"""simple docstring"""






import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler
@dataclass
class 	UpperCAmelCase__					(     SCREAMING_SNAKE_CASE__      ):


	"""simple docstring"""
	lowerCAmelCase__     :       int       =				0
	lowerCAmelCase__     :       bool       =				False
	lowerCAmelCase__     :       float       =				3.0




class 	UpperCAmelCase__					(     unittest.TestCase      ):


	"""simple docstring"""







	def    A     (      self			)     ->						int:
							# If no defaults are changed, `to_kwargs` returns an empty dict.
							self.assertDictEqual(MockClass().to_kwargs()							,  {}			)
							self.assertDictEqual(MockClass(a=2			).to_kwargs()							,  {"a": 2}			)
							self.assertDictEqual(MockClass(a=2							,  b=__lowerCAmelCase			).to_kwargs()							,  {"a": 2, "b": True}			)
							self.assertDictEqual(MockClass(a=2							,  c=2.2_5			).to_kwargs()							,  {"a": 2, "c": 2.2_5}			)







	@require_cuda
	def    A     (      self			)     ->						int:
							# If no defaults are changed, `to_kwargs` returns an empty dict.
							a_				:      str  		=       GradScalerKwargs(init_scale=1_0_2_4							,  growth_factor=2			)
							AcceleratorState._reset_state()
							a_				:      Any  		=       Accelerator(mixed_precision="fp16"							,  kwargs_handlers=[scaler_handler]			)
							print(accelerator.use_fpaa			)
							a_				:      Tuple  		=       accelerator.scaler

							# Check the kwargs have been applied
							self.assertEqual(scaler._init_scale							,  1_0_2_4.0			)
							self.assertEqual(scaler._growth_factor							,  2.0			)

							# Check the other values are at the default
							self.assertEqual(scaler._backoff_factor							,  0.5			)
							self.assertEqual(scaler._growth_interval							,  2_0_0_0			)
							self.assertEqual(scaler._enabled							,  __lowerCAmelCase			)







	@require_multi_gpu
	def    A     (      self			)     ->						int:
							a_				:      Any  		=       ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__			)]
							execute_subprocess_async(__lowerCAmelCase							,  env=os.environ.copy()			)


if __name__ == "__main__":
				UpperCamelCase         =  DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
				UpperCamelCase         =  Accelerator(kwargs_handlers=[ddp_scaler])
				UpperCamelCase         =  torch.nn.Linear(1_00, 2_00)
				UpperCamelCase         =  accelerator.prepare(model)

				# Check the values changed in kwargs
				UpperCamelCase         =  ''
				UpperCamelCase         =  model.bucket_bytes_cap // (10_24 * 10_24)
				if observed_bucket_cap_map != 15:
								error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
				if model.find_unused_parameters is not True:
								error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

				# Check the values of the defaults
				if model.dim != 0:
								error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
				if model.broadcast_buffers is not True:
								error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
				if model.gradient_as_bucket_view is not False:
								error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

				# Raise error at the end to make sure we don't stop at the first failure.
				if len(error_msg) > 0:
								raise ValueError(error_msg)












'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    UNetaDModel
 __a      :    ScoreSdeVeScheduler
 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       super().__init__()
       self.register_modules(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)





 @torch.no_grad()
 def __call__(				self      ,  __lowerCAmelCase = 1      ,  __lowerCAmelCase = 20_00      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = "pil"      ,  __lowerCAmelCase = True      ,  **__lowerCAmelCase      ,  ):
       UpperCamelCase_					: str            =				self.unet.config.sample_size
       UpperCamelCase_					: int            =				(batch_size, 3, img_size, img_size)

       UpperCamelCase_					: List[Any]            =				self.unet

       UpperCamelCase_					: Union[str, Any]            =				randn_tensor(__lowerCAmelCase      ,  generator=__lowerCAmelCase			) * self.scheduler.init_noise_sigma
       UpperCamelCase_					: Union[str, Any]            =				sample.to(self.device			)

       self.scheduler.set_timesteps(__lowerCAmelCase			)
       self.scheduler.set_sigmas(__lowerCAmelCase			)

       for i, t in enumerate(self.progress_bar(self.scheduler.timesteps			)			):
             UpperCamelCase_					: Union[str, Any]            =				self.scheduler.sigmas[i] * torch.ones(shape[0]      ,  device=self.device			)

             # correction step
             for _ in range(self.scheduler.config.correct_steps			):
                   UpperCamelCase_					: List[str]            =				self.unet(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
                   UpperCamelCase_					: List[str]            =				self.scheduler.step_correct(__lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			).prev_sample

             # prediction step
             UpperCamelCase_					: List[Any]            =				model(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
             UpperCamelCase_					: Tuple            =				self.scheduler.step_pred(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			)

             UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				output.prev_sample, output.prev_sample_mean

       UpperCamelCase_					: Tuple            =				sample_mean.clamp(0      ,  1			)
       UpperCamelCase_					: str            =				sample.cpu().permute(0      ,  2      ,  3      ,  1			).numpy()
       if output_type == "pil":
             UpperCamelCase_					: Dict            =				self.numpy_to_pil(__lowerCAmelCase			)

       if not return_dict:
             return (sample,)

       return ImagePipelineOutput(images=__lowerCAmelCase			)






