'''simple docstring'''




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						int      			=		(EulerAncestralDiscreteScheduler,)
      __UpperCamelCase:						str      			=		1_0




      def   _A			( self		:       List[Any]		,      **A		:       Union[str, Any] ):
             _UpperCAmelCase  :						str							   =					{
                 "num_train_timesteps": 1100,
                 "beta_start": 0.0_001,
                 "beta_end": 0.02,
                 "beta_schedule": "linear",
             }

             config.update(**A )
             return config




      def   _A			( self		:       Tuple ):
             for timesteps in [10, 50, 100, 1000]:
                    self.check_over_configs(num_train_timesteps=A )




      def   _A			( self		:       Optional[int] ):
             for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,      [0.0_002, 0.002, 0.02] ):
                    self.check_over_configs(beta_start=A		,      beta_end=A )




      def   _A			( self		:       str ):
             for schedule in ["linear", "scaled_linear"]:
                    self.check_over_configs(beta_schedule=A )




      def   _A			( self		:       Union[str, Any] ):
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=A )




      def   _A			( self		:       Any ):
             _UpperCAmelCase  :						Any							   =					self.scheduler_classes[0]
             _UpperCAmelCase  :						Any							   =					self.get_scheduler_config()
             _UpperCAmelCase  :						Tuple							   =					scheduler_class(**A )

             scheduler.set_timesteps(self.num_inference_steps )

             _UpperCAmelCase  :						List[str]							   =					torch.manual_seed(0 )

             _UpperCAmelCase  :						Optional[int]							   =					self.dummy_model()
             _UpperCAmelCase  :						str							   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
             _UpperCAmelCase  :						Optional[int]							   =					sample.to(A )

             for i, t in enumerate(scheduler.timesteps ):
                    _UpperCAmelCase  :						Any							   =					scheduler.scale_model_input(A		,      A )

                    _UpperCAmelCase  :						Optional[int]							   =					model(A		,      A )

                    _UpperCAmelCase  :						int							   =					scheduler.step(A		,      A		,      A		,      generator=A )
                    _UpperCAmelCase  :						str							   =					output.prev_sample

             _UpperCAmelCase  :						Optional[int]							   =					torch.sum(torch.abs(A ) )
             _UpperCAmelCase  :						Tuple							   =					torch.mean(torch.abs(A ) )

             assert abs(result_sum.item() - 152.3_192 ) < 1E-2
             assert abs(result_mean.item() - 0.1_983 ) < 1E-3




      def   _A			( self		:       Dict ):
             _UpperCAmelCase  :						Union[str, Any]							   =					self.scheduler_classes[0]
             _UpperCAmelCase  :						Tuple							   =					self.get_scheduler_config(prediction_type="v_prediction" )
             _UpperCAmelCase  :						Union[str, Any]							   =					scheduler_class(**A )

             scheduler.set_timesteps(self.num_inference_steps )

             _UpperCAmelCase  :						Union[str, Any]							   =					torch.manual_seed(0 )

             _UpperCAmelCase  :						List[str]							   =					self.dummy_model()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.dummy_sample_deter * scheduler.init_noise_sigma
             _UpperCAmelCase  :						Union[str, Any]							   =					sample.to(A )

             for i, t in enumerate(scheduler.timesteps ):
                    _UpperCAmelCase  :						List[str]							   =					scheduler.scale_model_input(A		,      A )

                    _UpperCAmelCase  :						Optional[Any]							   =					model(A		,      A )

                    _UpperCAmelCase  :						Optional[Any]							   =					scheduler.step(A		,      A		,      A		,      generator=A )
                    _UpperCAmelCase  :						Any							   =					output.prev_sample

             _UpperCAmelCase  :						int							   =					torch.sum(torch.abs(A ) )
             _UpperCAmelCase  :						Dict							   =					torch.mean(torch.abs(A ) )

             assert abs(result_sum.item() - 108.4_439 ) < 1E-2
             assert abs(result_mean.item() - 0.1_412 ) < 1E-3





      def   _A			( self		:       int ):
             _UpperCAmelCase  :						Union[str, Any]							   =					self.scheduler_classes[0]
             _UpperCAmelCase  :						str							   =					self.get_scheduler_config()
             _UpperCAmelCase  :						int							   =					scheduler_class(**A )

             scheduler.set_timesteps(self.num_inference_steps		,      device=A )
             _UpperCAmelCase  :						List[str]							   =					torch.manual_seed(0 )

             _UpperCAmelCase  :						Optional[Any]							   =					self.dummy_model()
             _UpperCAmelCase  :						int							   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
             _UpperCAmelCase  :						str							   =					sample.to(A )

             for t in scheduler.timesteps:
                    _UpperCAmelCase  :						Tuple							   =					scheduler.scale_model_input(A		,      A )

                    _UpperCAmelCase  :						Union[str, Any]							   =					model(A		,      A )

                    _UpperCAmelCase  :						List[str]							   =					scheduler.step(A		,      A		,      A		,      generator=A )
                    _UpperCAmelCase  :						List[str]							   =					output.prev_sample

             _UpperCAmelCase  :						str							   =					torch.sum(torch.abs(A ) )
             _UpperCAmelCase  :						Union[str, Any]							   =					torch.mean(torch.abs(A ) )

             assert abs(result_sum.item() - 152.3_192 ) < 1E-2
             assert abs(result_mean.item() - 0.1_983 ) < 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 			UpperCamelCase_							(      _UpperCAmelCase						:   List[str]					)					->					Union[str, Any]:


       """simple docstring"""







       return EnvironmentCommand()


class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      @staticmethod
      def   _A			( A		:       ArgumentParser ):
             _UpperCAmelCase  :						List[str]							   =					parser.add_parser("env"		,      help="Print relevant system environment info." )
             download_parser.set_defaults(func=A )




      def   _A			( self		:       List[str] ):
             _UpperCAmelCase  :						str							   =					{
                 "`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(A ) )

             return info





      @staticmethod
      def   _A			( A		:       Tuple ):
             return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n"

