import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class   snake_case_       (		__A       ):
   __A	:		List[Any]         =				(UnCLIPScheduler,)






   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						**lowercase_				:     List[str]  )					->						List[str]:
       lowercase__ :   int    					=				{
           "num_train_timesteps": 10_00,
           "variance_type": "fixed_small_log",
           "clip_sample": True,
           "clip_sample_range": 1.0,
           "prediction_type": "epsilon",
       }

       config.update(**lowercase_  )
       return config






   def       __UpperCamelCase     (      self				:     Tuple  )					->						Tuple:
       for timesteps in [1, 5, 1_00, 10_00]:
           self.check_over_configs(num_train_timesteps=lowercase_  )






   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Union[str, Any]:
       for variance in ["fixed_small_log", "learned_range"]:
           self.check_over_configs(variance_type=lowercase_  )






   def       __UpperCamelCase     (      self				:     int  )					->						List[Any]:
       for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=lowercase_  )






   def       __UpperCamelCase     (      self				:     str  )					->						int:
       for clip_sample_range in [1, 5, 10, 20]:
           self.check_over_configs(clip_sample_range=lowercase_  )






   def       __UpperCamelCase     (      self				:     int  )					->						Optional[int]:
       for prediction_type in ["epsilon", "sample"]:
           self.check_over_configs(prediction_type=lowercase_  )






   def       __UpperCamelCase     (      self				:     str  )					->						Union[str, Any]:
       for time_step in [0, 5_00, 9_99]:
           for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
               if prev_timestep is not None and prev_timestep >= time_step:
                   continue

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






   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						int:
       lowercase__ :   Dict    					=				self.scheduler_classes[0]
       lowercase__ :   Optional[Any]    					=				self.get_scheduler_config(variance_type="fixed_small_log"  )
       lowercase__ :   Optional[int]    					=				scheduler_class(**lowercase_  )

       assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 1.00_00E-10  )  ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_87  ) - 0.0_54_96_25  )  ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_99  ) - 0.9_99_49_87  )  ) < 1E-5






   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Optional[Any]:
       lowercase__ :   Dict    					=				self.scheduler_classes[0]
       lowercase__ :   Optional[Any]    					=				self.get_scheduler_config(variance_type="learned_range"  )
       lowercase__ :   Any    					=				scheduler_class(**lowercase_  )

       lowercase__ :   Tuple    					=				0.5

       assert scheduler._get_variance(1	,						predicted_variance=lowercase_  ) - -10.1_71_27_90 < 1E-5
       assert scheduler._get_variance(4_87	,						predicted_variance=lowercase_  ) - -5.7_99_80_52 < 1E-5
       assert scheduler._get_variance(9_99	,						predicted_variance=lowercase_  ) - -0.0_01_00_11 < 1E-5






   def       __UpperCamelCase     (      self				:     str  )					->						List[Any]:
       lowercase__ :   Union[str, Any]    					=				self.scheduler_classes[0]
       lowercase__ :   List[Any]    					=				self.get_scheduler_config()
       lowercase__ :   Optional[Any]    					=				scheduler_class(**lowercase_  )

       lowercase__ :   Union[str, Any]    					=				scheduler.timesteps

       lowercase__ :   List[Any]    					=				self.dummy_model()
       lowercase__ :   str    					=				self.dummy_sample_deter
       lowercase__ :   int    					=				torch.manual_seed(0  )

       for i, t in enumerate(lowercase_  ):
           # 1. predict noise residual
           lowercase__ :   Optional[int]    					=				model(lowercase_	,						lowercase_  )

           # 2. predict previous mean of sample x_t-1
           lowercase__ :   int    					=				scheduler.step(lowercase_	,						lowercase_	,						lowercase_	,						generator=lowercase_  ).prev_sample

           lowercase__ :   List[str]    					=				pred_prev_sample

       lowercase__ :   List[str]    					=				torch.sum(torch.abs(lowercase_  )  )
       lowercase__ :   List[Any]    					=				torch.mean(torch.abs(lowercase_  )  )

       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       __UpperCamelCase     (      self				:     Optional[Any]  )					->						List[str]:
       lowercase__ :   Optional[Any]    					=				self.scheduler_classes[0]
       lowercase__ :   Any    					=				self.get_scheduler_config()
       lowercase__ :   List[Any]    					=				scheduler_class(**lowercase_  )

       scheduler.set_timesteps(25  )

       lowercase__ :   Union[str, Any]    					=				scheduler.timesteps

       lowercase__ :   Tuple    					=				self.dummy_model()
       lowercase__ :   int    					=				self.dummy_sample_deter
       lowercase__ :   Optional[Any]    					=				torch.manual_seed(0  )

       for i, t in enumerate(lowercase_  ):
           # 1. predict noise residual
           lowercase__ :   Tuple    					=				model(lowercase_	,						lowercase_  )

           if i + 1 == timesteps.shape[0]:
               lowercase__ :   int    					=				None
           else:
               lowercase__ :   Union[str, Any]    					=				timesteps[i + 1]

           # 2. predict previous mean of sample x_t-1
           lowercase__ :   str    					=				scheduler.step(
               lowercase_	,						lowercase_	,						lowercase_	,						prev_timestep=lowercase_	,						generator=lowercase_  ).prev_sample

           lowercase__ :   str    					=				pred_prev_sample

       lowercase__ :   Dict    					=				torch.sum(torch.abs(lowercase_  )  )
       lowercase__ :   List[Any]    					=				torch.mean(torch.abs(lowercase_  )  )

       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       __UpperCamelCase     (      self				:     Any  )					->						Tuple:
       pass







   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Optional[Any]:
       pass








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

from packaging import version


_snake_case		=  {
    "<": 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'''




							_lowerCAmelCase     :							List[str]    =    F"\n{hint}" if hint is not None else ""

							# non-versioned check
							if re.match(r"^[\w_\-\d]+$"				, _lowerCamelCase  ):
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							List[str]    =    requirement, None, None
							else:
														_lowerCAmelCase     :							Optional[int]    =    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}"  )
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Dict    =    match[0]
														_lowerCAmelCase     :							Any    =    want_full.split(","  )  # there could be multiple requirements
														_lowerCAmelCase     :							Optional[int]    =    {}
														for w in want_range:
																					_lowerCAmelCase     :							Any    =    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}"  )
																					_lowerCAmelCase   ,    _lowerCAmelCase     :							Tuple    =    match[0]
																					_lowerCAmelCase     :							Union[str, Any]    =    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":
														_lowerCAmelCase     :							Tuple    =    ".".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:
														_lowerCAmelCase     :							Any    =    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'''




							_lowerCAmelCase     :							List[str]    =    "Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"
							return require_version(_lowerCamelCase				, _lowerCamelCase  )
