






'''simple docstring'''

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class     A    (      a__						):






 """simple docstring"""





 __a			:						Optional[int]      =						(DDIMParallelScheduler,)
 __a			:						int      =						(("""eta""", 0.0), ("""num_inference_steps""", 50))
 def   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       UpperCamelCase_				:   str					    =							{
           '''num_train_timesteps''': 10_00,
           '''beta_start''': 0.00_01,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''clip_sample''': True,
       }

       config.update(**lowercase__ )
       return config
 def   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       UpperCamelCase_				:   List[Any]					    =							self.scheduler_classes[0]
       UpperCamelCase_				:   List[Any]					    =							self.get_scheduler_config(**lowercase__ )
       UpperCamelCase_				:   str					    =							scheduler_class(**lowercase__ )

       UpperCamelCase_				:   Union[str, Any]					    =							10, 0.0

       UpperCamelCase_				:   List[str]					    =							self.dummy_model()
       UpperCamelCase_				:   Tuple					    =							self.dummy_sample_deter

       scheduler.set_timesteps(lowercase__ )

       for t in scheduler.timesteps:
             UpperCamelCase_				:   Union[str, Any]					    =							model(lowercase__			,     lowercase__ )
             UpperCamelCase_				:   Any					    =							scheduler.step(lowercase__			,     lowercase__			,     lowercase__			,     lowercase__ ).prev_sample

       return sample
 def   _UpperCAmelCase       (							self ):





       for timesteps in [1_00, 5_00, 10_00]:
             self.check_over_configs(num_train_timesteps=lowercase__ )
 def   _UpperCAmelCase       (							self ):





       for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=lowercase__ )

       UpperCamelCase_				:   int					    =							self.scheduler_classes[0]
       UpperCamelCase_				:   int					    =							self.get_scheduler_config(steps_offset=1 )
       UpperCamelCase_				:   Optional[int]					    =							scheduler_class(**lowercase__ )
       scheduler.set_timesteps(5 )
       assert torch.equal(scheduler.timesteps			,     torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )
 def   _UpperCAmelCase       (							self ):





       for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			,     [0.0_02, 0.02, 0.2, 2] ):
             self.check_over_configs(beta_start=lowercase__			,     beta_end=lowercase__ )
 def   _UpperCAmelCase       (							self ):





       for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=lowercase__ )
 def   _UpperCAmelCase       (							self ):





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





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





       for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=lowercase__ )
 def   _UpperCAmelCase       (							self ):





       for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=lowercase__ )
 def   _UpperCAmelCase       (							self ):
       self.check_over_configs(thresholding=lowercase__ )
       for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=lowercase__			,     prediction_type=lowercase__			,     sample_max_value=lowercase__			,     )
 def   _UpperCAmelCase       (							self ):





       for t in [1, 10, 49]:
             self.check_over_forward(time_step=lowercase__ )
 def   _UpperCAmelCase       (							self ):





       for t, num_inference_steps in zip([1, 10, 50]			,     [10, 50, 5_00] ):
             self.check_over_forward(time_step=lowercase__			,     num_inference_steps=lowercase__ )
 def   _UpperCAmelCase       (							self ):





       for t, eta in zip([1, 10, 49]			,     [0.0, 0.5, 1.0] ):
             self.check_over_forward(time_step=lowercase__			,     eta=lowercase__ )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							self.scheduler_classes[0]
       UpperCamelCase_				:   List[Any]					    =							self.get_scheduler_config()
       UpperCamelCase_				:   Union[str, Any]					    =							scheduler_class(**lowercase__ )

       assert torch.sum(torch.abs(scheduler._get_variance(0			,     0 ) - 0.0 ) ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_20			,     4_00 ) - 0.1_47_71 ) ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_80			,     9_60 ) - 0.3_24_60 ) ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(0			,     0 ) - 0.0 ) ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_87			,     4_86 ) - 0.0_09_79 ) ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_99			,     9_98 ) - 0.02 ) ) < 1E-5
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Any					    =							self.scheduler_classes[0]
       UpperCamelCase_				:   Optional[int]					    =							self.get_scheduler_config()
       UpperCamelCase_				:   Optional[int]					    =							scheduler_class(**lowercase__ )

       UpperCamelCase_				:   Tuple					    =							10, 0.0
       scheduler.set_timesteps(lowercase__ )

       UpperCamelCase_				:   Tuple					    =							self.dummy_model()
       UpperCamelCase_				:   Any					    =							self.dummy_sample_deter
       UpperCamelCase_				:   Tuple					    =							self.dummy_sample_deter + 0.1
       UpperCamelCase_				:   Optional[Any]					    =							self.dummy_sample_deter - 0.1

       UpperCamelCase_				:   Optional[int]					    =							samplea.shape[0]
       UpperCamelCase_				:   List[str]					    =							torch.stack([samplea, samplea, samplea]			,     dim=0 )
       UpperCamelCase_				:   Optional[Any]					    =							torch.arange(lowercase__ )[0:3, None].repeat(1			,     lowercase__ )

       UpperCamelCase_				:   List[str]					    =							model(samples.flatten(0			,     1 )			,     timesteps.flatten(0			,     1 ) )
       UpperCamelCase_				:   Dict					    =							scheduler.batch_step_no_noise(lowercase__			,     timesteps.flatten(0			,     1 )			,     samples.flatten(0			,     1 )			,     lowercase__ )

       UpperCamelCase_				:   List[Any]					    =							torch.sum(torch.abs(lowercase__ ) )
       UpperCamelCase_				:   Optional[int]					    =							torch.mean(torch.abs(lowercase__ ) )

       assert abs(result_sum.item() - 11_47.79_04 ) < 1E-2
       assert abs(result_mean.item() - 0.49_82 ) < 1E-3
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							self.full_loop()

       UpperCamelCase_				:   int					    =							torch.sum(torch.abs(lowercase__ ) )
       UpperCamelCase_				:   int					    =							torch.mean(torch.abs(lowercase__ ) )

       assert abs(result_sum.item() - 1_72.00_67 ) < 1E-2
       assert abs(result_mean.item() - 0.22_39_67 ) < 1E-3
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Tuple					    =							self.full_loop(prediction_type="""v_prediction""" )

       UpperCamelCase_				:   int					    =							torch.sum(torch.abs(lowercase__ ) )
       UpperCamelCase_				:   Optional[Any]					    =							torch.mean(torch.abs(lowercase__ ) )

       assert abs(result_sum.item() - 52.53_02 ) < 1E-2
       assert abs(result_mean.item() - 0.06_84 ) < 1E-3
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Tuple					    =							self.full_loop(set_alpha_to_one=lowercase__			,     beta_start=0.01 )
       UpperCamelCase_				:   str					    =							torch.sum(torch.abs(lowercase__ ) )
       UpperCamelCase_				:   Optional[Any]					    =							torch.mean(torch.abs(lowercase__ ) )

       assert abs(result_sum.item() - 1_49.82_95 ) < 1E-2
       assert abs(result_mean.item() - 0.19_51 ) < 1E-3





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[Any]					    =							self.full_loop(set_alpha_to_one=lowercase__			,     beta_start=0.01 )
       UpperCamelCase_				:   Dict					    =							torch.sum(torch.abs(lowercase__ ) )
       UpperCamelCase_				:   Optional[Any]					    =							torch.mean(torch.abs(lowercase__ ) )

       assert abs(result_sum.item() - 1_49.07_84 ) < 1E-2
       assert abs(result_mean.item() - 0.19_41 ) < 1E-3














'''simple docstring'''

# Lint as: python3
import itertools
import os
import re


UpperCamelCase							=re.compile(R"([A-Z]+)([A-Z][a-z])")
UpperCamelCase							=re.compile(R"([a-z\d])([A-Z])")

UpperCamelCase							=re.compile(R"(?<!_)_(?!_)")
UpperCamelCase							=re.compile(R"(_{2,})")

UpperCamelCase							=R"^\w+(\.\w+)*$"

UpperCamelCase							=R"<>:/\|?*"



def 							snake_case					(     a_							: str	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   str					    =							_uppercase_uppercase_re.sub(r"""\1_\2"""    ,   a_	)
      UpperCamelCase_				:   List[Any]					    =							_lowercase_uppercase_re.sub(r"""\1_\2"""    ,   a_	)
      return name.lower()



def 							snake_case					(     a_							: Any	)       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Optional[Any]					    =							_single_underscore_re.split(a_	)
      UpperCamelCase_				:   Tuple					    =							[_multiple_underscores_re.split(a_	) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(a_	) if n != """"""	)



def 							snake_case					(     a_							: Optional[int]	)       ->				str:





      """simple docstring"""

      if os.path.basename(a_	) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"	)
      return camelcase_to_snakecase(a_	)



def 							snake_case					(     a_							: List[Any]    ,   a_							: Optional[int]	)       ->				Optional[Any]:





      """simple docstring"""

      if os.path.basename(a_	) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"	)
      if not re.match(_split_re    ,   a_	):
            raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."	)
      return f"{filename_prefix_for_name(a_	)}-{split}"



def 							snake_case					(     a_							: Optional[Any]    ,   a_							: int    ,   a_							: List[Any]    ,   a_							: Union[str, Any]=None	)       ->				Dict:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							filename_prefix_for_split(a_    ,   a_	)
      if filetype_suffix:
            prefix += f".{filetype_suffix}"
      UpperCamelCase_				:   str					    =							os.path.join(a_    ,   a_	)
      return f"{filepath}*"



def 							snake_case					(     a_							: Tuple    ,   a_							: Any    ,   a_							: Optional[int]    ,   a_							: Union[str, Any]=None    ,   a_							: Optional[Any]=None	)       ->				List[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							filename_prefix_for_split(a_    ,   a_	)
      UpperCamelCase_				:   Dict					    =							os.path.join(a_    ,   a_	)

      if shard_lengths:
            UpperCamelCase_				:   Optional[int]					    =							len(a_	)
            UpperCamelCase_				:   Tuple					    =							[f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(a_	)]
            if filetype_suffix:
                  UpperCamelCase_				:   Tuple					    =							[filename + f".{filetype_suffix}" for filename in filenames]
            return filenames
      else:
            UpperCamelCase_				:   Dict					    =							prefix
            if filetype_suffix:
                  filename += f".{filetype_suffix}"
            return [filename]






