
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Any           =			(DEISMultistepScheduler,)
      _UpperCAmelCase				:    Optional[Any]           =			(("""num_inference_steps""", 25),)
      def 						UpperCamelCase__							(      self    ,  **__magic_name__      ):
       lowerCamelCase		:	str							=      {
           """num_train_timesteps""": 1_0_0_0,
           """beta_start""": 0.0_001,
           """beta_end""": 0.02,
           """beta_schedule""": """linear""",
           """solver_order""": 2,
       }

       config.update(**__magic_name__      )
       return config
      def 						UpperCamelCase__							(      self    ,  __magic_name__=0    ,  **__magic_name__      ):
       lowerCamelCase		:	str							=      dict(self.forward_default_kwargs      )
       lowerCamelCase		:	Any							=      kwargs.pop("""num_inference_steps"""    ,  __magic_name__      )
       lowerCamelCase		:	List[str]							=      self.dummy_sample
       lowerCamelCase		:	Dict							=      0.1 * sample
       lowerCamelCase		:	List[Any]							=      [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

         assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
      def 						UpperCamelCase__							(      self      ):
       pass
      def 						UpperCamelCase__							(      self    ,  __magic_name__=0    ,  **__magic_name__      ):
       lowerCamelCase		:	Union[str, Any]							=      dict(self.forward_default_kwargs      )
       lowerCamelCase		:	Optional[Any]							=      kwargs.pop("""num_inference_steps"""    ,  __magic_name__      )
       lowerCamelCase		:	Union[str, Any]							=      self.dummy_sample
       lowerCamelCase		:	Tuple							=      0.1 * sample
       lowerCamelCase		:	Optional[Any]							=      [residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
        lowerCamelCase		:	List[str]							=      self.get_scheduler_config()
        lowerCamelCase		:	Dict							=      scheduler_class(**__magic_name__      )
        scheduler.set_timesteps(__magic_name__      )

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

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

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

        lowerCamelCase		:	int							=      scheduler.step(__magic_name__    ,  __magic_name__    ,  __magic_name__    ,  **__magic_name__      ).prev_sample
        lowerCamelCase		:	Optional[Any]							=      new_scheduler.step(__magic_name__    ,  __magic_name__    ,  __magic_name__    ,  **__magic_name__      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
      def 						UpperCamelCase__							(      self    ,  __magic_name__=None    ,  **__magic_name__      ):
       if scheduler is None:
        lowerCamelCase		:	List[Any]							=      self.scheduler_classes[0]
        lowerCamelCase		:	str							=      self.get_scheduler_config(**__magic_name__      )
        lowerCamelCase		:	List[Any]							=      scheduler_class(**__magic_name__      )

       lowerCamelCase		:	Optional[Any]							=      self.scheduler_classes[0]
       lowerCamelCase		:	str							=      self.get_scheduler_config(**__magic_name__      )
       lowerCamelCase		:	Tuple							=      scheduler_class(**__magic_name__      )

       lowerCamelCase		:	Dict							=      1_0
       lowerCamelCase		:	Optional[int]							=      self.dummy_model()
       lowerCamelCase		:	Optional[Any]							=      self.dummy_sample_deter
       scheduler.set_timesteps(__magic_name__      )

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

       return sample
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	int							=      dict(self.forward_default_kwargs      )

       lowerCamelCase		:	Optional[Any]							=      kwargs.pop("""num_inference_steps"""    ,  __magic_name__      )

       for scheduler_class in self.scheduler_classes:
        lowerCamelCase		:	List[str]							=      self.get_scheduler_config()
        lowerCamelCase		:	Union[str, Any]							=      scheduler_class(**__magic_name__      )

        lowerCamelCase		:	List[Any]							=      self.dummy_sample
        lowerCamelCase		:	str							=      0.1 * sample

        if num_inference_steps is not None and hasattr(__magic_name__    ,  """set_timesteps"""      ):
         scheduler.set_timesteps(__magic_name__      )
        elif num_inference_steps is not None and not hasattr(__magic_name__    ,  """set_timesteps"""      ):
         lowerCamelCase		:	Union[str, Any]							=      num_inference_steps

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

        lowerCamelCase		:	Dict							=      scheduler.timesteps[5]
        lowerCamelCase		:	Union[str, Any]							=      scheduler.timesteps[6]

        lowerCamelCase		:	Union[str, Any]							=      scheduler.step(__magic_name__    ,  __magic_name__    ,  __magic_name__    ,  **__magic_name__      ).prev_sample
        lowerCamelCase		:	List[str]							=      scheduler.step(__magic_name__    ,  __magic_name__    ,  __magic_name__    ,  **__magic_name__      ).prev_sample

        self.assertEqual(output_a.shape    ,  sample.shape      )
        self.assertEqual(output_a.shape    ,  output_a.shape      )
      def 						UpperCamelCase__							(      self      ):
       # make sure that iterating over schedulers with same config names gives same results
       # for defaults
       lowerCamelCase		:	Union[str, Any]							=      DEISMultistepScheduler(**self.get_scheduler_config()      )
       lowerCamelCase		:	Dict							=      self.full_loop(scheduler=__magic_name__      )
       lowerCamelCase		:	Union[str, Any]							=      torch.mean(torch.abs(__magic_name__      )      )

       assert abs(result_mean.item() - 0.23_916      ) < 1e-3

       lowerCamelCase		:	str							=      DPMSolverSinglestepScheduler.from_config(scheduler.config      )
       lowerCamelCase		:	Dict							=      DPMSolverMultistepScheduler.from_config(scheduler.config      )
       lowerCamelCase		:	Union[str, Any]							=      UniPCMultistepScheduler.from_config(scheduler.config      )
       lowerCamelCase		:	Union[str, Any]							=      DEISMultistepScheduler.from_config(scheduler.config      )

       lowerCamelCase		:	List[Any]							=      self.full_loop(scheduler=__magic_name__      )
       lowerCamelCase		:	Dict							=      torch.mean(torch.abs(__magic_name__      )      )

       assert abs(result_mean.item() - 0.23_916      ) < 1e-3
      def 						UpperCamelCase__							(      self      ):
       for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       self.check_over_configs(thresholding=__magic_name__      )
       for order in [1, 2, 3]:
        for solver_type in ["logrho"]:
         for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "sample"]:
           self.check_over_configs(
               thresholding=__magic_name__    ,  prediction_type=__magic_name__    ,  sample_max_value=__magic_name__    ,  algorithm_type="""deis"""    ,  solver_order=__magic_name__    ,  solver_type=__magic_name__    ,  )
      def 						UpperCamelCase__							(      self      ):
       for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       for algorithm_type in ["deis"]:
        for solver_type in ["logrho"]:
         for order in [1, 2, 3]:
          for prediction_type in ["epsilon", "sample"]:
           self.check_over_configs(
               solver_order=__magic_name__    ,  solver_type=__magic_name__    ,  prediction_type=__magic_name__    ,  algorithm_type=__magic_name__    ,  )
           lowerCamelCase		:	Union[str, Any]							=      self.full_loop(
               solver_order=__magic_name__    ,  solver_type=__magic_name__    ,  prediction_type=__magic_name__    ,  algorithm_type=__magic_name__    ,  )
           assert not torch.isnan(__magic_name__      ).any(), "Samples have nan numbers"
      def 						UpperCamelCase__							(      self      ):
       self.check_over_configs(lower_order_final=__magic_name__      )
       self.check_over_configs(lower_order_final=__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       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=__magic_name__    ,  time_step=0      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Tuple							=      self.full_loop()
       lowerCamelCase		:	Union[str, Any]							=      torch.mean(torch.abs(__magic_name__      )      )

       assert abs(result_mean.item() - 0.23_916      ) < 1e-3
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[Any]							=      self.full_loop(prediction_type="""v_prediction"""      )
       lowerCamelCase		:	Tuple							=      torch.mean(torch.abs(__magic_name__      )      )

       assert abs(result_mean.item() - 0.091      ) < 1e-3






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Tuple							=      self.scheduler_classes[0]
       lowerCamelCase		:	str							=      self.get_scheduler_config(thresholding=__magic_name__    ,  dynamic_thresholding_ratio=0      )
       lowerCamelCase		:	List[Any]							=      scheduler_class(**__magic_name__      )

       lowerCamelCase		:	Dict							=      1_0
       lowerCamelCase		:	Optional[int]							=      self.dummy_model()
       lowerCamelCase		:	List[Any]							=      self.dummy_sample_deter.half()
       scheduler.set_timesteps(__magic_name__      )

       for i, t in enumerate(scheduler.timesteps      ):
        lowerCamelCase		:	Optional[int]							=      model(__magic_name__    ,  __magic_name__      )
        lowerCamelCase		:	Union[str, Any]							=      scheduler.step(__magic_name__    ,  __magic_name__    ,  __magic_name__      ).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=__SCREAMING_SNAKE_CASE)
class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    str           =			field(default="""automatic-speech-recognition"""  ,   metadata={"""include_in_asdict_even_if_is_default""": True})
      _UpperCAmelCase				:    ClassVar[Features]           =			Features({"""audio""": Audio()})
      _UpperCAmelCase				:    ClassVar[Features]           =			Features({"""transcription""": Value("""string""")})
      _UpperCAmelCase				:    str           =			"audio"
      _UpperCAmelCase				:    str           =			"transcription"
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       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]    ,  __magic_name__      ):
        raise ValueError(F'''Column {self.audio_column} is not an Audio type.'''      )
       lowerCamelCase		:	Optional[Any]							=      copy.deepcopy(self      )
       lowerCamelCase		:	List[Any]							=      self.input_schema.copy()
       lowerCamelCase		:	Tuple							=      features[self.audio_column]
       lowerCamelCase		:	int							=      input_schema
       return task_template






      @property
      def 						UpperCamelCase__							(      self      ):
       return {self.audio_column: "audio", self.transcription_column: "transcription"}




