



'''simple docstring'''

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 	a__     (				_lowercase	):
	__magic_name__		:    str	    =     (DDPMScheduler,)


	def 				lowercase__  (self : Dict,					**__UpperCAmelCase : Any      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= {
					    '''num_train_timesteps''': 1000,
					    '''beta_start''': 0.0001,
					    '''beta_end''': 0.02,
					    '''beta_schedule''': '''linear''',
					    '''variance_type''': '''fixed_small''',
					    '''clip_sample''': True,
					}

					config.update(**__UpperCAmelCase      )
					return config


	def 				lowercase__  (self : Optional[Any]      )     ->			Tuple:

					"""simple docstring"""

					for timesteps in [1, 5, 100, 1000]:
									self.check_over_configs(num_train_timesteps=__UpperCAmelCase      )


	def 				lowercase__  (self : Union[str, Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

					for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],					[0.002, 0.02, 0.2, 2]      ):
									self.check_over_configs(beta_start=__UpperCAmelCase,					beta_end=__UpperCAmelCase      )


	def 				lowercase__  (self : str      )     ->			List[str]:

					"""simple docstring"""

					for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[int]      )     ->			str:

					"""simple docstring"""

					for variance in ["fixed_small", "fixed_large", "other"]:
									self.check_over_configs(variance_type=__UpperCAmelCase      )


	def 				lowercase__  (self : str      )     ->			List[Any]:

					"""simple docstring"""

					for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[int]      )     ->			List[Any]:

					"""simple docstring"""

					self.check_over_configs(thresholding=__UpperCAmelCase      )
					for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(
													    thresholding=__UpperCAmelCase,					prediction_type=__UpperCAmelCase,					sample_max_value=__UpperCAmelCase,					)


	def 				lowercase__  (self : List[str]      )     ->			str:

					"""simple docstring"""

					for prediction_type in ["epsilon", "sample", "v_prediction"]:
									self.check_over_configs(prediction_type=__UpperCAmelCase      )


	def 				lowercase__  (self : List[Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

					for t in [0, 500, 999]:
									self.check_over_forward(time_step=__UpperCAmelCase      )


	def 				lowercase__  (self : int      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.get_scheduler_config()
					SCREAMING_SNAKE_CASE  :						Dict      					= scheduler_class(**__UpperCAmelCase      )

					assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_0979      )      ) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.02      )      ) < 1e-5


	def 				lowercase__  (self : Dict      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						Any      					= self.get_scheduler_config()
					SCREAMING_SNAKE_CASE  :						Dict      					= scheduler_class(**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= len(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Dict      					= self.dummy_model()
					SCREAMING_SNAKE_CASE  :						str      					= self.dummy_sample_deter
					SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.manual_seed(0      )

					for t in reversed(range(__UpperCAmelCase      )      ):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE  :						Tuple      					= model(__UpperCAmelCase,					__UpperCAmelCase      )

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE  :						Tuple      					= scheduler.step(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      ).prev_sample

									# if t > 0:
									#     noise = self.dummy_sample_deter
									#     variance = scheduler.get_variance(t) ** (0.5) * noise
									#
									# sample = pred_prev_sample + variance
									SCREAMING_SNAKE_CASE  :						str      					= pred_prev_sample

					SCREAMING_SNAKE_CASE  :						Tuple      					= torch.sum(torch.abs(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						str      					= torch.mean(torch.abs(__UpperCAmelCase      )      )

					assert abs(result_sum.item() - 258.9606      ) < 1e-2
					assert abs(result_mean.item() - 0.3372      ) < 1e-3


	def 				lowercase__  (self : List[Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.get_scheduler_config(prediction_type='''v_prediction'''      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= scheduler_class(**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= len(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.dummy_model()
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.dummy_sample_deter
					SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.manual_seed(0      )

					for t in reversed(range(__UpperCAmelCase      )      ):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE  :						Any      					= model(__UpperCAmelCase,					__UpperCAmelCase      )

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE  :						List[Any]      					= scheduler.step(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      ).prev_sample

									# if t > 0:
									#     noise = self.dummy_sample_deter
									#     variance = scheduler.get_variance(t) ** (0.5) * noise
									#
									# sample = pred_prev_sample + variance
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= pred_prev_sample

					SCREAMING_SNAKE_CASE  :						Any      					= torch.sum(torch.abs(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.mean(torch.abs(__UpperCAmelCase      )      )

					assert abs(result_sum.item() - 202.0296      ) < 1e-2
					assert abs(result_mean.item() - 0.2631      ) < 1e-3


	def 				lowercase__  (self : List[Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						int      					= self.get_scheduler_config()
					SCREAMING_SNAKE_CASE  :						int      					= scheduler_class(**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [100, 87, 50, 1, 0]

					scheduler.set_timesteps(timesteps=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= scheduler.timesteps

					for i, timestep in enumerate(__UpperCAmelCase      ):
									if i == len(__UpperCAmelCase      ) - 1:
													SCREAMING_SNAKE_CASE  :						str      					= -1
									else:
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= timesteps[i + 1]

									SCREAMING_SNAKE_CASE  :						Optional[int]      					= scheduler.previous_timestep(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Any      					= prev_t.item()

									self.assertEqual(__UpperCAmelCase,					__UpperCAmelCase      )


	def 				lowercase__  (self : int      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.get_scheduler_config()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= scheduler_class(**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Any      					= [100, 87, 50, 51, 0]

					with self.assertRaises(__UpperCAmelCase,					msg='''`custom_timesteps` must be in descending order.'''      ):
									scheduler.set_timesteps(timesteps=__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[Any]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.get_scheduler_config()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= scheduler_class(**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						int      					= [100, 87, 50, 1, 0]
					SCREAMING_SNAKE_CASE  :						List[str]      					= len(__UpperCAmelCase      )

					with self.assertRaises(__UpperCAmelCase,					msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''      ):
									scheduler.set_timesteps(num_inference_steps=__UpperCAmelCase,					timesteps=__UpperCAmelCase      )






	def 				lowercase__  (self : Tuple      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.get_scheduler_config()
					SCREAMING_SNAKE_CASE  :						Dict      					= scheduler_class(**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [scheduler.config.num_train_timesteps]

					with self.assertRaises(
					    __UpperCAmelCase,					msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''',					):
									scheduler.set_timesteps(timesteps=__UpperCAmelCase      )









'''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__     (				_lowercase	):
	__magic_name__		:    int	    =     0
	__magic_name__		:    bool	    =     False
	__magic_name__		:    float	    =     3.0



class 	a__     (				unittest.TestCase	):


	def 				lowercase__  (self : str      )     ->			Union[str, Any]:

					"""simple docstring"""

					self.assertDictEqual(MockClass().to_kwargs(),					{}      )
					self.assertDictEqual(MockClass(a=2      ).to_kwargs(),					{'''a''': 2}      )
					self.assertDictEqual(MockClass(a=2,					b=__UpperCAmelCase      ).to_kwargs(),					{'''a''': 2, '''b''': True}      )
					self.assertDictEqual(MockClass(a=2,					c=2.25      ).to_kwargs(),					{'''a''': 2, '''c''': 2.25}      )


	@require_cuda
	def 				lowercase__  (self : Dict      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= GradScalerKwargs(init_scale=1024,					growth_factor=2      )
					AcceleratorState._reset_state()
					SCREAMING_SNAKE_CASE  :						Tuple      					= Accelerator(mixed_precision='''fp16''',					kwargs_handlers=[scaler_handler]      )
					print(accelerator.use_fpaa      )
					SCREAMING_SNAKE_CASE  :						str      					= accelerator.scaler

					# Check the kwargs have been applied
					self.assertEqual(scaler._init_scale,					1024.0      )
					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,					2000      )
					self.assertEqual(scaler._enabled,					__UpperCAmelCase      )






	@require_multi_gpu
	def 				lowercase__  (self : Optional[Any]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
					execute_subprocess_async(__UpperCAmelCase,					env=os.environ.copy()      )


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

	# Check the values changed in kwargs
	snake_case_			=     """"""
	snake_case_			=     model.bucket_bytes_cap // (1024 * 1024)
	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)




