





'''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 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__							,   lowerCAmelCase__							,   unittest.TestCase      ):
      '''simple docstring'''



      lowerCAmelCase__       :      Dict 		=		StableDiffusionXLPipeline
      lowerCAmelCase__       :      List[Any] 		=		TEXT_TO_IMAGE_PARAMS
      lowerCAmelCase__       :      Any 		=		TEXT_TO_IMAGE_BATCH_PARAMS
      lowerCAmelCase__       :      str 		=		TEXT_TO_IMAGE_IMAGE_PARAMS
      lowerCAmelCase__       :      int 		=		TEXT_TO_IMAGE_IMAGE_PARAMS

      def   _lowerCamelCase		(							self    : List[str]  )		->  Optional[Any]:
           torch.manual_seed(0  )
           _lowercase							:      Tuple  						=							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=UpperCamelCase   ,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   ,)
           _lowercase							:      str  						=							EulerDiscreteScheduler(
               beta_start=0.0_0_0_8_5   ,beta_end=0.0_1_2   ,steps_offset=1   ,beta_schedule='scaled_linear'   ,timestep_spacing='leading'   ,)
           torch.manual_seed(0  )
           _lowercase							:      Optional[int]  						=							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  )
           _lowercase							:      Union[str, Any]  						=							CLIPTextConfig(
               bos_token_id=0   ,eos_token_id=2   ,hidden_size=32   ,intermediate_size=37   ,layer_norm_eps=1e-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1000   ,hidden_act='gelu'   ,projection_dim=32   ,)
           _lowercase							:      Tuple  						=							CLIPTextModel(UpperCamelCase  )
           _lowercase							:      Any  						=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   ,local_files_only=UpperCamelCase  )

           _lowercase							:      int  						=							CLIPTextModelWithProjection(UpperCamelCase  )
           _lowercase							:      Dict  						=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   ,local_files_only=UpperCamelCase  )

           _lowercase							:      Optional[int]  						=							{
               '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   _lowerCamelCase		(							self    : str   ,UpperCamelCase    : List[str]   ,UpperCamelCase    : str=0  )		->  Optional[Any]:
           if str(UpperCamelCase  ).startswith('mps'  ):
                _lowercase							:      Dict  						=							torch.manual_seed(UpperCamelCase  )
           else:
                _lowercase							:      Union[str, Any]  						=							torch.Generator(device=UpperCamelCase  ).manual_seed(UpperCamelCase  )
           _lowercase							:      Dict  						=							{
               '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   _lowerCamelCase		(							self    : Union[str, Any]  )		->  List[Any]:
           _lowercase							:      Dict  						=							'cpu'  # ensure determinism for the device-dependent torch.Generator
           _lowercase							:      Tuple  						=							self.get_dummy_components()
           _lowercase							:      int  						=							StableDiffusionXLPipeline(**UpperCamelCase  )
           _lowercase							:      Tuple  						=							sd_pipe.to(UpperCamelCase  )
           sd_pipe.set_progress_bar_config(disable=UpperCamelCase  )

           _lowercase							:      Any  						=							self.get_dummy_inputs(UpperCamelCase  )
           _lowercase							:      int  						=							sd_pipe(**UpperCamelCase  ).images
           _lowercase							:      Dict  						=							image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           _lowercase							:      Any  						=							np.array([0.5_8_7_3, 0.6_1_2_8, 0.4_7_9_7, 0.5_1_2_2, 0.5_6_7_4, 0.4_6_3_9, 0.5_2_2_7, 0.5_1_4_9, 0.4_7_4_7]  )

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

      def   _lowerCamelCase		(							self    : str  )		->  Union[str, Any]:
           _lowercase							:      List[Any]  						=							self.get_dummy_components()
           _lowercase							:      Optional[Any]  						=							StableDiffusionXLPipeline(**UpperCamelCase  )
           _lowercase							:      int  						=							sd_pipe.to(UpperCamelCase  )
           _lowercase							:      Dict  						=							sd_pipe.to(UpperCamelCase  )
           sd_pipe.set_progress_bar_config(disable=UpperCamelCase  )

           # forward without prompt embeds
           _lowercase							:      Optional[int]  						=							self.get_dummy_inputs(UpperCamelCase  )
           _lowercase							:      Optional[Any]  						=							3 * ['this is a negative prompt']
           _lowercase							:      int  						=							negative_prompt
           _lowercase							:      List[Any]  						=							3 * [inputs['prompt']]

           _lowercase							:      Optional[Any]  						=							sd_pipe(**UpperCamelCase  )
           _lowercase							:      Tuple  						=							output.images[0, -3:, -3:, -1]

           # forward with prompt embeds
           _lowercase							:      Dict  						=							self.get_dummy_inputs(UpperCamelCase  )
           _lowercase							:      Tuple  						=							3 * ['this is a negative prompt']
           _lowercase							:      Any  						=							3 * [inputs.pop('prompt'  )]

           (
               (
               _lowercase
           )						, (
               _lowercase
           )						, (
               _lowercase
           )						, (
               _lowercase
           )						, 
           )							:      Tuple  						=							sd_pipe.encode_prompt(UpperCamelCase   ,negative_prompt=UpperCamelCase  )

           _lowercase							:      Union[str, Any]  						=							sd_pipe(
               **UpperCamelCase   ,prompt_embeds=UpperCamelCase   ,negative_prompt_embeds=UpperCamelCase   ,pooled_prompt_embeds=UpperCamelCase   ,negative_pooled_prompt_embeds=UpperCamelCase   ,)
           _lowercase							:      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   _lowerCamelCase		(							self    : Dict  )		->  Dict:
           super().test_attention_slicing_forward_pass(expected_max_diff=3e-3  )



      def   _lowerCamelCase		(							self    : Dict  )		->  Any:
           super().test_inference_batch_single_identical(expected_max_diff=3e-3  )



@slow
@require_torch_gpu
class 							__SCREAMING_SNAKE_CASE			(       unittest.TestCase      ):
      '''simple docstring'''



      def   _lowerCamelCase		(							self    : Any  )		->  int:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()

      def   _lowerCamelCase		(							self    : str   ,UpperCamelCase    : Dict   ,UpperCamelCase    : int="cpu"   ,UpperCamelCase    : Optional[Any]=torch.floataa   ,UpperCamelCase    : Tuple=0  )		->  Dict:
           _lowercase							:      Dict  						=							torch.Generator(device=UpperCamelCase  ).manual_seed(UpperCamelCase  )
           _lowercase							:      List[Any]  						=							np.random.RandomState(UpperCamelCase  ).standard_normal((1, 4, 64, 64)  )
           _lowercase							:      str  						=							torch.from_numpy(UpperCamelCase  ).to(device=UpperCamelCase   ,dtype=UpperCamelCase  )
           _lowercase							:      str  						=							{
               '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   _lowerCamelCase		(							self    : int  )		->  Union[str, Any]:
           _lowercase							:      Any  						=							DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'  )
           pipe.to(UpperCamelCase  )
           pipe.set_progress_bar_config(disable=UpperCamelCase  )

           _lowercase							:      Dict  						=							self.get_inputs(UpperCamelCase  )
           _lowercase							:      Optional[Any]  						=							pipe(**UpperCamelCase  ).images
           _lowercase							:      Optional[Any]  						=							image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)
           _lowercase							:      int  						=							np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]  )
           assert np.abs(image_slice - expected_slice  ).max() < 7e-3






'''simple docstring'''







A						    =							{
    '''A''': ('''ABCDEFGHIJKLM''', '''NOPQRSTUVWXYZ'''),
    '''B''': ('''ABCDEFGHIJKLM''', '''NOPQRSTUVWXYZ'''),
    '''C''': ('''ABCDEFGHIJKLM''', '''ZNOPQRSTUVWXY'''),
    '''D''': ('''ABCDEFGHIJKLM''', '''ZNOPQRSTUVWXY'''),
    '''E''': ('''ABCDEFGHIJKLM''', '''YZNOPQRSTUVWX'''),
    '''F''': ('''ABCDEFGHIJKLM''', '''YZNOPQRSTUVWX'''),
    '''G''': ('''ABCDEFGHIJKLM''', '''XYZNOPQRSTUVW'''),
    '''H''': ('''ABCDEFGHIJKLM''', '''XYZNOPQRSTUVW'''),
    '''I''': ('''ABCDEFGHIJKLM''', '''WXYZNOPQRSTUV'''),
    '''J''': ('''ABCDEFGHIJKLM''', '''WXYZNOPQRSTUV'''),
    '''K''': ('''ABCDEFGHIJKLM''', '''VWXYZNOPQRSTU'''),
    '''L''': ('''ABCDEFGHIJKLM''', '''VWXYZNOPQRSTU'''),
    '''M''': ('''ABCDEFGHIJKLM''', '''UVWXYZNOPQRST'''),
    '''N''': ('''ABCDEFGHIJKLM''', '''UVWXYZNOPQRST'''),
    '''O''': ('''ABCDEFGHIJKLM''', '''TUVWXYZNOPQRS'''),
    '''P''': ('''ABCDEFGHIJKLM''', '''TUVWXYZNOPQRS'''),
    '''Q''': ('''ABCDEFGHIJKLM''', '''STUVWXYZNOPQR'''),
    '''R''': ('''ABCDEFGHIJKLM''', '''STUVWXYZNOPQR'''),
    '''S''': ('''ABCDEFGHIJKLM''', '''RSTUVWXYZNOPQ'''),
    '''T''': ('''ABCDEFGHIJKLM''', '''RSTUVWXYZNOPQ'''),
    '''U''': ('''ABCDEFGHIJKLM''', '''QRSTUVWXYZNOP'''),
    '''V''': ('''ABCDEFGHIJKLM''', '''QRSTUVWXYZNOP'''),
    '''W''': ('''ABCDEFGHIJKLM''', '''PQRSTUVWXYZNO'''),
    '''X''': ('''ABCDEFGHIJKLM''', '''PQRSTUVWXYZNO'''),
    '''Y''': ('''ABCDEFGHIJKLM''', '''OPQRSTUVWXYZN'''),
    '''Z''': ('''ABCDEFGHIJKLM''', '''OPQRSTUVWXYZN'''),
}

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	str)			->						list[tuple[str, str]]:

     '''simple docstring'''




     return [alphabet[char] for char in key.upper()]

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	str       ,  lowerCAmelCase__     :	str)			->						str:

     '''simple docstring'''




     _lowercase							:      Optional[int]  						=							''
     _lowercase							:      List[str]  						=							0
     _lowercase							:      Optional[Any]  						=							generate_table(lowerCAmelCase__)
     for char in words.upper():
          cipher += get_opponent(table[count]       ,  lowerCAmelCase__)
          _lowercase							:      Dict  						=							(count + 1) % len(lowerCAmelCase__)
     return cipher

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	str       ,  lowerCAmelCase__     :	str)			->						str:

     '''simple docstring'''




     return encrypt(lowerCAmelCase__       ,  lowerCAmelCase__)

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	tuple[str, str]       ,  lowerCAmelCase__     :	str)			->						tuple[int, int]:

     '''simple docstring'''




     _lowercase							:      Optional[Any]  						=							0 if char in table[0] else 1
     _lowercase							:      Tuple  						=							table[row].index(lowerCAmelCase__)
     return row, col

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	tuple[str, str]       ,  lowerCAmelCase__     :	str)			->						str:

     '''simple docstring'''




     _lowercase						, _lowercase							:      str  						=							get_position(lowerCAmelCase__       ,  char.upper())
     if row == 1:
          return table[0][col]
     else:
          return table[1][col] if row == 0 else char


if __name__ == "__main__":
    import doctest

    doctest.testmod()  # Fist ensure that all our tests are passing...
    A						    =							input('''Enter key: ''').strip()
    A						    =							input('''Enter text to encrypt: ''').strip()
    A						    =							encrypt(key, text)

    print(F"""Encrypted: {cipher_text}""")
    print(F"""Decrypted with key: {decrypt(key, cipher_text)}""")