

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[Any]       				=  (PNDMScheduler,)
	_lowercase  :   List[Any]       				=  (("num_inference_steps", 5_0),)







	def 	_SCREAMING_SNAKE_CASE							(   self: str  , **__UpperCamelCase: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    {
						    'num_train_timesteps': 10_00,
						    'beta_start': 0.0001,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**__UpperCamelCase      )
						return config







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict  , __UpperCamelCase: Optional[Any]=0  , **__UpperCamelCase: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    dict(self.forward_default_kwargs      )
						__magic_name__							=    kwargs.pop('num_inference_steps'  , __UpperCamelCase      )
						__magic_name__							=    self.dummy_sample
						__magic_name__							=    0.1 * sample
						__magic_name__							=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

						for scheduler_class in self.scheduler_classes:
											__magic_name__							=    self.get_scheduler_config(**__UpperCamelCase      )
											__magic_name__							=    scheduler_class(**__UpperCamelCase      )
											scheduler.set_timesteps(__UpperCamelCase      )
											# copy over dummy past residuals
											__magic_name__							=    dummy_past_residuals[:]

											with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(__UpperCamelCase      )
																__magic_name__							=    scheduler_class.from_pretrained(__UpperCamelCase      )
																new_scheduler.set_timesteps(__UpperCamelCase      )
																# copy over dummy past residuals
																__magic_name__							=    dummy_past_residuals[:]

											__magic_name__							=    scheduler.step_prk(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , **__UpperCamelCase      ).prev_sample
											__magic_name__							=    new_scheduler.step_prk(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , **__UpperCamelCase      ).prev_sample

											assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"

											__magic_name__							=    scheduler.step_plms(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , **__UpperCamelCase      ).prev_sample
											__magic_name__							=    new_scheduler.step_plms(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , **__UpperCamelCase      ).prev_sample

											assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						pass







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict  , __UpperCamelCase: str=0  , **__UpperCamelCase: str      ):

						'''simple docstring'''





						__magic_name__							=    dict(self.forward_default_kwargs      )
						__magic_name__							=    kwargs.pop('num_inference_steps'  , __UpperCamelCase      )
						__magic_name__							=    self.dummy_sample
						__magic_name__							=    0.1 * sample
						__magic_name__							=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

						for scheduler_class in self.scheduler_classes:
											__magic_name__							=    self.get_scheduler_config()
											__magic_name__							=    scheduler_class(**__UpperCamelCase      )
											scheduler.set_timesteps(__UpperCamelCase      )

											# copy over dummy past residuals (must be after setting timesteps)
											__magic_name__							=    dummy_past_residuals[:]

											with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(__UpperCamelCase      )
																__magic_name__							=    scheduler_class.from_pretrained(__UpperCamelCase      )
																# copy over dummy past residuals
																new_scheduler.set_timesteps(__UpperCamelCase      )

																# copy over dummy past residual (must be after setting timesteps)
																__magic_name__							=    dummy_past_residuals[:]

											__magic_name__							=    scheduler.step_prk(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , **__UpperCamelCase      ).prev_sample
											__magic_name__							=    new_scheduler.step_prk(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , **__UpperCamelCase      ).prev_sample

											assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"

											__magic_name__							=    scheduler.step_plms(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , **__UpperCamelCase      ).prev_sample
											__magic_name__							=    new_scheduler.step_plms(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , **__UpperCamelCase      ).prev_sample

											assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , **__UpperCamelCase: Dict      ):

						'''simple docstring'''





						__magic_name__							=    self.scheduler_classes[0]
						__magic_name__							=    self.get_scheduler_config(**__UpperCamelCase      )
						__magic_name__							=    scheduler_class(**__UpperCamelCase      )

						__magic_name__							=    10
						__magic_name__							=    self.dummy_model()
						__magic_name__							=    self.dummy_sample_deter
						scheduler.set_timesteps(__UpperCamelCase      )

						for i, t in enumerate(scheduler.prk_timesteps      ):
											__magic_name__							=    model(__UpperCamelCase  , __UpperCamelCase      )
											__magic_name__							=    scheduler.step_prk(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      ).prev_sample

						for i, t in enumerate(scheduler.plms_timesteps      ):
											__magic_name__							=    model(__UpperCamelCase  , __UpperCamelCase      )
											__magic_name__							=    scheduler.step_plms(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      ).prev_sample

						return sample







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    dict(self.forward_default_kwargs      )

						__magic_name__							=    kwargs.pop('num_inference_steps'  , __UpperCamelCase      )

						for scheduler_class in self.scheduler_classes:
											__magic_name__							=    self.get_scheduler_config()
											__magic_name__							=    scheduler_class(**__UpperCamelCase      )

											__magic_name__							=    self.dummy_sample
											__magic_name__							=    0.1 * sample

											if num_inference_steps is not None and hasattr(__UpperCamelCase  , 'set_timesteps'      ):
																scheduler.set_timesteps(__UpperCamelCase      )
											elif num_inference_steps is not None and not hasattr(__UpperCamelCase  , 'set_timesteps'      ):
																__magic_name__							=    num_inference_steps

											# copy over dummy past residuals (must be done after set_timesteps)
											__magic_name__							=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
											__magic_name__							=    dummy_past_residuals[:]

											__magic_name__							=    scheduler.step_prk(__UpperCamelCase  , 0  , __UpperCamelCase  , **__UpperCamelCase      ).prev_sample
											__magic_name__							=    scheduler.step_prk(__UpperCamelCase  , 1  , __UpperCamelCase  , **__UpperCamelCase      ).prev_sample

											self.assertEqual(output_a.shape  , sample.shape      )
											self.assertEqual(output_a.shape  , output_a.shape      )

											__magic_name__							=    scheduler.step_plms(__UpperCamelCase  , 0  , __UpperCamelCase  , **__UpperCamelCase      ).prev_sample
											__magic_name__							=    scheduler.step_plms(__UpperCamelCase  , 1  , __UpperCamelCase  , **__UpperCamelCase      ).prev_sample

											self.assertEqual(output_a.shape  , sample.shape      )
											self.assertEqual(output_a.shape  , output_a.shape      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						for timesteps in [1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=__UpperCamelCase      )

						__magic_name__							=    self.scheduler_classes[0]
						__magic_name__							=    self.get_scheduler_config(steps_offset=1      )
						__magic_name__							=    scheduler_class(**__UpperCamelCase      )
						scheduler.set_timesteps(10      )
						assert torch.equal(
						    scheduler.timesteps  , torch.LongTensor(
						        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]      )  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						for beta_start, beta_end in zip([0.0001, 0.001]  , [0.002, 0.02]      ):
											self.check_over_configs(beta_start=__UpperCamelCase  , beta_end=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						for t in [1, 5, 10]:
											self.check_over_forward(time_step=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						for t, num_inference_steps in zip([1, 5, 10]  , [10, 50, 1_00]      ):
											self.check_over_forward(num_inference_steps=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    27

						for scheduler_class in self.scheduler_classes:
											__magic_name__							=    self.dummy_sample
											__magic_name__							=    0.1 * sample

											__magic_name__							=    self.get_scheduler_config()
											__magic_name__							=    scheduler_class(**__UpperCamelCase      )

											scheduler.set_timesteps(__UpperCamelCase      )

											# before power of 3 fix, would error on first step, so we only need to do two
											for i, t in enumerate(scheduler.prk_timesteps[:2]      ):
																__magic_name__							=    scheduler.step_prk(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      ).prev_sample







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						with self.assertRaises(__UpperCamelCase      ):
											__magic_name__							=    self.scheduler_classes[0]
											__magic_name__							=    self.get_scheduler_config()
											__magic_name__							=    scheduler_class(**__UpperCamelCase      )

											scheduler.step_plms(self.dummy_sample  , 1  , self.dummy_sample      ).prev_sample







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    self.full_loop()
						__magic_name__							=    torch.sum(torch.abs(__UpperCamelCase      )      )
						__magic_name__							=    torch.mean(torch.abs(__UpperCamelCase      )      )

						assert abs(result_sum.item() - 198.1318      ) < 1E-2
						assert abs(result_mean.item() - 0.2580      ) < 1E-3







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    self.full_loop(prediction_type='v_prediction'      )
						__magic_name__							=    torch.sum(torch.abs(__UpperCamelCase      )      )
						__magic_name__							=    torch.mean(torch.abs(__UpperCamelCase      )      )

						assert abs(result_sum.item() - 67.3986      ) < 1E-2
						assert abs(result_mean.item() - 0.0878      ) < 1E-3







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						__magic_name__							=    self.full_loop(set_alpha_to_one=__UpperCamelCase  , beta_start=0.01      )
						__magic_name__							=    torch.sum(torch.abs(__UpperCamelCase      )      )
						__magic_name__							=    torch.mean(torch.abs(__UpperCamelCase      )      )

						assert abs(result_sum.item() - 230.0399      ) < 1E-2
						assert abs(result_mean.item() - 0.2995      ) < 1E-3







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						__magic_name__							=    self.full_loop(set_alpha_to_one=__UpperCamelCase  , beta_start=0.01      )
						__magic_name__							=    torch.sum(torch.abs(__UpperCamelCase      )      )
						__magic_name__							=    torch.mean(torch.abs(__UpperCamelCase      )      )

						assert abs(result_sum.item() - 186.9482      ) < 1E-2
						assert abs(result_mean.item() - 0.2434      ) < 1E-3







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			):
	_lowercase  :   int       				=  0
	_lowercase  :   bool       				=  False
	_lowercase  :   float       				=  3.0




class 				__UpperCamelCase				(					unittest.TestCase			):







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''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 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    GradScalerKwargs(init_scale=10_24  , growth_factor=2      )
						AcceleratorState._reset_state()
						__magic_name__							=    Accelerator(mixed_precision='fp16'  , kwargs_handlers=[scaler_handler]      )
						print(accelerator.use_fpaa      )
						__magic_name__							=    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  , 20_00      )
						self.assertEqual(scaler._enabled  , __UpperCamelCase      )







	@require_multi_gpu
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





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


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

						# Check the values changed in kwargs
						A__						=							""
						A__						=							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)




