

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__ :	int    =   (PNDMScheduler,)
   lowerCamelCase__ :	Any    =   (('num_inference_steps', 50),)



   def  _UpperCAmelCase (				self,    **lowercase_       )							->    Any:






     """simple docstring"""






     a__  ={
         '''num_train_timesteps''': 1000,
         '''beta_start''': 0.0001,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
     }

     config.update(**lowercase_       )
     return config



   def  _UpperCAmelCase (				self,    lowercase_=0,    **lowercase_       )							->    Tuple:






     """simple docstring"""






     a__  =dict(self.forward_default_kwargs       )
     a__  =kwargs.pop('''num_inference_steps''',    lowercase_       )
     a__  =self.dummy_sample
     a__  =0.1 * sample
     a__  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

     for scheduler_class in self.scheduler_classes:
       a__  =self.get_scheduler_config(**lowercase_       )
       a__  =scheduler_class(**lowercase_       )
       scheduler.set_timesteps(lowercase_       )
       # copy over dummy past residuals
       a__  =dummy_past_residuals[:]

       with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(lowercase_       )
         a__  =scheduler_class.from_pretrained(lowercase_       )
         new_scheduler.set_timesteps(lowercase_       )
         # copy over dummy past residuals
         a__  =dummy_past_residuals[:]

       a__  =scheduler.step_prk(lowercase_,    lowercase_,    lowercase_,    **lowercase_       ).prev_sample
       a__  =new_scheduler.step_prk(lowercase_,    lowercase_,    lowercase_,    **lowercase_       ).prev_sample

       assert torch.sum(torch.abs(output - new_output       )       ) < 1E-5, "Scheduler outputs are not identical"

       a__  =scheduler.step_plms(lowercase_,    lowercase_,    lowercase_,    **lowercase_       ).prev_sample
       a__  =new_scheduler.step_plms(lowercase_,    lowercase_,    lowercase_,    **lowercase_       ).prev_sample

       assert torch.sum(torch.abs(output - new_output       )       ) < 1E-5, "Scheduler outputs are not identical"



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     pass



   def  _UpperCAmelCase (				self,    lowercase_=0,    **lowercase_       )							->    Any:






     """simple docstring"""






     a__  =dict(self.forward_default_kwargs       )
     a__  =kwargs.pop('''num_inference_steps''',    lowercase_       )
     a__  =self.dummy_sample
     a__  =0.1 * sample
     a__  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

     for scheduler_class in self.scheduler_classes:
       a__  =self.get_scheduler_config()
       a__  =scheduler_class(**lowercase_       )
       scheduler.set_timesteps(lowercase_       )

       # copy over dummy past residuals (must be after setting timesteps)
       a__  =dummy_past_residuals[:]

       with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(lowercase_       )
         a__  =scheduler_class.from_pretrained(lowercase_       )
         # copy over dummy past residuals
         new_scheduler.set_timesteps(lowercase_       )

         # copy over dummy past residual (must be after setting timesteps)
         a__  =dummy_past_residuals[:]

       a__  =scheduler.step_prk(lowercase_,    lowercase_,    lowercase_,    **lowercase_       ).prev_sample
       a__  =new_scheduler.step_prk(lowercase_,    lowercase_,    lowercase_,    **lowercase_       ).prev_sample

       assert torch.sum(torch.abs(output - new_output       )       ) < 1E-5, "Scheduler outputs are not identical"

       a__  =scheduler.step_plms(lowercase_,    lowercase_,    lowercase_,    **lowercase_       ).prev_sample
       a__  =new_scheduler.step_plms(lowercase_,    lowercase_,    lowercase_,    **lowercase_       ).prev_sample

       assert torch.sum(torch.abs(output - new_output       )       ) < 1E-5, "Scheduler outputs are not identical"



   def  _UpperCAmelCase (				self,    **lowercase_       )							->    Dict:






     """simple docstring"""






     a__  =self.scheduler_classes[0]
     a__  =self.get_scheduler_config(**lowercase_       )
     a__  =scheduler_class(**lowercase_       )

     a__  =10
     a__  =self.dummy_model()
     a__  =self.dummy_sample_deter
     scheduler.set_timesteps(lowercase_       )

     for i, t in enumerate(scheduler.prk_timesteps       ):
       a__  =model(lowercase_,    lowercase_       )
       a__  =scheduler.step_prk(lowercase_,    lowercase_,    lowercase_       ).prev_sample

     for i, t in enumerate(scheduler.plms_timesteps       ):
       a__  =model(lowercase_,    lowercase_       )
       a__  =scheduler.step_plms(lowercase_,    lowercase_,    lowercase_       ).prev_sample

     return sample



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     a__  =dict(self.forward_default_kwargs       )

     a__  =kwargs.pop('''num_inference_steps''',    lowercase_       )

     for scheduler_class in self.scheduler_classes:
       a__  =self.get_scheduler_config()
       a__  =scheduler_class(**lowercase_       )

       a__  =self.dummy_sample
       a__  =0.1 * sample

       if num_inference_steps is not None and hasattr(lowercase_,    '''set_timesteps'''       ):
         scheduler.set_timesteps(lowercase_       )
       elif num_inference_steps is not None and not hasattr(lowercase_,    '''set_timesteps'''       ):
         a__  =num_inference_steps

       # copy over dummy past residuals (must be done after set_timesteps)
       a__  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
       a__  =dummy_past_residuals[:]

       a__  =scheduler.step_prk(lowercase_,    0,    lowercase_,    **lowercase_       ).prev_sample
       a__  =scheduler.step_prk(lowercase_,    1,    lowercase_,    **lowercase_       ).prev_sample

       self.assertEqual(output_a.shape,    sample.shape       )
       self.assertEqual(output_a.shape,    output_a.shape       )

       a__  =scheduler.step_plms(lowercase_,    0,    lowercase_,    **lowercase_       ).prev_sample
       a__  =scheduler.step_plms(lowercase_,    1,    lowercase_,    **lowercase_       ).prev_sample

       self.assertEqual(output_a.shape,    sample.shape       )
       self.assertEqual(output_a.shape,    output_a.shape       )



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     for timesteps in [100, 1000]:
       self.check_over_configs(num_train_timesteps=lowercase_       )



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     for steps_offset in [0, 1]:
       self.check_over_configs(steps_offset=lowercase_       )

     a__  =self.scheduler_classes[0]
     a__  =self.get_scheduler_config(steps_offset=1       )
     a__  =scheduler_class(**lowercase_       )
     scheduler.set_timesteps(10       )
     assert torch.equal(
         scheduler.timesteps,    torch.LongTensor(
             [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]       ),    )



   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     for beta_start, beta_end in zip([0.0001, 0.001],    [0.002, 0.02]       ):
       self.check_over_configs(beta_start=lowercase_,    beta_end=lowercase_       )



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=lowercase_       )



   def  _UpperCAmelCase (				self       )							->    List[str]:






     """simple docstring"""






     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=lowercase_       )



   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     for t in [1, 5, 10]:
       self.check_over_forward(time_step=lowercase_       )



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     for t, num_inference_steps in zip([1, 5, 10],    [10, 50, 100]       ):
       self.check_over_forward(num_inference_steps=lowercase_       )



   def  _UpperCAmelCase (				self       )							->    List[Any]:






     """simple docstring"""






     a__  =27

     for scheduler_class in self.scheduler_classes:
       a__  =self.dummy_sample
       a__  =0.1 * sample

       a__  =self.get_scheduler_config()
       a__  =scheduler_class(**lowercase_       )

       scheduler.set_timesteps(lowercase_       )

       # before power of 3 fix, would error on first step, so we only need to do two
       for i, t in enumerate(scheduler.prk_timesteps[:2]       ):
         a__  =scheduler.step_prk(lowercase_,    lowercase_,    lowercase_       ).prev_sample



   def  _UpperCAmelCase (				self       )							->    Dict:






     """simple docstring"""






     with self.assertRaises(lowercase_       ):
       a__  =self.scheduler_classes[0]
       a__  =self.get_scheduler_config()
       a__  =scheduler_class(**lowercase_       )

       scheduler.step_plms(self.dummy_sample,    1,    self.dummy_sample       ).prev_sample



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     a__  =self.full_loop()
     a__  =torch.sum(torch.abs(lowercase_       )       )
     a__  =torch.mean(torch.abs(lowercase_       )       )

     assert abs(result_sum.item() - 198.1318       ) < 1E-2
     assert abs(result_mean.item() - 0.2580       ) < 1E-3



   def  _UpperCAmelCase (				self       )							->    Tuple:






     """simple docstring"""






     a__  =self.full_loop(prediction_type='''v_prediction'''       )
     a__  =torch.sum(torch.abs(lowercase_       )       )
     a__  =torch.mean(torch.abs(lowercase_       )       )

     assert abs(result_sum.item() - 67.3986       ) < 1E-2
     assert abs(result_mean.item() - 0.0878       ) < 1E-3



   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






     a__  =self.full_loop(set_alpha_to_one=lowercase_,    beta_start=0.01       )
     a__  =torch.sum(torch.abs(lowercase_       )       )
     a__  =torch.mean(torch.abs(lowercase_       )       )

     assert abs(result_sum.item() - 230.0399       ) < 1E-2
     assert abs(result_mean.item() - 0.2995       ) < 1E-3



   def  _UpperCAmelCase (				self       )							->    Tuple:






     """simple docstring"""






     a__  =self.full_loop(set_alpha_to_one=lowercase_,    beta_start=0.01       )
     a__  =torch.sum(torch.abs(lowercase_       )       )
     a__  =torch.mean(torch.abs(lowercase_       )       )

     assert abs(result_sum.item() - 186.9482       ) < 1E-2
     assert abs(result_mean.item() - 0.2434       ) < 1E-3



from typing import TYPE_CHECKING

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


lowerCamelCase           =					{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''CanineForMultipleChoice''',
           '''CanineForQuestionAnswering''',
           '''CanineForSequenceClassification''',
           '''CanineForTokenClassification''',
           '''CanineLayer''',
           '''CanineModel''',
           '''CaninePreTrainedModel''',
           '''load_tf_weights_in_canine''',
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

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