





'''simple docstring'''


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Dict								=       (UnCLIPScheduler,)




 def 				_snake_case (						self      ,  **_lowerCAmelCase		)     ->				str:
     _lowerCAmelCase						      =   {
         "num_train_timesteps": 1000,
         "variance_type": "fixed_small_log",
         "clip_sample": True,
         "clip_sample_range": 1.0,
         "prediction_type": "epsilon",
     }

     config.update(**_lowerCAmelCase		)
     return config




 def 				_snake_case (						self		)     ->				List[str]:
     for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     for variance in ["fixed_small_log", "learned_range"]:
         self.check_over_configs(variance_type=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[str]:
     for clip_sample_range in [1, 5, 10, 20]:
         self.check_over_configs(clip_sample_range=_lowerCAmelCase		)




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




 def 				_snake_case (						self		)     ->				str:
     for time_step in [0, 500, 999]:
         for prev_timestep in [None, 5, 100, 250, 500, 750]:
             if prev_timestep is not None and prev_timestep >= time_step:
                 continue

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




 def 				_snake_case (						self		)     ->				List[Any]:
     _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		) - 1.0000E-10		)		) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0549625		)		) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.9994987		)		) < 1E-5




 def 				_snake_case (						self		)     ->				List[Any]:
     _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		) - -10.1712790 < 1E-5
     assert scheduler._get_variance(487      ,  predicted_variance=_lowerCAmelCase		) - -5.7998052 < 1E-5
     assert scheduler._get_variance(999      ,  predicted_variance=_lowerCAmelCase		) - -0.0010011 < 1E-5




 def 				_snake_case (						self		)     ->				List[str]:
     _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() - 252.2682495		) < 1E-2
     assert abs(result_mean.item() - 0.3284743		) < 1E-3




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     scheduler.set_timesteps(25		)

     _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() - 258.2044983		) < 1E-2
     assert abs(result_mean.item() - 0.3362038		) < 1E-3




 def 				_snake_case (						self		)     ->				str:
     pass





 def 				_snake_case (						self		)     ->				List[str]:
     pass









'''simple docstring'''


from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE									=				{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE									=				[
           "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MCTCTForCTC",
           "MCTCTModel",
           "MCTCTPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
       from .feature_extraction_mctct import MCTCTFeatureExtractor
       from .processing_mctct import MCTCTProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
       import sys

       _SCREAMING_SNAKE_CASE									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


