






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (DPMSolverSinglestepScheduler,)
  _lowerCamelCase    =  (('''num_inference_steps''', 25),)


  def  lowerCamelCase       (      self		:			Optional[int]       ,				**lowerCamelCase		:			int							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    {
             """num_train_timesteps""": 1000,
             """beta_start""": 0.0001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
             """solver_order""": 2,
             """prediction_type""": """epsilon""",
             """thresholding""": False,
             """sample_max_value""": 1.0,
             """algorithm_type""": """dpmsolver++""",
             """solver_type""": """midpoint""",
             """lambda_min_clipped""": -float("""inf"""							),
             """variance_type""": None,
         }

         config.update(**lowerCamelCase							)
         return config


  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			List[str]=0       ,				**lowerCamelCase		:			Union[str, Any]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    dict(self.forward_default_kwargs							)
         _UpperCAmelCase							=    kwargs.pop("""num_inference_steps"""       ,				lowerCamelCase							)
         _UpperCAmelCase							=    self.dummy_sample
         _UpperCAmelCase							=    0.1 * sample
         _UpperCAmelCase							=    [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
                _UpperCAmelCase							=    self.get_scheduler_config(**lowerCamelCase							)
                _UpperCAmelCase							=    scheduler_class(**lowerCamelCase							)
                scheduler.set_timesteps(lowerCamelCase							)
                # copy over dummy past residuals
                _UpperCAmelCase							=    dummy_past_residuals[: scheduler.config.solver_order]

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

                _UpperCAmelCase	,			_UpperCAmelCase							=    sample, sample
                for t in range(lowerCamelCase       ,				time_step + scheduler.config.solver_order + 1							):
                       _UpperCAmelCase							=    scheduler.step(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				**lowerCamelCase							).prev_sample
                       _UpperCAmelCase							=    new_scheduler.step(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				**lowerCamelCase							).prev_sample

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


  def  lowerCamelCase       (      self		:			Tuple							)						->	Optional[Any]:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			Optional[int]=0       ,				**lowerCamelCase		:			Tuple							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    dict(self.forward_default_kwargs							)
         _UpperCAmelCase							=    kwargs.pop("""num_inference_steps"""       ,				lowerCamelCase							)
         _UpperCAmelCase							=    self.dummy_sample
         _UpperCAmelCase							=    0.1 * sample
         _UpperCAmelCase							=    [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
                _UpperCAmelCase							=    self.get_scheduler_config()
                _UpperCAmelCase							=    scheduler_class(**lowerCamelCase							)
                scheduler.set_timesteps(lowerCamelCase							)

                # copy over dummy past residuals (must be after setting timesteps)
                _UpperCAmelCase							=    dummy_past_residuals[: scheduler.config.solver_order]

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

                       # copy over dummy past residual (must be after setting timesteps)
                       _UpperCAmelCase							=    dummy_past_residuals[: new_scheduler.config.solver_order]

                _UpperCAmelCase							=    scheduler.step(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				**lowerCamelCase							).prev_sample
                _UpperCAmelCase							=    new_scheduler.step(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				**lowerCamelCase							).prev_sample

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


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			Dict=None       ,				**lowerCamelCase		:			Optional[Any]							)						->	str:


         """simple docstring"""
         if scheduler is None:
                _UpperCAmelCase							=    self.scheduler_classes[0]
                _UpperCAmelCase							=    self.get_scheduler_config(**lowerCamelCase							)
                _UpperCAmelCase							=    scheduler_class(**lowerCamelCase							)

         _UpperCAmelCase							=    self.scheduler_classes[0]
         _UpperCAmelCase							=    self.get_scheduler_config(**lowerCamelCase							)
         _UpperCAmelCase							=    scheduler_class(**lowerCamelCase							)

         _UpperCAmelCase							=    10
         _UpperCAmelCase							=    self.dummy_model()
         _UpperCAmelCase							=    self.dummy_sample_deter
         scheduler.set_timesteps(lowerCamelCase							)

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

         return sample


  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    DPMSolverSinglestepScheduler(**self.get_scheduler_config()							)
         _UpperCAmelCase							=    50
         _UpperCAmelCase							=    self.dummy_model()
         _UpperCAmelCase							=    self.dummy_sample_deter
         scheduler.set_timesteps(lowerCamelCase							)

         # make sure that the first t is uneven
         for i, t in enumerate(scheduler.timesteps[3:]							):
                _UpperCAmelCase							=    model(lowerCamelCase       ,				lowerCamelCase							)
                _UpperCAmelCase							=    scheduler.step(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							).prev_sample

         _UpperCAmelCase							=    torch.mean(torch.abs(lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.2574							) < 1E-3


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[str]:


         """simple docstring"""
         for timesteps in [25, 50, 100, 999, 1000]:
                self.check_over_configs(num_train_timesteps=lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Any							)						->	str:


         """simple docstring"""
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         _UpperCAmelCase							=    DPMSolverSinglestepScheduler(**self.get_scheduler_config()							)
         _UpperCAmelCase							=    self.full_loop(scheduler=lowerCamelCase							)
         _UpperCAmelCase							=    torch.mean(torch.abs(lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.2791							) < 1E-3

         _UpperCAmelCase							=    DEISMultistepScheduler.from_config(scheduler.config							)
         _UpperCAmelCase							=    DPMSolverMultistepScheduler.from_config(scheduler.config							)
         _UpperCAmelCase							=    UniPCMultistepScheduler.from_config(scheduler.config							)
         _UpperCAmelCase							=    DPMSolverSinglestepScheduler.from_config(scheduler.config							)

         _UpperCAmelCase							=    self.full_loop(scheduler=lowerCamelCase							)
         _UpperCAmelCase							=    torch.mean(torch.abs(lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.2791							) < 1E-3


  def  lowerCamelCase       (      self		:			List[str]							)						->	Optional[Any]:


         """simple docstring"""
         self.check_over_configs(thresholding=lowerCamelCase							)
         for order in [1, 2, 3]:
                for solver_type in ["midpoint", "heun"]:
                       for threshold in [0.5, 1.0, 2.0]:
                              for prediction_type in ["epsilon", "sample"]:
                                     self.check_over_configs(
                                         thresholding=lowerCamelCase       ,				prediction_type=lowerCamelCase       ,				sample_max_value=lowerCamelCase       ,				algorithm_type="""dpmsolver++"""       ,				solver_order=lowerCamelCase       ,				solver_type=lowerCamelCase       ,				)


  def  lowerCamelCase       (      self		:			Tuple							)						->	int:


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


  def  lowerCamelCase       (      self		:			List[str]							)						->	Dict:


         """simple docstring"""
         for algorithm_type in ["dpmsolver", "dpmsolver++"]:
                for solver_type in ["midpoint", "heun"]:
                       for order in [1, 2, 3]:
                              for prediction_type in ["epsilon", "sample"]:
                                     self.check_over_configs(
                                         solver_order=lowerCamelCase       ,				solver_type=lowerCamelCase       ,				prediction_type=lowerCamelCase       ,				algorithm_type=lowerCamelCase       ,				)
                                     _UpperCAmelCase							=    self.full_loop(
                                         solver_order=lowerCamelCase       ,				solver_type=lowerCamelCase       ,				prediction_type=lowerCamelCase       ,				algorithm_type=lowerCamelCase       ,				)
                                     assert not torch.isnan(lowerCamelCase							).any(), "Samples have nan numbers"


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Dict:


         """simple docstring"""
         self.check_over_configs(lower_order_final=lowerCamelCase							)
         self.check_over_configs(lower_order_final=lowerCamelCase							)


  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         self.check_over_configs(lambda_min_clipped=-float("""inf"""							)							)
         self.check_over_configs(lambda_min_clipped=-5.1							)


  def  lowerCamelCase       (      self		:			str							)						->	Optional[Any]:


         """simple docstring"""
         self.check_over_configs(variance_type=lowerCamelCase							)
         self.check_over_configs(variance_type="""learned_range"""							)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Tuple:


         """simple docstring"""
         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                self.check_over_forward(num_inference_steps=lowerCamelCase       ,				time_step=0							)


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


         """simple docstring"""
         _UpperCAmelCase							=    self.full_loop()
         _UpperCAmelCase							=    torch.mean(torch.abs(lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.2791							) < 1E-3


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    self.full_loop(use_karras_sigmas=lowerCamelCase							)
         _UpperCAmelCase							=    torch.mean(torch.abs(lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.2248							) < 1E-3


  def  lowerCamelCase       (      self		:			List[str]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.full_loop(prediction_type="""v_prediction"""							)
         _UpperCAmelCase							=    torch.mean(torch.abs(lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.1453							) < 1E-3


  def  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.full_loop(prediction_type="""v_prediction"""       ,				use_karras_sigmas=lowerCamelCase							)
         _UpperCAmelCase							=    torch.mean(torch.abs(lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.0649							) < 1E-3





  def  lowerCamelCase       (      self		:			Dict							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    self.scheduler_classes[0]
         _UpperCAmelCase							=    self.get_scheduler_config(thresholding=lowerCamelCase       ,				dynamic_thresholding_ratio=0							)
         _UpperCAmelCase							=    scheduler_class(**lowerCamelCase							)

         _UpperCAmelCase							=    10
         _UpperCAmelCase							=    self.dummy_model()
         _UpperCAmelCase							=    self.dummy_sample_deter.half()
         scheduler.set_timesteps(lowerCamelCase							)

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

         assert sample.dtype == torch.floataa







from typing import TYPE_CHECKING

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


__a:     int   					=					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Dict   					=					['''MvpTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Tuple   					=					[
      '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MvpForCausalLM''',
      '''MvpForConditionalGeneration''',
      '''MvpForQuestionAnswering''',
      '''MvpForSequenceClassification''',
      '''MvpModel''',
      '''MvpPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
  from .tokenization_mvp import MvpTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mvp_fast import MvpTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mvp import (
        MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
        MvpForCausalLM,
        MvpForConditionalGeneration,
        MvpForQuestionAnswering,
        MvpForSequenceClassification,
        MvpModel,
        MvpPreTrainedModel,
    )

else:
  import sys

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