






import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()

class        snake_case_				(							unittest.TestCase   ):







   """simple docstring"""



   __lowerCAmelCase  =    """hf-internal-testing/tiny-stable-diffusion-pipe"""
   __lowerCAmelCase  =    1
   __lowerCAmelCase  =    77
   __lowerCAmelCase  =    32
   __lowerCAmelCase  =    4
   __lowerCAmelCase  =    __lowerCAmelCase  =    64
   __lowerCAmelCase  =    torch.manual_seed(0   )

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						List[str]=0.99_99						)	->		str:


       """simple docstring"""



       _lowercase     			=						UNetaDConditionModel.from_pretrained(self.model_id ,							subfolder='unet'						)
       _lowercase     			=						unet.to(snake_case__						)
       _lowercase     			=						EMAModel(unet.parameters() ,							decay=snake_case__ ,							model_cls=snake_case__ ,							model_config=unet.config						)
       return unet, ema_unet

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		str:


       """simple docstring"""



       _lowercase     			=						torch.randn(
           self.batch_size ,							self.num_in_channels ,							self.latent_height ,							self.latent_width ,							generator=self.generator						).to(snake_case__						)
       _lowercase     			=						torch.randint(0 ,							1_000 ,							size=(self.batch_size,) ,							generator=self.generator						).to(snake_case__						)
       _lowercase     			=						torch.randn(
           self.batch_size ,							self.prompt_length ,							self.text_encoder_hidden_dim ,							generator=self.generator						).to(snake_case__						)
       return noisy_latents, timesteps, encoder_hidden_states

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any] ,							snake_case__:						str						)	->		Tuple:


       """simple docstring"""



       _lowercase     			=						{}
       for k, param in unet.state_dict().items():
           _lowercase     			=						torch.randn_like(snake_case__						) + (param * torch.randn_like(snake_case__						))
           updated_state_dict.update({k: updated_param}						)
       unet.load_state_dict(snake_case__						)
       return unet

   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		Any:


       """simple docstring"""



       _lowercase			,     _lowercase     			=						self.get_models()
       # Take the first (hypothetical) EMA step.
       ema_unet.step(unet.parameters()						)
       assert ema_unet.optimization_step == 1

       # Take two more.
       for _ in range(2						):
           ema_unet.step(unet.parameters()						)
       assert ema_unet.optimization_step == 3

   def       SCREAMING_SNAKE_CASE__					(     self:						Any						)	->		Tuple:


       """simple docstring"""



       _lowercase			,     _lowercase     			=						self.get_models()
       # Since the `unet` is not being updated (i.e., backprop'd)
       # there won't be any difference between the `params` of `unet`
       # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
       ema_unet.step(unet.parameters()						)
       _lowercase     			=						list(unet.parameters()						)
       for s_param, param in zip(ema_unet.shadow_params ,							snake_case__						):
           assert torch.allclose(snake_case__ ,							snake_case__						)

       # The above holds true even if we call `ema.step()` multiple times since
       # `unet` params are still not being updated.
       for _ in range(4						):
           ema_unet.step(unet.parameters()						)
       for s_param, param in zip(ema_unet.shadow_params ,							snake_case__						):
           assert torch.allclose(snake_case__ ,							snake_case__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any]						)	->		Dict:


       """simple docstring"""



       _lowercase			,     _lowercase     			=						self.get_models()
       # Here we simulate the parameter updates for `unet`. Since there might
       # be some parameters which are initialized to zero we take extra care to
       # initialize their values to something non-zero before the multiplication.
       _lowercase     			=						self.simulate_backprop(snake_case__						)

       # Take the EMA step.
       ema_unet.step(unet_pseudo_updated_step_one.parameters()						)

       # Now the EMA'd parameters won't be equal to the original model parameters.
       _lowercase     			=						list(unet_pseudo_updated_step_one.parameters()						)
       for s_param, param in zip(ema_unet.shadow_params ,							snake_case__						):
           assert ~torch.allclose(snake_case__ ,							snake_case__						)

       # Ensure this is the case when we take multiple EMA steps.
       for _ in range(4						):
           ema_unet.step(unet.parameters()						)
       for s_param, param in zip(ema_unet.shadow_params ,							snake_case__						):
           assert ~torch.allclose(snake_case__ ,							snake_case__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Any						)	->		int:


       """simple docstring"""



       _lowercase			,     _lowercase     			=						self.get_models()

       # First backprop + EMA
       _lowercase     			=						self.simulate_backprop(snake_case__						)
       ema_unet.step(unet_step_one.parameters()						)
       _lowercase     			=						ema_unet.shadow_params

       # Second backprop + EMA
       _lowercase     			=						self.simulate_backprop(snake_case__						)
       ema_unet.step(unet_step_two.parameters()						)
       _lowercase     			=						ema_unet.shadow_params

       for step_one, step_two in zip(snake_case__ ,							snake_case__						):
           assert ~torch.allclose(snake_case__ ,							snake_case__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		Dict:


       """simple docstring"""



       _lowercase			,     _lowercase     			=						self.get_models(decay=0.0						)
       _lowercase     			=						self.simulate_backprop(snake_case__						)
       ema_unet.step(unet_step_one.parameters()						)
       _lowercase     			=						ema_unet.shadow_params

       _lowercase     			=						self.simulate_backprop(snake_case__						)
       ema_unet.step(unet_step_two.parameters()						)
       _lowercase     			=						ema_unet.shadow_params

       for step_one, step_two in zip(snake_case__ ,							snake_case__						):
           assert torch.allclose(snake_case__ ,							snake_case__						)

   @skip_mps
   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any]						)	->		Union[str, Any]:


       """simple docstring"""



       _lowercase			,     _lowercase     			=						self.get_models()
       _lowercase			,     _lowercase			,     _lowercase     			=						self.get_dummy_inputs()

       with tempfile.TemporaryDirectory() as tmpdir:
           ema_unet.save_pretrained(snake_case__						)
           _lowercase     			=						UNetaDConditionModel.from_pretrained(snake_case__ ,							model_cls=snake_case__						)
           _lowercase     			=						loaded_unet.to(unet.device						)

       # Since no EMA step has been performed the outputs should match.
       _lowercase     			=						unet(snake_case__ ,							snake_case__ ,							snake_case__						).sample
       _lowercase     			=						loaded_unet(snake_case__ ,							snake_case__ ,							snake_case__						).sample

       assert torch.allclose(snake_case__ ,							snake_case__ ,							atol=1E-4						)








from typing import Literal

lowerCamelCase					    =	'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						LETTERS if mode == 'decrypt' else key
    _lowercase     			=						key if mode == 'decrypt' else LETTERS
    _lowercase     			=						''
    # loop through each symbol in the message
    for symbol in message:
        if symbol.upper() in chars_a:
            # encrypt/decrypt the symbol
            _lowercase     			=						chars_a.find(symbol.upper()   )
            if symbol.isupper():
                translated += chars_b[sym_index].upper()
            else:
                translated += chars_b[sym_index].lower()
        else:
            # symbol is not in LETTERS, just add it
            translated += symbol
    return translated
def 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    return translate_message(_lowercase,   _lowercase,   'encrypt'   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    return translate_message(_lowercase,   _lowercase,   'decrypt'   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						'Hello World'
    _lowercase     			=						'QWERTYUIOPASDFGHJKLZXCVBNM'
    _lowercase     			=						'decrypt'  # set to 'encrypt' or 'decrypt'

    if mode == "encrypt":
        _lowercase     			=						encrypt_message(_lowercase,   _lowercase   )
    elif mode == "decrypt":
        _lowercase     			=						decrypt_message(_lowercase,   _lowercase   )
    print(f'''Using the key {key}, the {mode}ed message is: {translated}'''   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()
