


'''simple docstring'''





from __future__ import annotations


def 					a__    (				UpperCamelCase_	:			str,      UpperCamelCase_	:			dict[str, str]	):
   UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__  :Any            =			'''''', '''''', ''''''
   UpperCAmelCase__  :Dict            =			[]

   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__    (				UpperCamelCase_	:			str,      UpperCamelCase_	:			dict[str, str]	):
   UpperCAmelCase__      ,	UpperCAmelCase__  :Tuple            =			'''''', ''''''
   UpperCAmelCase__  :Any            =			[]

   for character in message_part:
      this_part += character_to_number[character]

   for digit in this_part:
      tmp += digit
      if len(UpperCamelCase_	) == len(UpperCamelCase_	):
         result.append(UpperCamelCase_	)
         UpperCAmelCase__  :List[str]            =			''''''

   return result[0], result[1], result[2]


def 					a__    (				UpperCamelCase_	:			str,      UpperCamelCase_	:			str	):
   # Validate message and alphabet, set to upper and remove spaces
   UpperCAmelCase__  :Tuple            =			alphabet.replace(''' ''',      ''''''	).upper()
   UpperCAmelCase__  :str            =			message.replace(''' ''',      ''''''	).upper()

   # Check length and characters
   if len(UpperCamelCase_	) != 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
   UpperCAmelCase__  :Optional[int]            =			(
       '''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''',
   )
   UpperCAmelCase__  :Union[str, Any]            =			{}
   UpperCAmelCase__  :Dict            =			{}
   for letter, number in zip(UpperCamelCase_,      UpperCamelCase_	):
      UpperCAmelCase__  :Dict            =			number
      UpperCAmelCase__  :Optional[Any]            =			letter

   return message, alphabet, character_to_number, number_to_character


def 					a__    (				UpperCamelCase_	:			str,      UpperCamelCase_	:			str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.",      UpperCamelCase_	:			int = 5	):
   UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__  :Tuple            =			__prepare(
       UpperCamelCase_,      UpperCamelCase_	)
   UpperCAmelCase__      ,	UpperCAmelCase__  :Any            =			'''''', ''''''

   for i in range(0,      len(UpperCamelCase_	) + 1,      UpperCamelCase_	):
      encrypted_numeric += __encrypt_part(
          message[i : i + period],      UpperCamelCase_	)

   for i in range(0,      len(UpperCamelCase_	),      3	):
      encrypted += number_to_character[encrypted_numeric[i : i + 3]]

   return encrypted


def 					a__    (				UpperCamelCase_	:			str,      UpperCamelCase_	:			str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.",      UpperCamelCase_	:			int = 5	):
   UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__  :Dict            =			__prepare(
       UpperCamelCase_,      UpperCamelCase_	)
   UpperCAmelCase__  :Union[str, Any]            =			[]
   UpperCAmelCase__  :int            =			''''''

   for i in range(0,      len(UpperCamelCase_	) + 1,      UpperCamelCase_	):
      UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__  :Dict            =			__decrypt_part(message[i : i + period],      UpperCamelCase_	)

      for j in range(0,      len(UpperCamelCase_	)	):
         decrypted_numeric.append(a[j] + b[j] + c[j]	)

   for each in decrypted_numeric:
      decrypted += number_to_character[each]

   return decrypted


if __name__ == "__main__":
    __lowerCamelCase       				=	'''DEFEND THE EAST WALL OF THE CASTLE.'''
    __lowerCamelCase       				=	encrypt_message(msg, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
    __lowerCamelCase       				=	decrypt_message(encrypted, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
    print(F'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')







'''simple docstring'''





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    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 							UpperCAmelCase ( _snake_case	,   _snake_case	,   unittest.TestCase	):
 UpperCAmelCase				=				StableDiffusionParadigmsPipeline
 UpperCAmelCase				=				TEXT_TO_IMAGE_PARAMS
 UpperCAmelCase				=				TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase				=				TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase				=				TEXT_TO_IMAGE_IMAGE_PARAMS


 def 					__SCREAMING_SNAKE_CASE				(			self       :  int   ):
    torch.manual_seed(0   )
    UpperCAmelCase__  :Optional[int]            =			UNetaDConditionModel(
        block_out_channels=(3_2, 6_4) ,					layers_per_block=2 ,					sample_size=3_2 ,					in_channels=4 ,					out_channels=4 ,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,					cross_attention_dim=3_2 ,					attention_head_dim=(2, 4) ,					use_linear_projection=__lowerCamelCase ,					)
    UpperCAmelCase__  :Union[str, Any]            =			DDIMParallelScheduler(
        beta_start=0.0_00_85 ,					beta_end=0.0_12 ,					beta_schedule='''scaled_linear''' ,					clip_sample=__lowerCamelCase ,					set_alpha_to_one=__lowerCamelCase ,					)
    torch.manual_seed(0   )
    UpperCAmelCase__  :Tuple            =			AutoencoderKL(
        block_out_channels=[3_2, 6_4] ,					in_channels=3 ,					out_channels=3 ,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,					latent_channels=4 ,					sample_size=1_2_8 ,					)
    torch.manual_seed(0   )
    UpperCAmelCase__  :Tuple            =			CLIPTextConfig(
        bos_token_id=0 ,					eos_token_id=2 ,					hidden_size=3_2 ,					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 ,					hidden_act='''gelu''' ,					projection_dim=5_1_2 ,					)
    UpperCAmelCase__  :List[Any]            =			CLIPTextModel(__lowerCamelCase   )
    UpperCAmelCase__  :Tuple            =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

    UpperCAmelCase__  :int            =			{
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''safety_checker''': None,
        '''feature_extractor''': None,
    }
    return components


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str ,					__lowerCamelCase       :  List[Any] ,					__lowerCamelCase       :  Any=0   ):
    if str(__lowerCamelCase   ).startswith('''mps'''   ):
       UpperCAmelCase__  :str            =			torch.manual_seed(__lowerCamelCase   )
    else:
       UpperCAmelCase__  :Union[str, Any]            =			torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
    UpperCAmelCase__  :Union[str, Any]            =			{
        '''prompt''': '''a photograph of an astronaut riding a horse''',
        '''generator''': generator,
        '''num_inference_steps''': 1_0,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
        '''parallel''': 3,
        '''debug''': True,
    }
    return inputs


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :Dict            =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__  :Dict            =			self.get_dummy_components()
    UpperCAmelCase__  :str            =			StableDiffusionParadigmsPipeline(**__lowerCamelCase   )
    UpperCAmelCase__  :List[Any]            =			sd_pipe.to(__lowerCamelCase   )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

    UpperCAmelCase__  :Optional[int]            =			self.get_dummy_inputs(__lowerCamelCase   )
    UpperCAmelCase__  :Any            =			sd_pipe(**__lowerCamelCase   ).images
    UpperCAmelCase__  :Union[str, Any]            =			image[0, -3:, -3:, -1]
    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase__  :List[Any]            =			np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]   )

    assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :Any            =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__  :Dict            =			self.get_dummy_components()
    torch.manual_seed(0   )
    UpperCAmelCase__  :Tuple            =			DDPMParallelScheduler()
    torch.manual_seed(0   )
    UpperCAmelCase__  :List[str]            =			StableDiffusionParadigmsPipeline(**__lowerCamelCase   )
    UpperCAmelCase__  :Optional[int]            =			sd_pipe.to(__lowerCamelCase   )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

    UpperCAmelCase__  :Optional[int]            =			self.get_dummy_inputs(__lowerCamelCase   )
    UpperCAmelCase__  :Optional[Any]            =			sd_pipe(**__lowerCamelCase   ).images
    UpperCAmelCase__  :Dict            =			image[0, -3:, -3:, -1]
    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase__  :List[str]            =			np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]   )

    assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    super().test_inference_batch_consistent(batch_sizes=[1, 2]   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    super().test_inference_batch_single_identical(batch_size=2 ,					expected_max_diff=3e-3   )





 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :int            =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__  :str            =			self.get_dummy_components()
    UpperCAmelCase__  :Any            =			StableDiffusionParadigmsPipeline(**__lowerCamelCase   )
    UpperCAmelCase__  :List[str]            =			sd_pipe.to(__lowerCamelCase   )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

    UpperCAmelCase__  :Tuple            =			self.get_dummy_inputs(__lowerCamelCase   )
    UpperCAmelCase__  :Optional[int]            =			'''french fries'''
    UpperCAmelCase__  :Any            =			sd_pipe(**__lowerCamelCase ,					negative_prompt=__lowerCamelCase   )
    UpperCAmelCase__  :int            =			output.images
    UpperCAmelCase__  :Tuple            =			image[0, -3:, -3:, -1]

    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase__  :Tuple            =			np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]   )

    assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2




@slow
@require_torch_gpu
class 							UpperCAmelCase ( unittest.TestCase	):


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any] ,					__lowerCamelCase       :  Tuple=0   ):
    UpperCAmelCase__  :List[Any]            =			torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
    UpperCAmelCase__  :List[str]            =			{
        '''prompt''': '''a photograph of an astronaut riding a horse''',
        '''generator''': generator,
        '''num_inference_steps''': 1_0,
        '''guidance_scale''': 7.5,
        '''output_type''': '''numpy''',
        '''parallel''': 3,
        '''debug''': True,
    }
    return inputs





 def 					__SCREAMING_SNAKE_CASE				(			self       :  int   ):
    UpperCAmelCase__  :Dict            =			'''stabilityai/stable-diffusion-2-base'''
    UpperCAmelCase__  :Union[str, Any]            =			DDIMParallelScheduler.from_pretrained(__lowerCamelCase ,					subfolder='''scheduler'''   )
    UpperCAmelCase__  :Dict            =			StableDiffusionParadigmsPipeline.from_pretrained(__lowerCamelCase ,					scheduler=__lowerCamelCase ,					safety_checker=__lowerCamelCase   )
    pipe.to(__lowerCamelCase   )
    pipe.set_progress_bar_config(disable=__lowerCamelCase   )
    pipe.enable_attention_slicing()

    UpperCAmelCase__  :Dict            =			self.get_inputs()
    UpperCAmelCase__  :int            =			pipe(**__lowerCamelCase   ).images
    UpperCAmelCase__  :List[str]            =			image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    UpperCAmelCase__  :Optional[int]            =			np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]   )

    assert np.abs(expected_slice - image_slice   ).max() < 1e-2



