



"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  int						=					(UnCLIPScheduler,)



      def 		snake_case_   (	self	:      int	,      **A	:      Tuple		):
           _UpperCAmelCase     :      Tuple														=       {
               "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(**A		)
           return config



      def 		snake_case_   (	self	:      Optional[Any]		):
           for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=A		)



      def 		snake_case_   (	self	:      Optional[int]		):
           for variance in ["fixed_small_log", "learned_range"]:
                self.check_over_configs(variance_type=A		)



      def 		snake_case_   (	self	:      Tuple		):
           for clip_sample in [True, False]:
                self.check_over_configs(clip_sample=A		)



      def 		snake_case_   (	self	:      Dict		):
           for clip_sample_range in [1, 5, 1_0, 2_0]:
                self.check_over_configs(clip_sample_range=A		)



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



      def 		snake_case_   (	self	:      str		):
           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=A	,      prev_timestep=A		)



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      List[Any]														=       self.scheduler_classes[0]
           _UpperCAmelCase     :      str														=       self.get_scheduler_config(variance_type="fixed_small_log"		)
           _UpperCAmelCase     :      List[str]														=       scheduler_class(**A		)

           assert torch.sum(torch.abs(scheduler._get_variance(0		) - 1.0000e-10		)		) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(4_8_7		) - 0.0_549_625		)		) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_9_9		) - 0.9_994_987		)		) < 1e-5



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Any														=       self.scheduler_classes[0]
           _UpperCAmelCase     :      str														=       self.get_scheduler_config(variance_type="learned_range"		)
           _UpperCAmelCase     :      int														=       scheduler_class(**A		)

           _UpperCAmelCase     :      Tuple														=       0.5

           assert scheduler._get_variance(1	,      predicted_variance=A		) - -10.1_712_790 < 1e-5
           assert scheduler._get_variance(4_8_7	,      predicted_variance=A		) - -5.7_998_052 < 1e-5
           assert scheduler._get_variance(9_9_9	,      predicted_variance=A		) - -0.0_010_011 < 1e-5



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      int														=       self.scheduler_classes[0]
           _UpperCAmelCase     :      Optional[Any]														=       self.get_scheduler_config()
           _UpperCAmelCase     :      List[Any]														=       scheduler_class(**A		)

           _UpperCAmelCase     :      str														=       scheduler.timesteps

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

           for i, t in enumerate(A		):
                # 1. predict noise residual
                _UpperCAmelCase     :      List[str]														=       model(A	,      A		)

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

                _UpperCAmelCase     :      Optional[Any]														=       pred_prev_sample

           _UpperCAmelCase     :      List[Any]														=       torch.sum(torch.abs(A		)		)
           _UpperCAmelCase     :      List[Any]														=       torch.mean(torch.abs(A		)		)

           assert abs(result_sum.item() - 252.2_682_495		) < 1e-2
           assert abs(result_mean.item() - 0.3_284_743		) < 1e-3



      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      Optional[Any]														=       self.scheduler_classes[0]
           _UpperCAmelCase     :      List[Any]														=       self.get_scheduler_config()
           _UpperCAmelCase     :      Optional[Any]														=       scheduler_class(**A		)

           scheduler.set_timesteps(2_5		)

           _UpperCAmelCase     :      Dict														=       scheduler.timesteps

           _UpperCAmelCase     :      int														=       self.dummy_model()
           _UpperCAmelCase     :      Dict														=       self.dummy_sample_deter
           _UpperCAmelCase     :      Any														=       torch.manual_seed(0		)

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

                if i + 1 == timesteps.shape[0]:
                     _UpperCAmelCase     :      List[Any]														=       None
                else:
                     _UpperCAmelCase     :      Optional[Any]														=       timesteps[i + 1]

                # 2. predict previous mean of sample x_t-1
                _UpperCAmelCase     :      List[Any]														=       scheduler.step(
                    A	,      A	,      A	,      prev_timestep=A	,      generator=A		).prev_sample

                _UpperCAmelCase     :      Optional[int]														=       pred_prev_sample

           _UpperCAmelCase     :      Union[str, Any]														=       torch.sum(torch.abs(A		)		)
           _UpperCAmelCase     :      List[str]														=       torch.mean(torch.abs(A		)		)

           assert abs(result_sum.item() - 258.2_044_983		) < 1e-2
           assert abs(result_mean.item() - 0.3_362_038		) < 1e-3



      def 		snake_case_   (	self	:      Tuple		):
           pass



      def 		snake_case_   (	self	:      Optional[Any]		):
           pass





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase							:   List[Any]				=				{
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase							:   int				=				[
     "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LiltForQuestionAnswering",
     "LiltForSequenceClassification",
     "LiltForTokenClassification",
     "LiltModel",
     "LiltPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_lilt import (
      LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
      LiltForQuestionAnswering,
      LiltForSequenceClassification,
      LiltForTokenClassification,
      LiltModel,
      LiltPreTrainedModel,
  )

else:
 import sys

 _lowerCAmelCase							:   Optional[int]				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
