



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			(UniPCMultistepScheduler,)
  a_      =			(("""num_inference_steps""", 25),)






  def     lowercase		(    self			:		Tuple ,  **lowerCAmelCase_			:		Any					)							->						int:
     __lowerCAmelCase							     =     {
         'num_train_timesteps': 1_0_0_0,
         'beta_start': 0.00_01,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
         'solver_order': 2,
         'solver_type': 'bh2',
     }

     config.update(**lowerCAmelCase_					)
     return config






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		Optional[int]=0 ,  **lowerCAmelCase_			:		Any					)							->						List[Any]:
     __lowerCAmelCase							     =     dict(self.forward_default_kwargs					)
     __lowerCAmelCase							     =     kwargs.pop('num_inference_steps' ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     self.dummy_sample
     __lowerCAmelCase							     =     0.1 * sample
     __lowerCAmelCase							     =     [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

        __lowerCAmelCase     ,     __lowerCAmelCase							     =     sample, sample
        for t in range(lowerCAmelCase_ ,  time_step + scheduler.config.solver_order + 1					):
           __lowerCAmelCase							     =     scheduler.step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  **lowerCAmelCase_					).prev_sample
           __lowerCAmelCase							     =     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     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		str=0 ,  **lowerCAmelCase_			:		Dict					)							->						Tuple:
     __lowerCAmelCase							     =     dict(self.forward_default_kwargs					)
     __lowerCAmelCase							     =     kwargs.pop('num_inference_steps' ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     self.dummy_sample
     __lowerCAmelCase							     =     0.1 * sample
     __lowerCAmelCase							     =     [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
        __lowerCAmelCase							     =     self.get_scheduler_config()
        __lowerCAmelCase							     =     scheduler_class(**lowerCAmelCase_					)
        scheduler.set_timesteps(lowerCAmelCase_					)

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

        with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(lowerCAmelCase_					)
           __lowerCAmelCase							     =     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)
           __lowerCAmelCase							     =     dummy_past_residuals[: new_scheduler.config.solver_order]

        __lowerCAmelCase							     =     scheduler.step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  **lowerCAmelCase_					).prev_sample
        __lowerCAmelCase							     =     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     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		Optional[Any]=None ,  **lowerCAmelCase_			:		Tuple					)							->						List[str]:
     if scheduler is None:
        __lowerCAmelCase							     =     self.scheduler_classes[0]
        __lowerCAmelCase							     =     self.get_scheduler_config(**lowerCAmelCase_					)
        __lowerCAmelCase							     =     scheduler_class(**lowerCAmelCase_					)

     __lowerCAmelCase							     =     self.scheduler_classes[0]
     __lowerCAmelCase							     =     self.get_scheduler_config(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     scheduler_class(**lowerCAmelCase_					)

     __lowerCAmelCase							     =     1_0
     __lowerCAmelCase							     =     self.dummy_model()
     __lowerCAmelCase							     =     self.dummy_sample_deter
     scheduler.set_timesteps(lowerCAmelCase_					)

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

     return sample






  def     lowercase		(    self			:		Tuple					)							->						Tuple:
     __lowerCAmelCase							     =     dict(self.forward_default_kwargs					)

     __lowerCAmelCase							     =     kwargs.pop('num_inference_steps' ,  lowerCAmelCase_					)

     for scheduler_class in self.scheduler_classes:
        __lowerCAmelCase							     =     self.get_scheduler_config()
        __lowerCAmelCase							     =     scheduler_class(**lowerCAmelCase_					)

        __lowerCAmelCase							     =     self.dummy_sample
        __lowerCAmelCase							     =     0.1 * sample

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

        # copy over dummy past residuals (must be done after set_timesteps)
        __lowerCAmelCase							     =     [residual + 0.2, residual + 0.15, residual + 0.10]
        __lowerCAmelCase							     =     dummy_past_residuals[: scheduler.config.solver_order]

        __lowerCAmelCase							     =     scheduler.timesteps[5]
        __lowerCAmelCase							     =     scheduler.timesteps[6]

        __lowerCAmelCase							     =     scheduler.step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  **lowerCAmelCase_					).prev_sample
        __lowerCAmelCase							     =     scheduler.step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  **lowerCAmelCase_					).prev_sample

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






  def     lowercase		(    self			:		int					)							->						int:
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     __lowerCAmelCase							     =     UniPCMultistepScheduler(**self.get_scheduler_config()					)
     __lowerCAmelCase							     =     self.full_loop(scheduler=lowerCAmelCase_					)
     __lowerCAmelCase							     =     torch.mean(torch.abs(lowerCAmelCase_					)					)

     assert abs(result_mean.item() - 0.24_64					) < 1e-3

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

     __lowerCAmelCase							     =     self.full_loop(scheduler=lowerCAmelCase_					)
     __lowerCAmelCase							     =     torch.mean(torch.abs(lowerCAmelCase_					)					)

     assert abs(result_mean.item() - 0.24_64					) < 1e-3






  def     lowercase		(    self			:		Optional[Any]					)							->						int:
     for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase_					)






  def     lowercase		(    self			:		Dict					)							->						int:
     self.check_over_configs(thresholding=lowerCAmelCase_					)
     for order in [1, 2, 3]:
        for solver_type in ["bh1", "bh2"]:
           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_ ,  solver_order=lowerCAmelCase_ ,  solver_type=lowerCAmelCase_ ,  )






  def     lowercase		(    self			:		List[Any]					)							->						Optional[int]:
     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[Any]					)							->						Optional[int]:
     for solver_type in ["bh1", "bh2"]:
        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_ ,  )
              __lowerCAmelCase							     =     self.full_loop(
                  solver_order=lowerCAmelCase_ ,  solver_type=lowerCAmelCase_ ,  prediction_type=lowerCAmelCase_ ,  )
              assert not torch.isnan(lowerCAmelCase_					).any(), "Samples have nan numbers"






  def     lowercase		(    self			:		Optional[Any]					)							->						Union[str, Any]:
     self.check_over_configs(lower_order_final=lowerCAmelCase_					)
     self.check_over_configs(lower_order_final=lowerCAmelCase_					)






  def     lowercase		(    self			:		int					)							->						Optional[int]:
     for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
        self.check_over_forward(num_inference_steps=lowerCAmelCase_ ,  time_step=0					)






  def     lowercase		(    self			:		List[Any]					)							->						int:
     __lowerCAmelCase							     =     self.full_loop()
     __lowerCAmelCase							     =     torch.mean(torch.abs(lowerCAmelCase_					)					)

     assert abs(result_mean.item() - 0.24_64					) < 1e-3






  def     lowercase		(    self			:		int					)							->						List[str]:
     __lowerCAmelCase							     =     self.full_loop(prediction_type='v_prediction'					)
     __lowerCAmelCase							     =     torch.mean(torch.abs(lowerCAmelCase_					)					)

     assert abs(result_mean.item() - 0.10_14					) < 1e-3






  def     lowercase		(    self			:		Optional[Any]					)							->						Any:
     __lowerCAmelCase							     =     self.scheduler_classes[0]
     __lowerCAmelCase							     =     self.get_scheduler_config(thresholding=lowerCAmelCase_ ,  dynamic_thresholding_ratio=0					)
     __lowerCAmelCase							     =     scheduler_class(**lowerCAmelCase_					)

     __lowerCAmelCase							     =     1_0
     __lowerCAmelCase							     =     self.dummy_model()
     __lowerCAmelCase							     =     self.dummy_sample_deter.half()
     scheduler.set_timesteps(lowerCAmelCase_					)

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

     assert sample.dtype == torch.floataa






  def     lowercase		(    self			:		Union[str, Any] ,  **lowerCAmelCase_			:		Tuple					)							->						str:
     for scheduler_class in self.scheduler_classes:
        __lowerCAmelCase							     =     self.get_scheduler_config(**lowerCAmelCase_					)
        __lowerCAmelCase							     =     scheduler_class(**lowerCAmelCase_					)

        scheduler.set_timesteps(scheduler.config.num_train_timesteps					)
        assert len(scheduler.timesteps.unique()					) == scheduler.num_inference_steps








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case  :   Optional[Any]						   =  {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   int						   =  ['FNetTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   Tuple						   =  ['FNetTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   List[str]						   =  [
         'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'FNetForMaskedLM',
         'FNetForMultipleChoice',
         'FNetForNextSentencePrediction',
         'FNetForPreTraining',
         'FNetForQuestionAnswering',
         'FNetForSequenceClassification',
         'FNetForTokenClassification',
         'FNetLayer',
         'FNetModel',
         'FNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     _snake_case  :   str						   =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

