


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class     lowerCamelCase__		(							UpperCAmelCase ):






			"""simple docstring"""






			_UpperCamelCase						:   Dict					=						(EulerAncestralDiscreteScheduler,)
			_UpperCamelCase						:   Optional[Any]					=						10




			def 			snake_case__			(							self ,  **snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    {
						    "num_train_timesteps": 1100,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						}

						config.update(**snake_case       )
						return config




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						for timesteps in [10, 50, 100, 1000]:
									self.check_over_configs(num_train_timesteps=snake_case       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] ,  [0.0002, 0.002, 0.02]       ):
									self.check_over_configs(beta_start=snake_case ,  beta_end=snake_case       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						for schedule in ["linear", "scaled_linear"]:
									self.check_over_configs(beta_schedule=snake_case       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


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




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.scheduler_classes[0]
						UpperCamelCase__   =    self.get_scheduler_config()
						UpperCamelCase__   =    scheduler_class(**snake_case       )

						scheduler.set_timesteps(self.num_inference_steps       )

						UpperCamelCase__   =    torch.manual_seed(0       )

						UpperCamelCase__   =    self.dummy_model()
						UpperCamelCase__   =    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						UpperCamelCase__   =    sample.to(snake_case       )

						for i, t in enumerate(scheduler.timesteps       ):
									UpperCamelCase__   =    scheduler.scale_model_input(snake_case ,  snake_case       )

									UpperCamelCase__   =    model(snake_case ,  snake_case       )

									UpperCamelCase__   =    scheduler.step(snake_case ,  snake_case ,  snake_case ,  generator=snake_case       )
									UpperCamelCase__   =    output.prev_sample

						UpperCamelCase__   =    torch.sum(torch.abs(snake_case       )       )
						UpperCamelCase__   =    torch.mean(torch.abs(snake_case       )       )

						assert abs(result_sum.item() - 152.3192       ) < 1E-2
						assert abs(result_mean.item() - 0.1983       ) < 1E-3




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.scheduler_classes[0]
						UpperCamelCase__   =    self.get_scheduler_config(prediction_type="v_prediction"       )
						UpperCamelCase__   =    scheduler_class(**snake_case       )

						scheduler.set_timesteps(self.num_inference_steps       )

						UpperCamelCase__   =    torch.manual_seed(0       )

						UpperCamelCase__   =    self.dummy_model()
						UpperCamelCase__   =    self.dummy_sample_deter * scheduler.init_noise_sigma
						UpperCamelCase__   =    sample.to(snake_case       )

						for i, t in enumerate(scheduler.timesteps       ):
									UpperCamelCase__   =    scheduler.scale_model_input(snake_case ,  snake_case       )

									UpperCamelCase__   =    model(snake_case ,  snake_case       )

									UpperCamelCase__   =    scheduler.step(snake_case ,  snake_case ,  snake_case ,  generator=snake_case       )
									UpperCamelCase__   =    output.prev_sample

						UpperCamelCase__   =    torch.sum(torch.abs(snake_case       )       )
						UpperCamelCase__   =    torch.mean(torch.abs(snake_case       )       )

						assert abs(result_sum.item() - 108.4439       ) < 1E-2
						assert abs(result_mean.item() - 0.1412       ) < 1E-3







			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.scheduler_classes[0]
						UpperCamelCase__   =    self.get_scheduler_config()
						UpperCamelCase__   =    scheduler_class(**snake_case       )

						scheduler.set_timesteps(self.num_inference_steps ,  device=snake_case       )
						UpperCamelCase__   =    torch.manual_seed(0       )

						UpperCamelCase__   =    self.dummy_model()
						UpperCamelCase__   =    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						UpperCamelCase__   =    sample.to(snake_case       )

						for t in scheduler.timesteps:
									UpperCamelCase__   =    scheduler.scale_model_input(snake_case ,  snake_case       )

									UpperCamelCase__   =    model(snake_case ,  snake_case       )

									UpperCamelCase__   =    scheduler.step(snake_case ,  snake_case ,  snake_case ,  generator=snake_case       )
									UpperCamelCase__   =    output.prev_sample

						UpperCamelCase__   =    torch.sum(torch.abs(snake_case       )       )
						UpperCamelCase__   =    torch.mean(torch.abs(snake_case       )       )

						assert abs(result_sum.item() - 152.3192       ) < 1E-2
						assert abs(result_mean.item() - 0.1983       ) < 1E-3






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


__UpperCamelCase      						=    [
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

__UpperCamelCase      						=    get_logger(__name__)


__UpperCamelCase      						=    {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}
def     UpperCamelCase_(			_A				:dict					)->						Optional[TaskTemplate]:
			UpperCamelCase__   =    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__   =    NAMEaTEMPLATE.get(_A					)
			return template.from_dict(_A					)


