





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					(UnCLIPScheduler,)






  def 							lowercase						(							self					:						str   ,    **lowerCAmelCase_					:						str	)				->		str:
     __lowerCAmelCase				      =      {
         'num_train_timesteps': 1_0_0_0,
         'variance_type': 'fixed_small_log',
         'clip_sample': True,
         'clip_sample_range': 1.0,
         'prediction_type': 'epsilon',
     }

     config.update(**lowerCAmelCase_	)
     return config






  def 							lowercase						(							self					:						Optional[Any]	)				->		Optional[int]:
     for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase_	)






  def 							lowercase						(							self					:						Union[str, Any]	)				->		List[Any]:
     for variance in ["fixed_small_log", "learned_range"]:
        self.check_over_configs(variance_type=lowerCAmelCase_	)






  def 							lowercase						(							self					:						int	)				->		Union[str, Any]:
     for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=lowerCAmelCase_	)






  def 							lowercase						(							self					:						Tuple	)				->		List[Any]:
     for clip_sample_range in [1, 5, 1_0, 2_0]:
        self.check_over_configs(clip_sample_range=lowerCAmelCase_	)






  def 							lowercase						(							self					:						Dict	)				->		Any:
     for prediction_type in ["epsilon", "sample"]:
        self.check_over_configs(prediction_type=lowerCAmelCase_	)






  def 							lowercase						(							self					:						int	)				->		Tuple:
     for time_step in [0, 5_0_0, 9_9_9]:
        for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
           if prev_timestep is not None and prev_timestep >= time_step:
              continue

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






  def 							lowercase						(							self					:						Any	)				->		Dict:
     __lowerCAmelCase				      =      self.scheduler_classes[0]
     __lowerCAmelCase				      =      self.get_scheduler_config(variance_type='fixed_small_log'	)
     __lowerCAmelCase				      =      scheduler_class(**lowerCAmelCase_	)

     assert torch.sum(torch.abs(scheduler._get_variance(0	) - 1.00_00e-10	)	) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_8_7	) - 0.0_54_96_25	)	) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_9_9	) - 0.9_99_49_87	)	) < 1e-5






  def 							lowercase						(							self					:						List[Any]	)				->		int:
     __lowerCAmelCase				      =      self.scheduler_classes[0]
     __lowerCAmelCase				      =      self.get_scheduler_config(variance_type='learned_range'	)
     __lowerCAmelCase				      =      scheduler_class(**lowerCAmelCase_	)

     __lowerCAmelCase				      =      0.5

     assert scheduler._get_variance(1   ,    predicted_variance=lowerCAmelCase_	) - -10.1_71_27_90 < 1e-5
     assert scheduler._get_variance(4_8_7   ,    predicted_variance=lowerCAmelCase_	) - -5.7_99_80_52 < 1e-5
     assert scheduler._get_variance(9_9_9   ,    predicted_variance=lowerCAmelCase_	) - -0.0_01_00_11 < 1e-5






  def 							lowercase						(							self					:						Dict	)				->		Optional[int]:
     __lowerCAmelCase				      =      self.scheduler_classes[0]
     __lowerCAmelCase				      =      self.get_scheduler_config()
     __lowerCAmelCase				      =      scheduler_class(**lowerCAmelCase_	)

     __lowerCAmelCase				      =      scheduler.timesteps

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

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

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

        __lowerCAmelCase				      =      pred_prev_sample

     __lowerCAmelCase				      =      torch.sum(torch.abs(lowerCAmelCase_	)	)
     __lowerCAmelCase				      =      torch.mean(torch.abs(lowerCAmelCase_	)	)

     assert abs(result_sum.item() - 2_52.2_68_24_95	) < 1e-2
     assert abs(result_mean.item() - 0.3_28_47_43	) < 1e-3






  def 							lowercase						(							self					:						List[str]	)				->		Optional[int]:
     __lowerCAmelCase				      =      self.scheduler_classes[0]
     __lowerCAmelCase				      =      self.get_scheduler_config()
     __lowerCAmelCase				      =      scheduler_class(**lowerCAmelCase_	)

     scheduler.set_timesteps(2_5	)

     __lowerCAmelCase				      =      scheduler.timesteps

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

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

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

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

        __lowerCAmelCase				      =      pred_prev_sample

     __lowerCAmelCase				      =      torch.sum(torch.abs(lowerCAmelCase_	)	)
     __lowerCAmelCase				      =      torch.mean(torch.abs(lowerCAmelCase_	)	)

     assert abs(result_sum.item() - 2_58.2_04_49_83	) < 1e-2
     assert abs(result_mean.item() - 0.3_36_20_38	) < 1e-3






  def 							lowercase						(							self					:						Union[str, Any]	)				->		Optional[Any]:
     pass






  def 							lowercase						(							self					:						Tuple	)				->		Tuple:
     pass









import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


A__      :     Tuple       =					{
    '<': operator.lt,
    '<=': operator.le,
    '==': operator.eq,
    '!=': operator.ne,
    '>=': operator.ge,
    '>': operator.gt,
}






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				if got_ver is None or want_ver is None:
								raise ValueError(
								    F"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
								    F""" reinstalling {pkg}."""							)
				if not ops[op](version.parse(lowerCamelCase_							)						,  version.parse(lowerCamelCase_							)							):
								raise ImportError(
								    F"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"""							)






def     a     (		lowerCamelCase_						,  lowerCamelCase_ = None							):






				'''simple docstring'''
				lowercase__  					=     F"""\n{hint}""" if hint is not None else ''''''

				# non-versioned check
				if re.match(r'''^[\w_\-\d]+$'''						,  lowerCamelCase_							):
								lowercase__					,		lowercase__					,		lowercase__  					=     requirement, None, None
				else:
								lowercase__  					=     re.findall(r'''^([^!=<>\s]+)([\s!=<>]{1,2}.+)'''						,  lowerCamelCase_							)
								if not match:
												raise ValueError(
												    '''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'''
												    F""" got {requirement}"""							)
								lowercase__					,		lowercase__  					=     match[0]
								lowercase__  					=     want_full.split(''','''							)  # there could be multiple requirements
								lowercase__  					=     {}
								for w in want_range:
												lowercase__  					=     re.findall(r'''^([\s!=<>]{1,2})(.+)'''						,  lowerCamelCase_							)
												if not match:
																raise ValueError(
																    '''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'''
																    F""" but got {requirement}"""							)
												lowercase__					,		lowercase__  					=     match[0]
												lowercase__  					=     want_ver
												if op not in ops:
																raise ValueError(F"""{requirement}: need one of {list(ops.keys()							)}, but got {op}"""							)

    # special case
				if pkg == "python":
								lowercase__  					=     '''.'''.join([str(lowerCamelCase_							) for x in sys.version_info[:3]]							)
								for op, want_ver in wanted.items():
												_compare_versions(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							)
								return

				# check if any version is installed
				try:
								lowercase__  					=     importlib.metadata.version(lowerCamelCase_							)
				except importlib.metadata.PackageNotFoundError:
								raise importlib.metadata.PackageNotFoundError(
								    F"""The '{requirement}' distribution was not found and is required by this application. {hint}"""							)

				# check that the right version is installed if version number or a range was provided
				if want_ver is not None:
								for op, want_ver in wanted.items():
												_compare_versions(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							)






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     '''Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'''
				return require_version(lowerCamelCase_						,  lowerCamelCase_							)




