



import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					str		      =		(IPNDMScheduler,)
       UpperCamelCase__						:					Union[str, Any]		      =		(('''num_inference_steps''', 50),)



       def 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	**__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

           __a		 =       {"""num_train_timesteps""": 1_000}
           config.update(**A__)
           return config



       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=0  ,	**__SCREAMING_SNAKE_CASE							:  Tuple):


           '''simple docstring'''

           __a		 =       dict(self.forward_default_kwargs)
           __a		 =       kwargs.pop('''num_inference_steps'''  ,	A__)
           __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__)
               __a		 =       scheduler_class(**A__)
               scheduler.set_timesteps(A__)
               # copy over dummy past residuals
               __a		 =       dummy_past_residuals[:]

               if time_step is None:
                   __a		 =       scheduler.timesteps[len(scheduler.timesteps) // 2]

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

               __a		 =       scheduler.step(A__  ,	A__  ,	A__  ,	**A__).prev_sample
               __a		 =       new_scheduler.step(A__  ,	A__  ,	A__  ,	**A__).prev_sample

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

               __a		 =       scheduler.step(A__  ,	A__  ,	A__  ,	**A__).prev_sample
               __a		 =       new_scheduler.step(A__  ,	A__  ,	A__  ,	**A__).prev_sample

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



       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           pass



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=0  ,	**__SCREAMING_SNAKE_CASE							:  List[Any]):


           '''simple docstring'''

           __a		 =       dict(self.forward_default_kwargs)
           __a		 =       kwargs.pop('''num_inference_steps'''  ,	A__)
           __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(**A__)
               scheduler.set_timesteps(A__)

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

               if time_step is None:
                   __a		 =       scheduler.timesteps[len(scheduler.timesteps) // 2]

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

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

               __a		 =       scheduler.step(A__  ,	A__  ,	A__  ,	**A__).prev_sample
               __a		 =       new_scheduler.step(A__  ,	A__  ,	A__  ,	**A__).prev_sample

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

               __a		 =       scheduler.step(A__  ,	A__  ,	A__  ,	**A__).prev_sample
               __a		 =       new_scheduler.step(A__  ,	A__  ,	A__  ,	**A__).prev_sample

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



       def 	_lowerCamelCase    (	self							:  Dict  ,	**__SCREAMING_SNAKE_CASE							:  List[Any]):


           '''simple docstring'''

           __a		 =       self.scheduler_classes[0]
           __a		 =       self.get_scheduler_config(**A__)
           __a		 =       scheduler_class(**A__)

           __a		 =       10
           __a		 =       self.dummy_model()
           __a		 =       self.dummy_sample_deter
           scheduler.set_timesteps(A__)

           for i, t in enumerate(scheduler.timesteps):
               __a		 =       model(A__  ,	A__)
               __a		 =       scheduler.step(A__  ,	A__  ,	A__).prev_sample

           for i, t in enumerate(scheduler.timesteps):
               __a		 =       model(A__  ,	A__)
               __a		 =       scheduler.step(A__  ,	A__  ,	A__).prev_sample

           return sample



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       dict(self.forward_default_kwargs)

           __a		 =       kwargs.pop('''num_inference_steps'''  ,	A__)

           for scheduler_class in self.scheduler_classes:
               __a		 =       self.get_scheduler_config()
               __a		 =       scheduler_class(**A__)

               __a		 =       self.dummy_sample
               __a		 =       0.1 * sample

               if num_inference_steps is not None and hasattr(A__  ,	'''set_timesteps'''):
                   scheduler.set_timesteps(A__)
               elif num_inference_steps is not None and not hasattr(A__  ,	'''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.timesteps[5]
               __a		 =       scheduler.timesteps[6]

               __a		 =       scheduler.step(A__  ,	A__  ,	A__  ,	**A__).prev_sample
               __a		 =       scheduler.step(A__  ,	A__  ,	A__  ,	**A__).prev_sample

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

               __a		 =       scheduler.step(A__  ,	A__  ,	A__  ,	**A__).prev_sample
               __a		 =       scheduler.step(A__  ,	A__  ,	A__  ,	**A__).prev_sample

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



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           for timesteps in [100, 1_000]:
               self.check_over_configs(num_train_timesteps=A__  ,	time_step=A__)



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

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





       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       self.full_loop()
           __a		 =       torch.mean(torch.abs(A__))

           assert abs(result_mean.item() - 2_540_529) < 10










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__snake_case		:Union[str, Any]   	=  {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:List[str]   	=  ['''ViTFeatureExtractor''']
 __snake_case		:Optional[Any]   	=  ['''ViTImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:str   	=  [
     '''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ViTForImageClassification''',
     '''ViTForMaskedImageModeling''',
     '''ViTModel''',
     '''ViTPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:Tuple   	=  [
     '''TFViTForImageClassification''',
     '''TFViTModel''',
     '''TFViTPreTrainedModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:Tuple   	=  [
     '''FlaxViTForImageClassification''',
     '''FlaxViTModel''',
     '''FlaxViTPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_vit import ViTFeatureExtractor
  from .image_processing_vit import ViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit import (
      VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTForImageClassification,
      ViTForMaskedImageModeling,
      ViTModel,
      ViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
 import sys

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





