



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 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					0
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					3.0




class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									# 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=a_      ).to_kwargs()			,     {"a": 2, "b": True}      )
									self.assertDictEqual(MockClass(a=2			,     c=2.25      ).to_kwargs()			,     {"a": 2, "c": 2.25}      )






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






							@require_multi_gpu
							def snake_case_  (   self      ):
									a_     :      str 						=     ["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__      )]
									execute_subprocess_async(a_			,     env=os.environ.copy()      )


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

					# Check the values changed in kwargs
					SCREAMING_SNAKE_CASE_				= """"""
					SCREAMING_SNAKE_CASE_				= 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"""
import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 			snake_case_						(						unittest.TestCase				):






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      Any 						=     UNetaDModel(
									    block_out_channels=(3_2, 6_4)			,     layers_per_block=2			,     sample_size=3_2			,     in_channels=3			,     out_channels=3			,     down_block_types=("DownBlock2D", "AttnDownBlock2D")			,     up_block_types=("AttnUpBlock2D", "UpBlock2D")			,     )
									return model






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.dummy_uncond_unet
									a_     :      Any 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )

									a_     :      Tuple 						=     torch.manual_seed(0      )
									a_     :      Union[str, Any] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"      ).images

									a_     :      int 						=     torch.manual_seed(0      )
									a_     :      Optional[int] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"			,     return_dict=a_      )[0]

									a_     :      List[Any] 						=     image[0, -3:, -3:, -1]
									a_     :      Union[str, Any] 						=     image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      List[Any] 						=     np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




@slow
@require_torch
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     "google/ddpm-cifar10-32"

									a_     :      Optional[int] 						=     UNetaDModel.from_pretrained(a_      )
									a_     :      Union[str, Any] 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )
									a_     :      Optional[int] 						=     torch.manual_seed(0      )
									a_     :      List[Any] 						=     pndm(generator=a_			,     output_type="numpy"      ).images

									a_     :      str 						=     image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      Optional[Any] 						=     np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2