




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 	UpperCamelCase__			(unittest.TestCase  ):

    '''simple docstring'''



    lowerCamelCase_   :			List[Any]			  =   """hf-internal-testing/tiny-stable-diffusion-pipe"""
    lowerCamelCase_   :			Dict			  =   1
    lowerCamelCase_   :			Union[str, Any]			  =   7_7
    lowerCamelCase_   :			Any			  =   3_2
    lowerCamelCase_   :			Any			  =   4
    lowerCamelCase_   :			str			  =   6_4
    lowerCamelCase_   :			List[Any]			  =   torch.manual_seed(0  )







    def 					_lowercase  (					self  ,						UpperCamelCase__=0.9999  )  ->   Optional[Any]:
           lowerCamelCase     :   Optional[Any]    				=      UNetaDConditionModel.from_pretrained(self.model_id  ,						subfolder="unet"  )
           lowerCamelCase     :   int    				=      unet.to(UpperCamelCase__  )
           lowerCamelCase     :   Tuple    				=      EMAModel(unet.parameters()  ,						decay=UpperCamelCase__  ,						model_cls=UpperCamelCase__  ,						model_config=unet.config  )
           return unet, ema_unet







    def 					_lowercase  (					self  )  ->   Union[str, Any]:
           lowerCamelCase     :   Optional[Any]    				=      torch.randn(
               self.batch_size  ,						self.num_in_channels  ,						self.latent_height  ,						self.latent_width  ,						generator=self.generator  ).to(UpperCamelCase__  )
           lowerCamelCase     :   Any    				=      torch.randint(0  ,						1000  ,						size=(self.batch_size,)  ,						generator=self.generator  ).to(UpperCamelCase__  )
           lowerCamelCase     :   Union[str, Any]    				=      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 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   int:
           lowerCamelCase     :   Dict    				=      {}
           for k, param in unet.state_dict().items():
                  lowerCamelCase     :   Any    				=      torch.randn_like(UpperCamelCase__  ) + (param * torch.randn_like(UpperCamelCase__  ))
                  updated_state_dict.update({k: updated_param}  )
           unet.load_state_dict(UpperCamelCase__  )
           return unet







    def 					_lowercase  (					self  )  ->   Any:
           lowerCamelCase				,   lowerCamelCase     :   List[str]    				=      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 					_lowercase  (					self  )  ->   Union[str, Any]:
           lowerCamelCase				,   lowerCamelCase     :   List[str]    				=      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()  )
           lowerCamelCase     :   Dict    				=      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 					_lowercase  (					self  )  ->   Any:
           lowerCamelCase				,   lowerCamelCase     :   Optional[Any]    				=      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.
           lowerCamelCase     :   str    				=      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.
           lowerCamelCase     :   int    				=      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 					_lowercase  (					self  )  ->   Any:
           lowerCamelCase				,   lowerCamelCase     :   Dict    				=      self.get_models()

           # First backprop + EMA
           lowerCamelCase     :   Optional[Any]    				=      self.simulate_backprop(UpperCamelCase__  )
           ema_unet.step(unet_step_one.parameters()  )
           lowerCamelCase     :   Tuple    				=      ema_unet.shadow_params

           # Second backprop + EMA
           lowerCamelCase     :   Optional[Any]    				=      self.simulate_backprop(UpperCamelCase__  )
           ema_unet.step(unet_step_two.parameters()  )
           lowerCamelCase     :   List[Any]    				=      ema_unet.shadow_params

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







    def 					_lowercase  (					self  )  ->   List[Any]:
           lowerCamelCase				,   lowerCamelCase     :   str    				=      self.get_models(decay=0.0  )
           lowerCamelCase     :   str    				=      self.simulate_backprop(UpperCamelCase__  )
           ema_unet.step(unet_step_one.parameters()  )
           lowerCamelCase     :   Union[str, Any]    				=      ema_unet.shadow_params

           lowerCamelCase     :   Optional[int]    				=      self.simulate_backprop(UpperCamelCase__  )
           ema_unet.step(unet_step_two.parameters()  )
           lowerCamelCase     :   List[str]    				=      ema_unet.shadow_params

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







    @skip_mps
    def 					_lowercase  (					self  )  ->   int:
           lowerCamelCase				,   lowerCamelCase     :   Optional[int]    				=      self.get_models()
           lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase     :   Any    				=      self.get_dummy_inputs()

           with tempfile.TemporaryDirectory() as tmpdir:
                  ema_unet.save_pretrained(UpperCamelCase__  )
                  lowerCamelCase     :   str    				=      UNetaDConditionModel.from_pretrained(UpperCamelCase__  ,						model_cls=UpperCamelCase__  )
                  lowerCamelCase     :   Optional[Any]    				=      loaded_unet.to(unet.device  )

           # Since no EMA step has been performed the outputs should match.
           lowerCamelCase     :   Optional[int]    				=      unet(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ).sample
           lowerCamelCase     :   int    				=      loaded_unet(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ).sample

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










SCREAMING_SNAKE_CASE__ :   str						=     {
    '+': lambda x, y: x + y,
    '-': lambda x, y: x - y,
    '*': lambda x, y: x * y,
    '/': lambda x, y: x / y,
}

def   A   (   _SCREAMING_SNAKE_CASE					)			->				List[str]:
       return c.isdigit()

def   A   (   _SCREAMING_SNAKE_CASE					)			->				Optional[Any]:
       lowerCamelCase     :   Tuple    				=      []

       # iterate over the string in reverse order
       for c in expression.split()[::-1]:
              # push operand to stack
              if is_operand(_SCREAMING_SNAKE_CASE					):
                     stack.append(int(_SCREAMING_SNAKE_CASE					)					)

              else:
                     # pop values from stack can calculate the result
                     # push the result onto the stack again
                     lowerCamelCase     :   str    				=      stack.pop()
                     lowerCamelCase     :   Optional[int]    				=      stack.pop()
                     stack.append(calc[c](_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)					)

       return stack.pop()


# Driver code
if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__ :   Optional[int]						=     '+ 9 * 2 6'
       print(evaluate(test_expression))

       SCREAMING_SNAKE_CASE__ :   Union[str, Any]						=     '/ * 10 2 + 4 1 '
       print(evaluate(test_expression))






