


"""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      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							0
		A    =							False
		A    =							3.0






class      A(  unittest.TestCase		):

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					self.assertDictEqual(MockClass().to_kwargs()					,				{}   )
					self.assertDictEqual(MockClass(a=2   ).to_kwargs()					,				{'''a''': 2}   )
					self.assertDictEqual(MockClass(a=2					,				b=SCREAMING_SNAKE_CASE__   ).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   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	GradScalerKwargs(init_scale=10_24					,				growth_factor=2   )
					AcceleratorState._reset_state()
					_UpperCamelCase					:Any					     =	Accelerator(mixed_precision='''fp16'''					,				kwargs_handlers=[scaler_handler]   )
					print(accelerator.use_fpaa   )
					_UpperCamelCase					:Dict					     =	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					,				20_00   )
					self.assertEqual(scaler._enabled					,				SCREAMING_SNAKE_CASE__   )





		@require_multi_gpu
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	['''torchrun''', f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__   )]
					execute_subprocess_async(SCREAMING_SNAKE_CASE__					,				env=os.environ.copy()   )


if __name__ == "__main__":
					UpperCamelCase__				:Any			   =	DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
					UpperCamelCase__				:Dict			   =	Accelerator(kwargs_handlers=[ddp_scaler])
					UpperCamelCase__				:List[Any]			   =	torch.nn.Linear(100, 200)
					UpperCamelCase__				:Optional[Any]			   =	accelerator.prepare(model)

					# Check the values changed in kwargs
					UpperCamelCase__				:Optional[Any]			   =	""""""
					UpperCamelCase__				:List[str]			   =	model.bucket_bytes_cap // (1_024 * 1_024)
					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 ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					super().__init__()

					# make sure scheduler can always be converted to DDIM
					_UpperCamelCase					:int					     =	DDIMScheduler.from_config(scheduler.config   )

					self.register_modules(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )





		@torch.no_grad()
		def __call__( self					,				SCREAMING_SNAKE_CASE__ = 1					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = 0.0					,				SCREAMING_SNAKE_CASE__ = 50					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = "pil"					,				SCREAMING_SNAKE_CASE__ = True					,				)      ->	Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""



					if isinstance(self.unet.config.sample_size					,				SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:Optional[int]					     =	(
								    batch_size,
								    self.unet.config.in_channels,
								    self.unet.config.sample_size,
								    self.unet.config.sample_size,
								)
					else:
								_UpperCamelCase					:Tuple					     =	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) and len(SCREAMING_SNAKE_CASE__   ) != batch_size:
								raise ValueError(
								    f"You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE__   )}, but requested an effective batch"
								    f" size of {batch_size}. Make sure the batch size matches the length of the generators."   )

					_UpperCamelCase					:Optional[int]					     =	randn_tensor(SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__					,				device=self.device					,				dtype=self.unet.dtype   )

					# set step values
					self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

					for t in self.progress_bar(self.scheduler.timesteps   ):
								# 1. predict noise model_output
								_UpperCamelCase					:List[str]					     =	self.unet(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								_UpperCamelCase					:Optional[Any]					     =	self.scheduler.step(
								    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				eta=SCREAMING_SNAKE_CASE__					,				use_clipped_model_output=SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__   ).prev_sample

					_UpperCamelCase					:str					     =	(image / 2 + 0.5).clamp(0					,				1   )
					_UpperCamelCase					:int					     =	image.cpu().permute(0					,				2					,				3					,				1   ).numpy()
					if output_type == "pil":
								_UpperCamelCase					:Union[str, Any]					     =	self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )

					if not return_dict:
								return (image,)

					return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   )

