





import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


a__      :     Any         =		get_logger(__name__)





class UpperCAmelCase_				( enum.Enum ):
  __lowerCAmelCase   :						List[Any]							=   """all_checks"""
  __lowerCAmelCase   :						str							=   """basic_checks"""
  __lowerCAmelCase   :						str							=   """no_checks"""





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  pass





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  pass





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  pass





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  pass




def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[dict]  ,						_UpperCAmelCase							:dict  ,						_UpperCAmelCase							:str=None				)	->				int:
     '''simple docstring'''


     if expected_checksums is None:
          logger.info('''Unable to verify checksums.'''				)
          return
     if len(set(_UpperCAmelCase				) - set(_UpperCAmelCase				)				) > 0:
          raise ExpectedMoreDownloadedFiles(str(set(_UpperCAmelCase				) - set(_UpperCAmelCase				)				)				)
     if len(set(_UpperCAmelCase				) - set(_UpperCAmelCase				)				) > 0:
          raise UnexpectedDownloadedFile(str(set(_UpperCAmelCase				) - set(_UpperCAmelCase				)				)				)
     A_          =       [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
     A_          =       ''' for ''' + verification_name if verification_name is not None else ''''''
     if len(_UpperCAmelCase				) > 0:
          raise NonMatchingChecksumError(
              f'Checksums didn\'t match{for_verification_name}:\n'
              f'{bad_urls}\n'
              '''Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'''				)
     logger.info('''All the checksums matched successfully''' + for_verification_name				)





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  pass





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  pass





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  pass





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  pass




def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[dict]  ,						_UpperCAmelCase							:dict				)	->				str:
     '''simple docstring'''


     if expected_splits is None:
          logger.info('''Unable to verify splits sizes.'''				)
          return
     if len(set(_UpperCAmelCase				) - set(_UpperCAmelCase				)				) > 0:
          raise ExpectedMoreSplits(str(set(_UpperCAmelCase				) - set(_UpperCAmelCase				)				)				)
     if len(set(_UpperCAmelCase				) - set(_UpperCAmelCase				)				) > 0:
          raise UnexpectedSplits(str(set(_UpperCAmelCase				) - set(_UpperCAmelCase				)				)				)
     A_          =       [
         {'''expected''': expected_splits[name], '''recorded''': recorded_splits[name]}
         for name in expected_splits
         if expected_splits[name].num_examples != recorded_splits[name].num_examples
     ]
     if len(_UpperCAmelCase				) > 0:
          raise NonMatchingSplitsSizesError(str(_UpperCAmelCase				)				)
     logger.info('''All the splits matched successfully.'''				)

def      UpperCAmelCase_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:bool = True				)	->				dict:
     '''simple docstring'''


     if record_checksum:
          A_          =       shaaaa()
          with open(_UpperCAmelCase  ,						'''rb'''				) as f:
               for chunk in iter(lambda: f.read(1 << 20				)  ,						b''''''				):
                    m.update(_UpperCAmelCase				)
               A_          =       m.hexdigest()
     else:
          A_          =       None
     return {"num_bytes": os.path.getsize(_UpperCAmelCase				), "checksum": checksum}


def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[int]				)	->				Any:
     '''simple docstring'''


     if dataset_size and config.IN_MEMORY_MAX_SIZE:
          return dataset_size < config.IN_MEMORY_MAX_SIZE
     else:
          return False










import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						int							=   (KDPMaDiscreteScheduler,)
  __lowerCAmelCase   :						Union[str, Any]							=   10


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





       A_          =       {
           '''num_train_timesteps''': 1_1_0_0,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**__snake_case )
       return config


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """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=__snake_case ,beta_end=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.scheduler_classes[0]
       A_          =       self.get_scheduler_config(prediction_type='''v_prediction''' )
       A_          =       scheduler_class(**__snake_case )

       scheduler.set_timesteps(self.num_inference_steps )

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter * scheduler.init_noise_sigma
       A_          =       sample.to(__snake_case )

       for i, t in enumerate(scheduler.timesteps ):
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       torch.mean(torch.abs(__snake_case ) )

       if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 4.69_34E-07 ) < 1E-2
            assert abs(result_mean.item() - 6.11_12E-10 ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07 ) < 1E-2
            assert abs(result_mean.item() - 0.0002 ) < 1E-3


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





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

       scheduler.set_timesteps(self.num_inference_steps )

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter * scheduler.init_noise_sigma
       A_          =       sample.to(__snake_case )

       for i, t in enumerate(scheduler.timesteps ):
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       torch.mean(torch.abs(__snake_case ) )

       if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





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

       scheduler.set_timesteps(self.num_inference_steps ,device=__snake_case )

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter.to(__snake_case ) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       torch.mean(torch.abs(__snake_case ) )

       if str(__snake_case ).startswith('''cpu''' ):
            # The following sum varies between 148 and 156 on mps. Why?
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3



