
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 		lowercase_							(							unittest.TestCase					):
 A__    :					Any    =     """hf-internal-testing/tiny-stable-diffusion-pipe"""
 A__    :					str    =     1
 A__    :					Dict    =     77
 A__    :					Optional[Any]    =     32
 A__    :					List[Any]    =     4
 A__    :					str    =     64
 A__    :					List[str]    =     torch.manual_seed(0					)

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0.9_999  ):




        """simple docstring"""


        UpperCamelCase_											=							UNetaDConditionModel.from_pretrained(self.model_id  ,						subfolder="""unet"""  )
        UpperCamelCase_											=							unet.to(__UpperCamelCase  )
        UpperCamelCase_											=							EMAModel(unet.parameters()  ,						decay=__UpperCamelCase  ,						model_cls=__UpperCamelCase  ,						model_config=unet.config  )
        return unet, ema_unet

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							torch.randn(
            self.batch_size  ,						self.num_in_channels  ,						self.latent_height  ,						self.latent_width  ,						generator=self.generator  ).to(__UpperCamelCase  )
        UpperCamelCase_											=							torch.randint(0  ,						1_0_0_0  ,						size=(self.batch_size,)  ,						generator=self.generator  ).to(__UpperCamelCase  )
        UpperCamelCase_											=							torch.randn(
            self.batch_size  ,						self.prompt_length  ,						self.text_encoder_hidden_dim  ,						generator=self.generator  ).to(__UpperCamelCase  )
        return noisy_latents, timesteps, encoder_hidden_states

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


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

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_							,					UpperCamelCase_											=							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 lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_							,					UpperCamelCase_											=							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()  )
        UpperCamelCase_											=							list(unet.parameters()  )
        for s_param, param in zip(ema_unet.shadow_params  ,						__UpperCamelCase  ):
               assert torch.allclose(__UpperCamelCase  ,						__UpperCamelCase  )

        # 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  ,						__UpperCamelCase  ):
               assert torch.allclose(__UpperCamelCase  ,						__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_							,					UpperCamelCase_											=							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.
        UpperCamelCase_											=							self.simulate_backprop(__UpperCamelCase  )

        # 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.
        UpperCamelCase_											=							list(unet_pseudo_updated_step_one.parameters()  )
        for s_param, param in zip(ema_unet.shadow_params  ,						__UpperCamelCase  ):
               assert ~torch.allclose(__UpperCamelCase  ,						__UpperCamelCase  )

        # 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  ,						__UpperCamelCase  ):
               assert ~torch.allclose(__UpperCamelCase  ,						__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_							,					UpperCamelCase_											=							self.get_models()

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

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

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

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_							,					UpperCamelCase_											=							self.get_models(decay=0.0  )
        UpperCamelCase_											=							self.simulate_backprop(__UpperCamelCase  )
        ema_unet.step(unet_step_one.parameters()  )
        UpperCamelCase_											=							ema_unet.shadow_params

        UpperCamelCase_											=							self.simulate_backprop(__UpperCamelCase  )
        ema_unet.step(unet_step_two.parameters()  )
        UpperCamelCase_											=							ema_unet.shadow_params

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

 @skip_mps
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


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

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

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

        assert torch.allclose(__UpperCamelCase  ,						__UpperCamelCase  ,						atol=1e-4  )





_A							=       [chr(i) for i in range(32, 126)]
_A							=       list(range(len(alphabets)))
_A							=       list(range(len(alphabets)))
_A							=       list(range(len(alphabets)))
_A							=       list(reversed(range(len(alphabets))))
_A							=       []
_A							=       _A							=       _A							=       0




def 					lowerCamelCase__					(    )		->						List[str]:
       global gear_one_pos
       global gear_two_pos
       global gear_three_pos
       UpperCamelCase_											=							gear_one[0]
       gear_one.append(a__  )
       del gear_one[0]
       gear_one_pos += 1
       if gear_one_pos % int(len(a__  )  ) == 0:
              UpperCamelCase_											=							gear_two[0]
              gear_two.append(a__  )
              del gear_two[0]
              gear_two_pos += 1
              if gear_two_pos % int(len(a__  )  ) == 0:
                     UpperCamelCase_											=							gear_three[0]
                     gear_three.append(a__  )
                     del gear_three[0]
                     gear_three_pos += 1




def 					lowerCamelCase__					(    a__	:	Optional[int]  )		->						Any:
       UpperCamelCase_											=							alphabets.index(a__  )
       UpperCamelCase_											=							gear_one[target]
       UpperCamelCase_											=							gear_two[target]
       UpperCamelCase_											=							gear_three[target]
       UpperCamelCase_											=							reflector[target]
       UpperCamelCase_											=							gear_three.index(a__  )
       UpperCamelCase_											=							gear_two.index(a__  )
       UpperCamelCase_											=							gear_one.index(a__  )
       code.append(alphabets[target]  )
       rotator()


if __name__ == "__main__":
      _A							=       list(input('''Type your message:\n'''))
      while True:
            try:
                  _A							=       int(input('''Please set token:(must be only digits)\n'''))
                  break
            except Exception as error:
                  print(error)
      for _ in range(token):
            rotator()
      for j in decode:
            engine(j)
      print('''\n''' + ''''''.join(code))
      print(
          F'''\nYour Token is {token} please write it down.\nIf you want to decode '''
          '''this message again you should input same digits as token!'''
      )



