



import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class 	__lowerCAmelCase       (		lowerCAmelCase__				):
 lowerCAmelCase__       =					(DPMSolverMultistepScheduler,)
 lowerCAmelCase__       =					(("""num_inference_steps""", 2_5),)




 def     lowerCamelCase (  self		,			**__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      {
        '''num_train_timesteps''': 1000,
        '''beta_start''': 0.0_001,
        '''beta_end''': 0.02,
        '''beta_schedule''': '''linear''',
        '''solver_order''': 2,
        '''prediction_type''': '''epsilon''',
        '''thresholding''': False,
        '''sample_max_value''': 1.0,
        '''algorithm_type''': '''dpmsolver++''',
        '''solver_type''': '''midpoint''',
        '''lower_order_final''': False,
        '''lambda_min_clipped''': -float('''inf'''			),
        '''variance_type''': None,
    }

    config.update(**__UpperCAmelCase			)
    return config




 def     lowerCamelCase (  self		,			__UpperCAmelCase=0		,			**__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      dict(self.forward_default_kwargs			)
    __lowerCamelCase  							=      kwargs.pop('''num_inference_steps'''		,			__UpperCAmelCase			)
    __lowerCamelCase  							=      self.dummy_sample
    __lowerCamelCase  							=      0.1 * sample
    __lowerCamelCase  							=      [residual + 0.2, residual + 0.15, residual + 0.10]

    for scheduler_class in self.scheduler_classes:
       __lowerCamelCase  							=      self.get_scheduler_config(**__UpperCAmelCase			)
       __lowerCamelCase  							=      scheduler_class(**__UpperCAmelCase			)
       scheduler.set_timesteps(__UpperCAmelCase			)
       # copy over dummy past residuals
       __lowerCamelCase  							=      dummy_past_residuals[: scheduler.config.solver_order]

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

       __lowerCamelCase							,__lowerCamelCase  							=      sample, sample
       for t in range(__UpperCAmelCase		,			time_step + scheduler.config.solver_order + 1			):
          __lowerCamelCase  							=      scheduler.step(__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			**__UpperCAmelCase			).prev_sample
          __lowerCamelCase  							=      new_scheduler.step(__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			**__UpperCAmelCase			).prev_sample

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




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    pass




 def     lowerCamelCase (  self		,			__UpperCAmelCase=0		,			**__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      dict(self.forward_default_kwargs			)
    __lowerCamelCase  							=      kwargs.pop('''num_inference_steps'''		,			__UpperCAmelCase			)
    __lowerCamelCase  							=      self.dummy_sample
    __lowerCamelCase  							=      0.1 * sample
    __lowerCamelCase  							=      [residual + 0.2, residual + 0.15, residual + 0.10]

    for scheduler_class in self.scheduler_classes:
       __lowerCamelCase  							=      self.get_scheduler_config()
       __lowerCamelCase  							=      scheduler_class(**__UpperCAmelCase			)
       scheduler.set_timesteps(__UpperCAmelCase			)

       # copy over dummy past residuals (must be after setting timesteps)
       __lowerCamelCase  							=      dummy_past_residuals[: scheduler.config.solver_order]

       with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(__UpperCAmelCase			)
          __lowerCamelCase  							=      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)
          __lowerCamelCase  							=      dummy_past_residuals[: new_scheduler.config.solver_order]

       __lowerCamelCase  							=      scheduler.step(__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			**__UpperCAmelCase			).prev_sample
       __lowerCamelCase  							=      new_scheduler.step(__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			**__UpperCAmelCase			).prev_sample

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




 def     lowerCamelCase (  self		,			__UpperCAmelCase=None		,			**__UpperCAmelCase			):



    '''simple docstring'''
    if scheduler is None:
       __lowerCamelCase  							=      self.scheduler_classes[0]
       __lowerCamelCase  							=      self.get_scheduler_config(**__UpperCAmelCase			)
       __lowerCamelCase  							=      scheduler_class(**__UpperCAmelCase			)

    __lowerCamelCase  							=      10
    __lowerCamelCase  							=      self.dummy_model()
    __lowerCamelCase  							=      self.dummy_sample_deter
    scheduler.set_timesteps(__UpperCAmelCase			)

    for i, t in enumerate(scheduler.timesteps			):
       __lowerCamelCase  							=      model(__UpperCAmelCase		,			__UpperCAmelCase			)
       __lowerCamelCase  							=      scheduler.step(__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase			).prev_sample

    return sample




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      dict(self.forward_default_kwargs			)

    __lowerCamelCase  							=      kwargs.pop('''num_inference_steps'''		,			__UpperCAmelCase			)

    for scheduler_class in self.scheduler_classes:
       __lowerCamelCase  							=      self.get_scheduler_config()
       __lowerCamelCase  							=      scheduler_class(**__UpperCAmelCase			)

       __lowerCamelCase  							=      self.dummy_sample
       __lowerCamelCase  							=      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'''			):
          __lowerCamelCase  							=      num_inference_steps

       # copy over dummy past residuals (must be done after set_timesteps)
       __lowerCamelCase  							=      [residual + 0.2, residual + 0.15, residual + 0.10]
       __lowerCamelCase  							=      dummy_past_residuals[: scheduler.config.solver_order]

       __lowerCamelCase  							=      scheduler.timesteps[5]
       __lowerCamelCase  							=      scheduler.timesteps[6]

       __lowerCamelCase  							=      scheduler.step(__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			**__UpperCAmelCase			).prev_sample
       __lowerCamelCase  							=      scheduler.step(__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			**__UpperCAmelCase			).prev_sample

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




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    for timesteps in [25, 50, 100, 999, 1000]:
       self.check_over_configs(num_train_timesteps=__UpperCAmelCase			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    self.check_over_configs(thresholding=__UpperCAmelCase			)
    for order in [1, 2, 3]:
       for solver_type in ["midpoint", "heun"]:
          for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample"]:
                self.check_over_configs(
                    thresholding=__UpperCAmelCase		,			prediction_type=__UpperCAmelCase		,			sample_max_value=__UpperCAmelCase		,			algorithm_type='''dpmsolver++'''		,			solver_order=__UpperCAmelCase		,			solver_type=__UpperCAmelCase		,			)




 def     lowerCamelCase (  self			):



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




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
       for solver_type in ["midpoint", "heun"]:
          for order in [1, 2, 3]:
             for prediction_type in ["epsilon", "sample"]:
                if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                   if order == 3:
                      continue
                else:
                   self.check_over_configs(
                       solver_order=__UpperCAmelCase		,			solver_type=__UpperCAmelCase		,			prediction_type=__UpperCAmelCase		,			algorithm_type=__UpperCAmelCase		,			)
                __lowerCamelCase  							=      self.full_loop(
                    solver_order=__UpperCAmelCase		,			solver_type=__UpperCAmelCase		,			prediction_type=__UpperCAmelCase		,			algorithm_type=__UpperCAmelCase		,			)
                assert not torch.isnan(__UpperCAmelCase			).any(), "Samples have nan numbers"




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    self.check_over_configs(lower_order_final=__UpperCAmelCase			)
    self.check_over_configs(lower_order_final=__UpperCAmelCase			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    self.check_over_configs(lambda_min_clipped=-float('''inf'''			)			)
    self.check_over_configs(lambda_min_clipped=-5.1			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    self.check_over_configs(variance_type=__UpperCAmelCase			)
    self.check_over_configs(variance_type='''learned_range'''			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
       self.check_over_forward(num_inference_steps=__UpperCAmelCase		,			time_step=0			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.full_loop()
    __lowerCamelCase  							=      torch.mean(torch.abs(__UpperCAmelCase			)			)

    assert abs(result_mean.item() - 0.3_301			) < 1E-3




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.full_loop(thresholding=__UpperCAmelCase		,			dynamic_thresholding_ratio=0.87		,			sample_max_value=0.5			)
    __lowerCamelCase  							=      torch.mean(torch.abs(__UpperCAmelCase			)			)

    assert abs(result_mean.item() - 1.1_364			) < 1E-3




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.full_loop(prediction_type='''v_prediction'''			)
    __lowerCamelCase  							=      torch.mean(torch.abs(__UpperCAmelCase			)			)

    assert abs(result_mean.item() - 0.2_251			) < 1E-3




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.full_loop(prediction_type='''v_prediction'''		,			use_karras_sigmas=__UpperCAmelCase			)
    __lowerCamelCase  							=      torch.mean(torch.abs(__UpperCAmelCase			)			)

    assert abs(result_mean.item() - 0.2_096			) < 1E-3




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    # make sure that iterating over schedulers with same config names gives same results
    # for defaults
    __lowerCamelCase  							=      DPMSolverMultistepScheduler(**self.get_scheduler_config()			)
    __lowerCamelCase  							=      self.full_loop(scheduler=__UpperCAmelCase			)
    __lowerCamelCase  							=      torch.mean(torch.abs(__UpperCAmelCase			)			)

    assert abs(result_mean.item() - 0.3_301			) < 1E-3

    __lowerCamelCase  							=      DPMSolverSinglestepScheduler.from_config(scheduler.config			)
    __lowerCamelCase  							=      UniPCMultistepScheduler.from_config(scheduler.config			)
    __lowerCamelCase  							=      DEISMultistepScheduler.from_config(scheduler.config			)
    __lowerCamelCase  							=      DPMSolverMultistepScheduler.from_config(scheduler.config			)

    __lowerCamelCase  							=      self.full_loop(scheduler=__UpperCAmelCase			)
    __lowerCamelCase  							=      torch.mean(torch.abs(__UpperCAmelCase			)			)

    assert abs(result_mean.item() - 0.3_301			) < 1E-3




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.scheduler_classes[0]
    __lowerCamelCase  							=      self.get_scheduler_config(thresholding=__UpperCAmelCase		,			dynamic_thresholding_ratio=0			)
    __lowerCamelCase  							=      scheduler_class(**__UpperCAmelCase			)

    __lowerCamelCase  							=      10
    __lowerCamelCase  							=      self.dummy_model()
    __lowerCamelCase  							=      self.dummy_sample_deter.half()
    scheduler.set_timesteps(__UpperCAmelCase			)

    for i, t in enumerate(scheduler.timesteps			):
       __lowerCamelCase  							=      model(__UpperCAmelCase		,			__UpperCAmelCase			)
       __lowerCamelCase  							=      scheduler.step(__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase			).prev_sample

    assert sample.dtype == torch.floataa




 def     lowerCamelCase (  self		,			**__UpperCAmelCase			):



    '''simple docstring'''
    for scheduler_class in self.scheduler_classes:
       __lowerCamelCase  							=      self.get_scheduler_config(**__UpperCAmelCase			)
       __lowerCamelCase  							=      scheduler_class(**__UpperCAmelCase			)

       scheduler.set_timesteps(scheduler.config.num_train_timesteps			)
       assert len(scheduler.timesteps.unique()			) == scheduler.num_inference_steps











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 						a__     (						_UpperCamelCase     :	Optional[Any]							):
   return EnvironmentCommand()





class 	__lowerCAmelCase       (		lowerCAmelCase__				):




 @staticmethod
 def     lowerCamelCase (  __UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      parser.add_parser('''env'''		,			help='''Print relevant system environment info.'''			)
    download_parser.set_defaults(func=__UpperCAmelCase			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      {
        '''`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(__UpperCAmelCase			)			)

    return info




 @staticmethod
 def     lowerCamelCase (  __UpperCAmelCase			):



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






