




'''simple docstring'''



import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class 		lowercase_   (			_lowerCAmelCase       ):


   """simple docstring"""

   lowerCamelCase_      =						(UnCLIPScheduler,)



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				**__lowerCamelCase :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				{
          "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(**_lowercase   )
      return config



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
         self.check_over_configs(num_train_timesteps=_lowercase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      for variance in ["fixed_small_log", "learned_range"]:
         self.check_over_configs(variance_type=_lowercase   )



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=_lowercase   )



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      for clip_sample_range in [1, 5, 1_0, 2_0]:
         self.check_over_configs(clip_sample_range=_lowercase   )



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      for prediction_type in ["epsilon", "sample"]:
         self.check_over_configs(prediction_type=_lowercase   )



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      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=_lowercase  ,				prev_timestep=_lowercase   )



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.scheduler_classes[0]
      _SCREAMING_SNAKE_CASE	=				self.get_scheduler_config(variance_type="fixed_small_log"   )
      _SCREAMING_SNAKE_CASE	=				scheduler_class(**_lowercase   )

      assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0_000e-10   )   ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_8_7   ) - 0.0_5_4_9_6_2_5   )   ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_9_9   ) - 0.9_9_9_4_9_8_7   )   ) < 1e-5



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.scheduler_classes[0]
      _SCREAMING_SNAKE_CASE	=				self.get_scheduler_config(variance_type="learned_range"   )
      _SCREAMING_SNAKE_CASE	=				scheduler_class(**_lowercase   )

      _SCREAMING_SNAKE_CASE	=				0.5

      assert scheduler._get_variance(1  ,				predicted_variance=_lowercase   ) - -1_0.1_7_1_2_7_9_0 < 1e-5
      assert scheduler._get_variance(4_8_7  ,				predicted_variance=_lowercase   ) - -5.7_9_9_8_0_5_2 < 1e-5
      assert scheduler._get_variance(9_9_9  ,				predicted_variance=_lowercase   ) - -0.0_0_1_0_0_1_1 < 1e-5



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.scheduler_classes[0]
      _SCREAMING_SNAKE_CASE	=				self.get_scheduler_config()
      _SCREAMING_SNAKE_CASE	=				scheduler_class(**_lowercase   )

      _SCREAMING_SNAKE_CASE	=				scheduler.timesteps

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

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

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

         _SCREAMING_SNAKE_CASE	=				pred_prev_sample

      _SCREAMING_SNAKE_CASE	=				torch.sum(torch.abs(_lowercase   )   )
      _SCREAMING_SNAKE_CASE	=				torch.mean(torch.abs(_lowercase   )   )

      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 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.scheduler_classes[0]
      _SCREAMING_SNAKE_CASE	=				self.get_scheduler_config()
      _SCREAMING_SNAKE_CASE	=				scheduler_class(**_lowercase   )

      scheduler.set_timesteps(2_5   )

      _SCREAMING_SNAKE_CASE	=				scheduler.timesteps

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

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

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

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

         _SCREAMING_SNAKE_CASE	=				pred_prev_sample

      _SCREAMING_SNAKE_CASE	=				torch.sum(torch.abs(_lowercase   )   )
      _SCREAMING_SNAKE_CASE	=				torch.mean(torch.abs(_lowercase   )   )

      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 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      pass



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      pass







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_			=						{
    'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
    'tokenization_ctrl': ['CTRLTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'CTRLForSequenceClassification',
     'CTRLLMHeadModel',
     'CTRLModel',
     'CTRLPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFCTRLForSequenceClassification',
     'TFCTRLLMHeadModel',
     'TFCTRLModel',
     'TFCTRLPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
 from .tokenization_ctrl import CTRLTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ctrl import (
      CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      CTRLForSequenceClassification,
      CTRLLMHeadModel,
      CTRLModel,
      CTRLPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_ctrl import (
      TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCTRLForSequenceClassification,
      TFCTRLLMHeadModel,
      TFCTRLModel,
      TFCTRLPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase_			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

