __encrypt_part(
          message[i : i + period] , SCREAMING_SNAKE_CASE		)
  
    for i in range(0 , len(SCREAMING_SNAKE_CASE		) , 3		):
      encrypted += number_to_character[encrypted_numeric[i : i + 3]]
  
    return encrypted
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." , SCREAMING_SNAKE_CASE  :     int = 5		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__      ,							a__      ,							a__			:      Dict            =__prepare(
        SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
    a__			:      Optional[Any]            =[]
    a__			:      Any            =""
  
    for i in range(0 , len(SCREAMING_SNAKE_CASE		) + 1 , SCREAMING_SNAKE_CASE		):
      a__      ,							a__      ,							a__			:      Union[str, Any]            =__decrypt_part(message[i : i + period] , SCREAMING_SNAKE_CASE		)
  
      for j in range(0 , len(SCREAMING_SNAKE_CASE		)		):
        decrypted_numeric.append(a[j] + b[j] + c[j]		)
  
    for each in decrypted_numeric:
      decrypted += number_to_character[each]
  
    return decrypted
  
  
  if __name__ == "__main__":
       UpperCAmelCase			:			List[str]	  = """DEFEND THE EAST WALL OF THE CASTLE."""
       UpperCAmelCase			:			Optional[Any]	  = encrypt_message(msg, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
       UpperCAmelCase			:			Optional[int]	  = decrypt_message(encrypted, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
       print(F"""Encrypted: {encrypted}\nDecrypted: {decrypted}""")
  
  
  
  
  
  
  