'''simple docstring'''




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class 		__A       (__magic_name__   ):
       snake_case       :Union[str, Any]				    =					(UnCLIPScheduler,)






       def 			_snake_case						(      self				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				Tuple       				=		{
                  "num_train_timesteps": 10_00,
                  "variance_type": "fixed_small_log",
                  "clip_sample": True,
                  "clip_sample_range": 1.0,
                  "prediction_type": "epsilon",
              }

              config.update(**UpperCamelCase_      )
              return config






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






       def 			_snake_case						(      self      ):
              for variance in ["fixed_small_log", "learned_range"]:
                     self.check_over_configs(variance_type=UpperCamelCase_      )






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






       def 			_snake_case						(      self      ):
              for clip_sample_range in [1, 5, 10, 20]:
                     self.check_over_configs(clip_sample_range=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(prediction_type=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              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=UpperCamelCase_				,     prev_timestep=UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Dict       				=		self.scheduler_classes[0]
              __UpperCAmelCase  :				Optional[int]       				=		self.get_scheduler_config(variance_type="fixed_small_log"      )
              __UpperCAmelCase  :				int       				=		scheduler_class(**UpperCamelCase_      )

              assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 1.0000E-10      )      ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(4_87      ) - 0.0_5_4_9_6_2_5      )      ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(9_99      ) - 0.9_9_9_4_9_8_7      )      ) < 1E-5






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Tuple       				=		self.scheduler_classes[0]
              __UpperCAmelCase  :				str       				=		self.get_scheduler_config(variance_type="learned_range"      )
              __UpperCAmelCase  :				Any       				=		scheduler_class(**UpperCamelCase_      )

              __UpperCAmelCase  :				Tuple       				=		0.5

              assert scheduler._get_variance(1				,     predicted_variance=UpperCamelCase_      ) - -1_0.1_7_1_2_7_9_0 < 1E-5
              assert scheduler._get_variance(4_87				,     predicted_variance=UpperCamelCase_      ) - -5.7_9_9_8_0_5_2 < 1E-5
              assert scheduler._get_variance(9_99				,     predicted_variance=UpperCamelCase_      ) - -0.0_0_1_0_0_1_1 < 1E-5






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Tuple       				=		self.scheduler_classes[0]
              __UpperCAmelCase  :				Dict       				=		self.get_scheduler_config()
              __UpperCAmelCase  :				Tuple       				=		scheduler_class(**UpperCamelCase_      )

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

              __UpperCAmelCase  :				Any       				=		self.dummy_model()
              __UpperCAmelCase  :				List[Any]       				=		self.dummy_sample_deter
              __UpperCAmelCase  :				Any       				=		torch.manual_seed(0      )

              for i, t in enumerate(UpperCamelCase_      ):
                     # 1. predict noise residual
                     __UpperCAmelCase  :				Dict       				=		model(UpperCamelCase_				,     UpperCamelCase_      )

                     # 2. predict previous mean of sample x_t-1
                     __UpperCAmelCase  :				Optional[int]       				=		scheduler.step(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     generator=UpperCamelCase_      ).prev_sample

                     __UpperCAmelCase  :				List[Any]       				=		pred_prev_sample

              __UpperCAmelCase  :				Tuple       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
              __UpperCAmelCase  :				Tuple       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

              assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5      ) < 1E-2
              assert abs(result_mean.item() - 0.3_2_8_4_7_4_3      ) < 1E-3






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Tuple       				=		self.scheduler_classes[0]
              __UpperCAmelCase  :				List[str]       				=		self.get_scheduler_config()
              __UpperCAmelCase  :				str       				=		scheduler_class(**UpperCamelCase_      )

              scheduler.set_timesteps(25      )

              __UpperCAmelCase  :				str       				=		scheduler.timesteps

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

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

                     if i + 1 == timesteps.shape[0]:
                            __UpperCAmelCase  :				Optional[int]       				=		None
                     else:
                            __UpperCAmelCase  :				Dict       				=		timesteps[i + 1]

                     # 2. predict previous mean of sample x_t-1
                     __UpperCAmelCase  :				Tuple       				=		scheduler.step(
                         UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     prev_timestep=UpperCamelCase_				,     generator=UpperCamelCase_      ).prev_sample

                     __UpperCAmelCase  :				Optional[int]       				=		pred_prev_sample

              __UpperCAmelCase  :				str       				=		torch.sum(torch.abs(UpperCamelCase_      )      )
              __UpperCAmelCase  :				Union[str, Any]       				=		torch.mean(torch.abs(UpperCamelCase_      )      )

              assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3      ) < 1E-2
              assert abs(result_mean.item() - 0.3_3_6_2_0_3_8      ) < 1E-3






       def 			_snake_case						(      self      ):
              pass







       def 			_snake_case						(      self      ):
              pass







'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_a   :	Dict 							=   {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   ["ReformerTokenizer"]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	List[Any] 							=   ["ReformerTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ReformerAttention",
       "ReformerForMaskedLM",
       "ReformerForQuestionAnswering",
       "ReformerForSequenceClassification",
       "ReformerLayer",
       "ReformerModel",
       "ReformerModelWithLMHead",
       "ReformerPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

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






