


"""simple docstring"""






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	List[str]    =		(UnCLIPScheduler,)


     def 							_UpperCAmelCase      (      self    ,				**__UpperCAmelCase							)       ->							str:
          _a    	=				{
              '''num_train_timesteps''': 1000,
              '''variance_type''': '''fixed_small_log''',
              '''clip_sample''': True,
              '''clip_sample_range''': 1.0,
              '''prediction_type''': '''epsilon''',
          }

          config.update(**__UpperCAmelCase							)
          return config


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          for timesteps in [1, 5, 100, 1000]:
               self.check_over_configs(num_train_timesteps=__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							int:
          for variance in ["fixed_small_log", "learned_range"]:
               self.check_over_configs(variance_type=__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__UpperCAmelCase							)


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


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


     def 							_UpperCAmelCase      (      self							)       ->							List[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=__UpperCAmelCase    ,				prev_timestep=__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				self.scheduler_classes[0]
          _a    	=				self.get_scheduler_config(variance_type='''fixed_small_log'''							)
          _a    	=				scheduler_class(**__UpperCAmelCase							)

          assert torch.sum(torch.abs(scheduler._get_variance(0							) - 1.0_0_0_0e-1_0							)							) < 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 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				self.scheduler_classes[0]
          _a    	=				self.get_scheduler_config(variance_type='''learned_range'''							)
          _a    	=				scheduler_class(**__UpperCAmelCase							)

          _a    	=				0.5

          assert scheduler._get_variance(1    ,				predicted_variance=__UpperCAmelCase							) - -10.1712790 < 1e-5
          assert scheduler._get_variance(487    ,				predicted_variance=__UpperCAmelCase							) - -5.7998052 < 1e-5
          assert scheduler._get_variance(999    ,				predicted_variance=__UpperCAmelCase							) - -0.0010011 < 1e-5


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				self.scheduler_classes[0]
          _a    	=				self.get_scheduler_config()
          _a    	=				scheduler_class(**__UpperCAmelCase							)

          _a    	=				scheduler.timesteps

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

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

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

               _a    	=				pred_prev_sample

          _a    	=				torch.sum(torch.abs(__UpperCAmelCase							)							)
          _a    	=				torch.mean(torch.abs(__UpperCAmelCase							)							)

          assert abs(result_sum.item() - 252.2682495							) < 1e-2
          assert abs(result_mean.item() - 0.3284743							) < 1e-3


     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				self.scheduler_classes[0]
          _a    	=				self.get_scheduler_config()
          _a    	=				scheduler_class(**__UpperCAmelCase							)

          scheduler.set_timesteps(25							)

          _a    	=				scheduler.timesteps

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

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

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

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

               _a    	=				pred_prev_sample

          _a    	=				torch.sum(torch.abs(__UpperCAmelCase							)							)
          _a    	=				torch.mean(torch.abs(__UpperCAmelCase							)							)

          assert abs(result_sum.item() - 258.2044983							) < 1e-2
          assert abs(result_mean.item() - 0.3362038							) < 1e-3


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          pass





     def 							_UpperCAmelCase      (      self							)       ->							Any:
          pass



"""simple docstring"""






from typing import TYPE_CHECKING

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


__snake_case		=       {
    '''configuration_pix2struct''': [
        '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Pix2StructConfig''',
        '''Pix2StructTextConfig''',
        '''Pix2StructVisionConfig''',
    ],
    '''processing_pix2struct''': ['''Pix2StructProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       ['''Pix2StructImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Pix2StructPreTrainedModel''',
       '''Pix2StructForConditionalGeneration''',
       '''Pix2StructVisionModel''',
       '''Pix2StructTextModel''',
   ]

if TYPE_CHECKING:
   from .configuration_pixastruct import (
       PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       PixaStructConfig,
       PixaStructTextConfig,
       PixaStructVisionConfig,
   )
   from .processing_pixastruct import PixaStructProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_pixastruct import PixaStructImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_pixastruct import (
          PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
          PixaStructForConditionalGeneration,
          PixaStructPreTrainedModel,
          PixaStructTextModel,
          PixaStructVisionModel,
      )

else:
   import sys

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