





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__	,   )













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}""")






