
"""simple docstring"""
import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Union[str, Any]	=       (DDIMScheduler,)
    __lowercase				:			List[str]	=       (("""eta""", 0.0), ("""num_inference_steps""", 5_0))
    def        lowerCamelCase__						(     self    ,     **snake_case_  ):
      _snake_case  :    Tuple            = {
          "num_train_timesteps": 10_00,
          "beta_start": 0.0001,
          "beta_end": 0.02,
          "beta_schedule": "linear",
          "clip_sample": True,
      }

      config.update(**snake_case_  )
      return config
    def        lowerCamelCase__						(     self    ,     **snake_case_  ):
      _snake_case  :    Optional[int]            = self.scheduler_classes[0]
      _snake_case  :    Dict            = self.get_scheduler_config(**snake_case_  )
      _snake_case  :    Optional[int]            = scheduler_class(**snake_case_  )

      _snake_case      ,       _snake_case  :    List[Any]            = 10, 0.0

      _snake_case  :    List[Any]            = self.dummy_model()
      _snake_case  :    Tuple            = self.dummy_sample_deter

      scheduler.set_timesteps(snake_case_  )

      for t in scheduler.timesteps:
        _snake_case  :    Tuple            = model(snake_case_    ,     snake_case_  )
        _snake_case  :    Optional[Any]            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ).prev_sample

      return sample
    def        lowerCamelCase__						(     self  ):
      for timesteps in [1_00, 5_00, 10_00]:
        self.check_over_configs(num_train_timesteps=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=snake_case_  )

      _snake_case  :    Optional[int]            = self.scheduler_classes[0]
      _snake_case  :    Optional[Any]            = self.get_scheduler_config(steps_offset=1  )
      _snake_case  :    Tuple            = scheduler_class(**snake_case_  )
      scheduler.set_timesteps(5  )
      assert torch.equal(scheduler.timesteps    ,     torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]  )  )
    def        lowerCamelCase__						(     self  ):
      for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,     [0.002, 0.02, 0.2, 2]  ):
        self.check_over_configs(beta_start=snake_case_    ,     beta_end=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      self.check_over_configs(thresholding=snake_case_  )
      for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(
              thresholding=snake_case_    ,     prediction_type=snake_case_    ,     sample_max_value=snake_case_    ,     )
    def        lowerCamelCase__						(     self  ):
      for t in [1, 10, 49]:
        self.check_over_forward(time_step=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for t, num_inference_steps in zip([1, 10, 50]    ,     [10, 50, 5_00]  ):
        self.check_over_forward(time_step=snake_case_    ,     num_inference_steps=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for t, eta in zip([1, 10, 49]    ,     [0.0, 0.5, 1.0]  ):
        self.check_over_forward(time_step=snake_case_    ,     eta=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = self.scheduler_classes[0]
      _snake_case  :    Optional[Any]            = self.get_scheduler_config()
      _snake_case  :    Optional[Any]            = scheduler_class(**snake_case_  )

      assert torch.sum(torch.abs(scheduler._get_variance(0    ,     0  ) - 0.0  )  ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_20    ,     4_00  ) - 0.14771  )  ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,     9_60  ) - 0.32460  )  ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(0    ,     0  ) - 0.0  )  ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_87    ,     4_86  ) - 0.00979  )  ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,     9_98  ) - 0.02  )  ) < 1E-5
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = self.full_loop()

      _snake_case  :    Union[str, Any]            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    Tuple            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_sum.item() - 172.0067  ) < 1E-2
      assert abs(result_mean.item() - 0.223967  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = self.full_loop(prediction_type="v_prediction"  )

      _snake_case  :    Optional[Any]            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    Optional[int]            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_sum.item() - 52.5302  ) < 1E-2
      assert abs(result_mean.item() - 0.0684  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      # We specify different beta, so that the first alpha is 0.99
      _snake_case  :    Tuple            = self.full_loop(set_alpha_to_one=snake_case_    ,     beta_start=0.01  )
      _snake_case  :    str            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    Union[str, Any]            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_sum.item() - 149.8295  ) < 1E-2
      assert abs(result_mean.item() - 0.1951  ) < 1E-3


    def        lowerCamelCase__						(     self  ):
      # We specify different beta, so that the first alpha is 0.99
      _snake_case  :    Dict            = self.full_loop(set_alpha_to_one=snake_case_    ,     beta_start=0.01  )
      _snake_case  :    Tuple            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    Dict            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_sum.item() - 149.0784  ) < 1E-2
      assert abs(result_mean.item() - 0.1941  ) < 1E-3


'''simple docstring'''

from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


_lowerCamelCase :				int														= [
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

_lowerCamelCase :				int														= get_logger(__name__)


_lowerCamelCase :				List[str]														= {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def  _lowerCAmelCase				(				__a      )  ->      Optional[TaskTemplate]:
     '''simple docstring'''







     _UpperCamelCase  :List[str]       =task_template_dict.get("""task"""      )
     if task_name is None:
          logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(__a      )}'''      )
          return None
     _UpperCamelCase  :List[str]       =NAMEaTEMPLATE.get(__a      )
     return template.from_dict(__a      )