





import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 	__snake_case					(      SCREAMING_SNAKE_CASE__ ):






    '''simple docstring'''
    _lowerCAmelCase  =						(KDPMaAncestralDiscreteScheduler,)
    _lowerCAmelCase  =						10





    def 						A		( self    :		Dict      ,    **snake_case    :		List[str]				):

      """simple docstring"""





      __lowerCAmelCase								=					{
          "num_train_timesteps": 1_100,
          "beta_start": 0.0001,
          "beta_end": 0.02,
          "beta_schedule": "linear",
      }

      config.update(**lowerCAmelCase__				)
      return config





    def 						A		( self    :		List[Any]				):

      """simple docstring"""





      for timesteps in [10, 50, 100, 1_000]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase__				)





    def 						A		( self    :		str				):

      """simple docstring"""





      for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]      ,    [0.0002, 0.002, 0.02]				):
        self.check_over_configs(beta_start=lowerCAmelCase__      ,    beta_end=lowerCAmelCase__				)





    def 						A		( self    :		List[Any]				):

      """simple docstring"""





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





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

      """simple docstring"""





      if torch_device == "mps":
        return
      __lowerCAmelCase								=					self.scheduler_classes[0]
      __lowerCAmelCase								=					self.get_scheduler_config()
      __lowerCAmelCase								=					scheduler_class(**lowerCAmelCase__				)

      scheduler.set_timesteps(self.num_inference_steps				)

      __lowerCAmelCase								=					torch.manual_seed(0				)

      __lowerCAmelCase								=					self.dummy_model()
      __lowerCAmelCase								=					self.dummy_sample_deter * scheduler.init_noise_sigma
      __lowerCAmelCase								=					sample.to(lowerCAmelCase__				)

      for i, t in enumerate(scheduler.timesteps				):
        __lowerCAmelCase								=					scheduler.scale_model_input(lowerCAmelCase__      ,    lowerCAmelCase__				)

        __lowerCAmelCase								=					model(lowerCAmelCase__      ,    lowerCAmelCase__				)

        __lowerCAmelCase								=					scheduler.step(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    generator=lowerCAmelCase__				)
        __lowerCAmelCase								=					output.prev_sample

      __lowerCAmelCase								=					torch.sum(torch.abs(lowerCAmelCase__				)				)
      __lowerCAmelCase								=					torch.mean(torch.abs(lowerCAmelCase__				)				)

      assert abs(result_sum.item() - 13_849.3_877				) < 1e-2
      assert abs(result_mean.item() - 18.0331				) < 5e-3





    def 						A		( self    :		Dict				):

      """simple docstring"""





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





    def 						A		( self    :		Dict				):

      """simple docstring"""





      if torch_device == "mps":
        return
      __lowerCAmelCase								=					self.scheduler_classes[0]
      __lowerCAmelCase								=					self.get_scheduler_config(prediction_type='''v_prediction'''				)
      __lowerCAmelCase								=					scheduler_class(**lowerCAmelCase__				)

      scheduler.set_timesteps(self.num_inference_steps				)

      __lowerCAmelCase								=					self.dummy_model()
      __lowerCAmelCase								=					self.dummy_sample_deter * scheduler.init_noise_sigma
      __lowerCAmelCase								=					sample.to(lowerCAmelCase__				)

      __lowerCAmelCase								=					torch.manual_seed(0				)

      for i, t in enumerate(scheduler.timesteps				):
        __lowerCAmelCase								=					scheduler.scale_model_input(lowerCAmelCase__      ,    lowerCAmelCase__				)

        __lowerCAmelCase								=					model(lowerCAmelCase__      ,    lowerCAmelCase__				)

        __lowerCAmelCase								=					scheduler.step(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    generator=lowerCAmelCase__				)
        __lowerCAmelCase								=					output.prev_sample

      __lowerCAmelCase								=					torch.sum(torch.abs(lowerCAmelCase__				)				)
      __lowerCAmelCase								=					torch.mean(torch.abs(lowerCAmelCase__				)				)

      assert abs(result_sum.item() - 328.9970				) < 1e-2
      assert abs(result_mean.item() - 0.4284				) < 1e-3





    def 						A		( self    :		Optional[int]				):

      """simple docstring"""





      if torch_device == "mps":
        return
      __lowerCAmelCase								=					self.scheduler_classes[0]
      __lowerCAmelCase								=					self.get_scheduler_config()
      __lowerCAmelCase								=					scheduler_class(**lowerCAmelCase__				)

      scheduler.set_timesteps(self.num_inference_steps      ,    device=lowerCAmelCase__				)
      __lowerCAmelCase								=					torch.manual_seed(0				)

      __lowerCAmelCase								=					self.dummy_model()
      __lowerCAmelCase								=					self.dummy_sample_deter.to(lowerCAmelCase__				) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
        __lowerCAmelCase								=					scheduler.scale_model_input(lowerCAmelCase__      ,    lowerCAmelCase__				)

        __lowerCAmelCase								=					model(lowerCAmelCase__      ,    lowerCAmelCase__				)

        __lowerCAmelCase								=					scheduler.step(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    generator=lowerCAmelCase__				)
        __lowerCAmelCase								=					output.prev_sample

      __lowerCAmelCase								=					torch.sum(torch.abs(lowerCAmelCase__				)				)
      __lowerCAmelCase								=					torch.mean(torch.abs(lowerCAmelCase__				)				)

      assert abs(result_sum.item() - 13_849.3_818				) < 1e-1
      assert abs(result_mean.item() - 18.0331				) < 1e-3






"""simple docstring"""
import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Union[str, Any]						)    ->       int:
 return EnvironmentCommand()







class  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__				):



 '''simple docstring'''





 @staticmethod
 def lowercase_      (lowerCAmelCase__   ):

  '''simple docstring'''







  _UpperCamelCase      :	Optional[Any]					      =  parser.add_parser("env"					,     help="Print relevant system environment info."   )
  download_parser.set_defaults(func=lowerCAmelCase__   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Any					      =  {
      "`datasets` version": version,
      "Platform": platform.platform(),
      "Python version": platform.python_version(),
      "Huggingface_hub version": huggingface_hub.__version__,
      "PyArrow version": pyarrow.__version__,
      "Pandas version": pandas.__version__,
  }

  print("\nCopy-and-paste the text below in your GitHub issue.\n"   )
  print(self.format_dict(lowerCAmelCase__   )   )

  return info






 @staticmethod
 def lowercase_      (lowerCAmelCase__   ):

  '''simple docstring'''







  return "\n".join([F"- {prop}: {val}" for prop, val in d.items()]   ) + "\n"



