




"""simple docstring"""

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Optional[int]				  =     (UnCLIPScheduler,)

 def       lowerCAmelCase_							(			self	:	Any   ,							**_UpperCAmelCase	:	Union[str, Any]		):
       _A        =       {
           'num_train_timesteps': 1_000,
           'variance_type': 'fixed_small_log',
           'clip_sample': True,
           'clip_sample_range': 1.0,
           'prediction_type': 'epsilon',
       }

       config.update(**_UpperCAmelCase		)
       return config

 def       lowerCAmelCase_							(			self	:	int		):
       for timesteps in [1, 5, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	int		):
       for variance in ["fixed_small_log", "learned_range"]:
             self.check_over_configs(variance_type=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	str		):
       for clip_sample_range in [1, 5, 10, 20]:
             self.check_over_configs(clip_sample_range=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       for prediction_type in ["epsilon", "sample"]:
             self.check_over_configs(prediction_type=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	str		):
       for time_step in [0, 500, 999]:
             for prev_timestep in [None, 5, 100, 250, 500, 750]:
                   if prev_timestep is not None and prev_timestep >= time_step:
                         continue

                   self.check_over_forward(time_step=_UpperCAmelCase   ,							prev_timestep=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A        =       self.scheduler_classes[0]
       _A        =       self.get_scheduler_config(variance_type='fixed_small_log'		)
       _A        =       scheduler_class(**_UpperCAmelCase		)

       assert torch.sum(torch.abs(scheduler._get_variance(0		) - 1.0_0_0_0E-1_0		)		) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.054_9625		)		) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.999_4987		)		) < 1E-5

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       _A        =       self.scheduler_classes[0]
       _A        =       self.get_scheduler_config(variance_type='learned_range'		)
       _A        =       scheduler_class(**_UpperCAmelCase		)

       _A        =       0.5

       assert scheduler._get_variance(1   ,							predicted_variance=_UpperCAmelCase		) - -10.171_2790 < 1E-5
       assert scheduler._get_variance(487   ,							predicted_variance=_UpperCAmelCase		) - -5.799_8052 < 1E-5
       assert scheduler._get_variance(999   ,							predicted_variance=_UpperCAmelCase		) - -0.001_0011 < 1E-5

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

       _A        =       scheduler.timesteps

       _A        =       self.dummy_model()
       _A        =       self.dummy_sample_deter
       _A        =       torch.manual_seed(0		)

       for i, t in enumerate(_UpperCAmelCase		):
             # 1. predict noise residual
             _A        =       model(_UpperCAmelCase   ,							_UpperCAmelCase		)

             # 2. predict previous mean of sample x_t-1
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							generator=_UpperCAmelCase		).prev_sample

             _A        =       pred_prev_sample

       _A        =       torch.sum(torch.abs(_UpperCAmelCase		)		)
       _A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

       assert abs(result_sum.item() - 252.268_2495		) < 1E-2
       assert abs(result_mean.item() - 0.328_4743		) < 1E-3

 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       self.scheduler_classes[0]
       _A        =       self.get_scheduler_config()
       _A        =       scheduler_class(**_UpperCAmelCase		)

       scheduler.set_timesteps(25		)

       _A        =       scheduler.timesteps

       _A        =       self.dummy_model()
       _A        =       self.dummy_sample_deter
       _A        =       torch.manual_seed(0		)

       for i, t in enumerate(_UpperCAmelCase		):
             # 1. predict noise residual
             _A        =       model(_UpperCAmelCase   ,							_UpperCAmelCase		)

             if i + 1 == timesteps.shape[0]:
                   _A        =       None
             else:
                   _A        =       timesteps[i + 1]

             # 2. predict previous mean of sample x_t-1
             _A        =       scheduler.step(
                 _UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							prev_timestep=_UpperCAmelCase   ,							generator=_UpperCAmelCase		).prev_sample

             _A        =       pred_prev_sample

       _A        =       torch.sum(torch.abs(_UpperCAmelCase		)		)
       _A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

       assert abs(result_sum.item() - 258.204_4983		) < 1E-2
       assert abs(result_mean.item() - 0.336_2038		) < 1E-3

 def       lowerCAmelCase_							(			self	:	int		):
       pass



 def       lowerCAmelCase_							(			self	:	List[str]		):
       pass







"""simple docstring"""

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

from .. import config
from .logging import get_logger


a         =      get_logger(__name__)





class     lowercase_	(					enum.Enum							):



 '''simple docstring'''
 UpperCAmelCase   :					Optional[int]				  =     '''all_checks'''
 UpperCAmelCase   :					List[Any]				  =     '''basic_checks'''
 UpperCAmelCase   :					Any				  =     '''no_checks'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''






def 						_snake_case					( _snake_case							: Optional[dict]    ,  _snake_case							: dict    ,  _snake_case							: Dict=None							)   -> Dict:






      '''simple docstring'''


      if expected_checksums is None:
            logger.info('Unable to verify checksums.'							)
            return
      if len(set(_snake_case							) - set(_snake_case							)							) > 0:
            raise ExpectedMoreDownloadedFiles(str(set(_snake_case							) - set(_snake_case							)							)							)
      if len(set(_snake_case							) - set(_snake_case							)							) > 0:
            raise UnexpectedDownloadedFile(str(set(_snake_case							) - set(_snake_case							)							)							)
      _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(_snake_case							) > 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     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''






def 						_snake_case					( _snake_case							: Optional[dict]    ,  _snake_case							: dict							)   -> List[str]:






      '''simple docstring'''


      if expected_splits is None:
            logger.info('Unable to verify splits sizes.'							)
            return
      if len(set(_snake_case							) - set(_snake_case							)							) > 0:
            raise ExpectedMoreSplits(str(set(_snake_case							) - set(_snake_case							)							)							)
      if len(set(_snake_case							) - set(_snake_case							)							) > 0:
            raise UnexpectedSplits(str(set(_snake_case							) - set(_snake_case							)							)							)
      _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(_snake_case							) > 0:
            raise NonMatchingSplitsSizesError(str(_snake_case							)							)
      logger.info('All the splits matched successfully.'							)






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: bool = True							)   -> dict:






      '''simple docstring'''


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







def 						_snake_case					( _snake_case							: int							)   -> int:






      '''simple docstring'''


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

