

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  (DPMSolverSinglestepScheduler,)
  SCREAMING_SNAKE_CASE    :      List[str]       =  (('''num_inference_steps''', 25),)

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {
            '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(**_SCREAMING_SNAKE_CASE					)
        return config

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=0 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     dict(self.forward_default_kwargs					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.dummy_sample
        SCREAMING_SNAKE_CASE_				:  int           =     0.1 * sample
        SCREAMING_SNAKE_CASE_				:  Dict           =     [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
              # copy over dummy past residuals
              SCREAMING_SNAKE_CASE_				:  List[str]           =     dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)
                    new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
                    # copy over dummy past residuals
                    SCREAMING_SNAKE_CASE_				:  List[Any]           =     dummy_past_residuals[: new_scheduler.config.solver_order]

              SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[int]           =     sample, sample
              for t in range(_SCREAMING_SNAKE_CASE ,					time_step + scheduler.config.solver_order + 1					):
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     scheduler.step(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
                    SCREAMING_SNAKE_CASE_				:  Optional[int]           =     new_scheduler.step(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=0 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     dict(self.forward_default_kwargs					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.dummy_sample
        SCREAMING_SNAKE_CASE_				:  Tuple           =     0.1 * sample
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  int           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)
                    # copy over dummy past residuals
                    new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

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

              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler.step(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Dict           =     new_scheduler.step(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

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

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=None ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        if scheduler is None:
              SCREAMING_SNAKE_CASE_				:  List[str]           =     self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     10
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.dummy_model()
        SCREAMING_SNAKE_CASE_				:  Any           =     self.dummy_sample_deter
        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

        for i, t in enumerate(scheduler.timesteps					):
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     model(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  int           =     scheduler.step(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

        return sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     DPMSolverSinglestepScheduler(**self.get_scheduler_config()					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     50
        SCREAMING_SNAKE_CASE_				:  int           =     self.dummy_model()
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.dummy_sample_deter
        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

        # make sure that the first t is uneven
        for i, t in enumerate(scheduler.timesteps[3:]					):
              SCREAMING_SNAKE_CASE_				:  List[str]           =     model(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler.step(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

        SCREAMING_SNAKE_CASE_				:  Any           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for timesteps in [25, 50, 100, 999, 1000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     DPMSolverSinglestepScheduler(**self.get_scheduler_config()					)
        SCREAMING_SNAKE_CASE_				:  Any           =     self.full_loop(scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

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

        SCREAMING_SNAKE_CASE_				:  List[str]           =     DEISMultistepScheduler.from_config(scheduler.config					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     DPMSolverMultistepScheduler.from_config(scheduler.config					)
        SCREAMING_SNAKE_CASE_				:  int           =     UniPCMultistepScheduler.from_config(scheduler.config					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     DPMSolverSinglestepScheduler.from_config(scheduler.config					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.full_loop(scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE					)
        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=_SCREAMING_SNAKE_CASE ,					prediction_type=_SCREAMING_SNAKE_CASE ,					sample_max_value=_SCREAMING_SNAKE_CASE ,					algorithm_type='dpmsolver++' ,					solver_order=_SCREAMING_SNAKE_CASE ,					solver_type=_SCREAMING_SNAKE_CASE ,					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """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=_SCREAMING_SNAKE_CASE ,					solver_type=_SCREAMING_SNAKE_CASE ,					prediction_type=_SCREAMING_SNAKE_CASE ,					algorithm_type=_SCREAMING_SNAKE_CASE ,					)
                                SCREAMING_SNAKE_CASE_				:  int           =     self.full_loop(
                                    solver_order=_SCREAMING_SNAKE_CASE ,					solver_type=_SCREAMING_SNAKE_CASE ,					prediction_type=_SCREAMING_SNAKE_CASE ,					algorithm_type=_SCREAMING_SNAKE_CASE ,					)
                                assert not torch.isnan(_SCREAMING_SNAKE_CASE					).any(), "Samples have nan numbers"

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE					)
        self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        self.check_over_configs(lambda_min_clipped=-float('inf'					)					)
        self.check_over_configs(lambda_min_clipped=-5.1					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE					)
        self.check_over_configs(variance_type='learned_range'					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.full_loop()
        SCREAMING_SNAKE_CASE_				:  List[str]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.full_loop(use_karras_sigmas=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Any           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.full_loop(prediction_type='v_prediction'					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.full_loop(prediction_type='v_prediction' ,					use_karras_sigmas=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

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



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  str           =     self.get_scheduler_config(thresholding=_SCREAMING_SNAKE_CASE ,					dynamic_thresholding_ratio=0					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     10
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.dummy_model()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.dummy_sample_deter.half()
        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

        for i, t in enumerate(scheduler.timesteps					):
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     model(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

        assert sample.dtype == torch.floataa









import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__)
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      str       =  field(default='''automatic-speech-recognition'''   ,     metadata={'''include_in_asdict_even_if_is_default''': True})
  SCREAMING_SNAKE_CASE    :      ClassVar[Features]       =  Features({'''audio''': Audio()})
  SCREAMING_SNAKE_CASE    :      ClassVar[Features]       =  Features({'''transcription''': Value('''string''')})
  SCREAMING_SNAKE_CASE    :      str       =  "audio"
  SCREAMING_SNAKE_CASE    :      str       =  "transcription"

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        if self.audio_column not in features:
              raise ValueError(f"Column {self.audio_column} is not present in features."					)
        if not isinstance(features[self.audio_column] ,					_SCREAMING_SNAKE_CASE					):
              raise ValueError(f"Column {self.audio_column} is not an Audio type."					)
        SCREAMING_SNAKE_CASE_				:  int           =     copy.deepcopy(self					)
        SCREAMING_SNAKE_CASE_				:  int           =     self.input_schema.copy()
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     features[self.audio_column]
        SCREAMING_SNAKE_CASE_				:  List[str]           =     input_schema
        return task_template



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return {self.audio_column: "audio", self.transcription_column: "transcription"}






