




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       A      (		unittest.TestCase		):
     """simple docstring"""

     _snake_case:						Optional[Any]  ='hf-internal-testing/tiny-stable-diffusion-pipe'
     _snake_case:						Any  =1
     _snake_case:						Union[str, Any]  =77
     _snake_case:						Tuple  =32
     _snake_case:						int  =4
     _snake_case:						Union[str, Any]  =64
     _snake_case:						Optional[Any]  =torch.manual_seed(0		)




     def 			__snake_case (     self	: Dict						,	A__	: List[str]=0.9999      )					->							Any:
           lowerCamelCase__   	=					UNetaDConditionModel.from_pretrained(self.model_id						,	subfolder='unet'      )
           lowerCamelCase__   	=					unet.to(A__      )
           lowerCamelCase__   	=					EMAModel(unet.parameters()						,	decay=A__						,	model_cls=A__						,	model_config=unet.config      )
           return unet, ema_unet




     def 			__snake_case (     self	: List[str]      )					->							str:
           lowerCamelCase__   	=					torch.randn(
               self.batch_size						,	self.num_in_channels						,	self.latent_height						,	self.latent_width						,	generator=self.generator      ).to(A__      )
           lowerCamelCase__   	=					torch.randint(0						,	1_0_0_0						,	size=(self.batch_size,)						,	generator=self.generator      ).to(A__      )
           lowerCamelCase__   	=					torch.randn(
               self.batch_size						,	self.prompt_length						,	self.text_encoder_hidden_dim						,	generator=self.generator      ).to(A__      )
           return noisy_latents, timesteps, encoder_hidden_states




     def 			__snake_case (     self	: Any						,	A__	: Union[str, Any]      )					->							int:
           lowerCamelCase__   	=					{}
           for k, param in unet.state_dict().items():
                 lowerCamelCase__   	=					torch.randn_like(A__      ) + (param * torch.randn_like(A__      ))
                 updated_state_dict.update({k: updated_param}      )
           unet.load_state_dict(A__      )
           return unet




     def 			__snake_case (     self	: int      )					->							Optional[Any]:
           lowerCamelCase__	,		lowerCamelCase__   	=					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 			__snake_case (     self	: int      )					->							List[Any]:
           lowerCamelCase__	,		lowerCamelCase__   	=					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__   	=					list(unet.parameters()      )
           for s_param, param in zip(ema_unet.shadow_params						,	A__      ):
                 assert torch.allclose(A__						,	A__      )

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




     def 			__snake_case (     self	: Optional[Any]      )					->							List[Any]:
           lowerCamelCase__	,		lowerCamelCase__   	=					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__   	=					self.simulate_backprop(A__      )

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

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




     def 			__snake_case (     self	: Dict      )					->							Any:
           # If we call EMA step after a backpropagation consecutively for two times,
           # the shadow params from those two steps should be different.
           lowerCamelCase__	,		lowerCamelCase__   	=					self.get_models()

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

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

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




     def 			__snake_case (     self	: str      )					->							str:
           # If there's no decay even if there are backprops, EMA steps
           # won't take any effect i.e., the shadow params would remain the
           # same.
           lowerCamelCase__	,		lowerCamelCase__   	=					self.get_models(decay=0.0      )
           lowerCamelCase__   	=					self.simulate_backprop(A__      )
           ema_unet.step(unet_step_one.parameters()      )
           lowerCamelCase__   	=					ema_unet.shadow_params

           lowerCamelCase__   	=					self.simulate_backprop(A__      )
           ema_unet.step(unet_step_two.parameters()      )
           lowerCamelCase__   	=					ema_unet.shadow_params

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




     @skip_mps
     def 			__snake_case (     self	: Any      )					->							int:
           lowerCamelCase__	,		lowerCamelCase__   	=					self.get_models()
           lowerCamelCase__	,		lowerCamelCase__	,		lowerCamelCase__   	=					self.get_dummy_inputs()

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

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

           assert torch.allclose(A__						,	A__						,	atol=1E-4      )
from __future__ import annotations

from statistics import mean
def 					__lowercase						(    lowerCamelCase__       )					->list[int]:

  '''simple docstring'''




  a__  		=  2
  a__  		=  list(lowerCamelCase__       )
  a__  		=  [0] * len(lowerCamelCase__       )
  a__  		=  0
  while True:
    a__  		=  True
    for i, burst_time in enumerate(lowerCamelCase__       ):
      if rem_burst_times[i] > 0:
        a__  		=  False
        if rem_burst_times[i] > quantum:
          t += quantum
          rem_burst_times[i] -= quantum
        else:
          t += rem_burst_times[i]
          a__  		=  t - burst_time
          a__  		=  0
    if done is True:
      return waiting_times
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->list[int]:

  '''simple docstring'''




  return [burst + waiting for burst, waiting in zip(lowerCamelCase__			,					lowerCamelCase__       )]


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     [3, 5, 7]
     _SCREAMING_SNAKE_CASE     =     calculate_waiting_times(burst_times)
     _SCREAMING_SNAKE_CASE     =     calculate_turn_around_times(burst_times, waiting_times)
     print("""Process ID \tBurst Time \tWaiting Time \tTurnaround Time""")
     for i, burst_time in enumerate(burst_times):
          print(
              F"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
              F"""{turn_around_times[i]}"""
          )
     print(F"""\nAverage waiting time = {mean(waiting_times):.5f}""")
     print(F"""Average turn around time = {mean(turn_around_times):.5f}""")

