




'''simple docstring'''





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 		SCREAMING_SNAKE_CASE			(     __a							):




				'''simple docstring'''





				__UpperCamelCase     				=   (DDPMParallelScheduler,)







				def        _UpperCamelCase							(   self  ,						**SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       Tuple        =       {
							    'num_train_timesteps': 10_00,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'variance_type': 'fixed_small',
							    'clip_sample': True,
							}

							config.update(**lowercase_				)
							return config







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							for timesteps in [1, 5, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=lowercase_				)







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,						[0.0_02, 0.02, 0.2, 2]				):
										self.check_over_configs(beta_start=lowercase_  ,						beta_end=lowercase_				)







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







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







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







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







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







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







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







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







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







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







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							for t in [0, 5_00, 9_99]:
										self.check_over_forward(time_step=lowercase_				)







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       List[str]        =       self.scheduler_classes[0]
							snake_case:       int        =       self.get_scheduler_config()
							snake_case:       Any        =       scheduler_class(**lowercase_				)

							assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87				) - 0.0_09_79				)				) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99				) - 0.02				)				) < 1E-5







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       Any        =       self.scheduler_classes[0]
							snake_case:       Union[str, Any]        =       self.get_scheduler_config()
							snake_case:       str        =       scheduler_class(**lowercase_				)

							snake_case:       Optional[Any]        =       len(lowercase_				)

							snake_case:       Union[str, Any]        =       self.dummy_model()
							snake_case:       List[Any]        =       self.dummy_sample_deter
							snake_case:       List[Any]        =       self.dummy_sample_deter + 0.1
							snake_case:       str        =       self.dummy_sample_deter - 0.1

							snake_case:       List[Any]        =       samplea.shape[0]
							snake_case:       List[str]        =       torch.stack([samplea, samplea, samplea]  ,						dim=0				)
							snake_case:       Any        =       torch.arange(lowercase_				)[0:3, None].repeat(1  ,						lowercase_				)

							snake_case:       Optional[int]        =       model(samples.flatten(0  ,						1				)  ,						timesteps.flatten(0  ,						1				)				)
							snake_case:       int        =       scheduler.batch_step_no_noise(lowercase_  ,						timesteps.flatten(0  ,						1				)  ,						samples.flatten(0  ,						1				)				)

							snake_case:       List[Any]        =       torch.sum(torch.abs(lowercase_				)				)
							snake_case:       Tuple        =       torch.mean(torch.abs(lowercase_				)				)

							assert abs(result_sum.item() - 11_53.18_33				) < 1E-2
							assert abs(result_mean.item() - 0.50_05				) < 1E-3







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       Dict        =       self.scheduler_classes[0]
							snake_case:       List[Any]        =       self.get_scheduler_config()
							snake_case:       Dict        =       scheduler_class(**lowercase_				)

							snake_case:       List[Any]        =       len(lowercase_				)

							snake_case:       Tuple        =       self.dummy_model()
							snake_case:       Union[str, Any]        =       self.dummy_sample_deter
							snake_case:       Optional[int]        =       torch.manual_seed(0				)

							for t in reversed(range(lowercase_				)				):
										# 1. predict noise residual
										snake_case:       int        =       model(lowercase_  ,						lowercase_				)

										# 2. predict previous mean of sample x_t-1
										snake_case:       str        =       scheduler.step(lowercase_  ,						lowercase_  ,						lowercase_  ,						generator=lowercase_				).prev_sample

										snake_case:       str        =       pred_prev_sample

							snake_case:       str        =       torch.sum(torch.abs(lowercase_				)				)
							snake_case:       List[str]        =       torch.mean(torch.abs(lowercase_				)				)

							assert abs(result_sum.item() - 2_58.96_06				) < 1E-2
							assert abs(result_mean.item() - 0.33_72				) < 1E-3







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       Any        =       self.scheduler_classes[0]
							snake_case:       str        =       self.get_scheduler_config(prediction_type='v_prediction'				)
							snake_case:       List[str]        =       scheduler_class(**lowercase_				)

							snake_case:       List[str]        =       len(lowercase_				)

							snake_case:       Union[str, Any]        =       self.dummy_model()
							snake_case:       Union[str, Any]        =       self.dummy_sample_deter
							snake_case:       Tuple        =       torch.manual_seed(0				)

							for t in reversed(range(lowercase_				)				):
										# 1. predict noise residual
										snake_case:       Optional[int]        =       model(lowercase_  ,						lowercase_				)

										# 2. predict previous mean of sample x_t-1
										snake_case:       Optional[int]        =       scheduler.step(lowercase_  ,						lowercase_  ,						lowercase_  ,						generator=lowercase_				).prev_sample

										snake_case:       Tuple        =       pred_prev_sample

							snake_case:       Union[str, Any]        =       torch.sum(torch.abs(lowercase_				)				)
							snake_case:       List[Any]        =       torch.mean(torch.abs(lowercase_				)				)

							assert abs(result_sum.item() - 2_02.02_96				) < 1E-2
							assert abs(result_mean.item() - 0.26_31				) < 1E-3







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       Dict        =       self.scheduler_classes[0]
							snake_case:       List[Any]        =       self.get_scheduler_config()
							snake_case:       Union[str, Any]        =       scheduler_class(**lowercase_				)

							snake_case:       Tuple        =       [1_00, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=lowercase_				)

							snake_case:       List[Any]        =       scheduler.timesteps

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

										snake_case:       Dict        =       scheduler.previous_timestep(lowercase_				)
										snake_case:       Union[str, Any]        =       prev_t.item()

										self.assertEqual(lowercase_  ,						lowercase_				)







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       Dict        =       self.scheduler_classes[0]
							snake_case:       int        =       self.get_scheduler_config()
							snake_case:       Dict        =       scheduler_class(**lowercase_				)

							snake_case:       List[Any]        =       [1_00, 87, 50, 51, 0]

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







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







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

							snake_case:       Any        =       [1_00, 87, 50, 1, 0]
							snake_case:       Tuple        =       len(lowercase_				)

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







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       Dict        =       self.scheduler_classes[0]
							snake_case:       int        =       self.get_scheduler_config()
							snake_case:       List[Any]        =       scheduler_class(**lowercase_				)

							snake_case:       List[Any]        =       [scheduler.config.num_train_timesteps]

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


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
       import torch

if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm


__snake_case						  =		logging.get_logger(__name__)





@dataclass
class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						[
      '''no_inference''',
      '''no_cuda''',
      '''no_tpu''',
      '''no_speed''',
      '''no_memory''',
      '''no_env_print''',
      '''no_multi_process''',
  ]




  def __init__(		self      :       int   ,				**lowercase_      :       int      ):







    '''simple docstring'''


    for deprecated_arg in self.deprecated_args:
      if deprecated_arg in kwargs:
        lowercase_     =       deprecated_arg[3:]
        setattr(self   ,				lowercase_   ,				not kwargs.pop(lowercase_      )      )
        logger.warning(
            F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
            F""" {positive_arg}={kwargs[positive_arg]}"""      )

    lowercase_     =       kwargs.pop("""torchscript"""   ,				self.torchscript      )
    lowercase_     =       kwargs.pop("""torch_xla_tpu_print_metrics"""   ,				self.torch_xla_tpu_print_metrics      )
    lowercase_     =       kwargs.pop("""fp16_opt_level"""   ,				self.fpaa_opt_level      )
    super().__init__(**lowercase_      )

  A_    							=						field(default=__a ,				metadata={'''help''': '''Trace the models using torchscript'''}      )
  A_    							=						field(default=__a ,				metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}      )
  A_    							=						field(
      default='''O1''' ,				metadata={
          '''help''': (
              '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
              '''See details at https://nvidia.github.io/apex/amp.html'''
          )
      } ,				)




  @cached_property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    logger.info("""PyTorch: setting up devices"""      )
    if not self.cuda:
      lowercase_     =       torch.device("""cpu"""      )
      lowercase_     =       0
    elif is_torch_tpu_available():
      lowercase_     =       xm.xla_device()
      lowercase_     =       0
    else:
      lowercase_     =       torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""      )
      lowercase_     =       torch.cuda.device_count()
    return device, n_gpu




  @property
  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    return is_torch_tpu_available() and self.tpu




  @property
  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    # TODO(PVP): currently only single GPU is supported
    return torch.cuda.current_device()




  @property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    return self._setup_devices[0]




  @property
  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    return self._setup_devices[1]





  @property
  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    return self.n_gpu > 0

