

from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 			__magic_name__					(    lowerCAmelCase_   ):





			def __init__(	self      ,						__snake_case      ,						__snake_case      )     ->				Dict:




						'''simple docstring'''
						super().__init__()
						self.register_modules(unet=__snake_case      ,						scheduler=__snake_case      )





			@torch.no_grad()
			def __call__(	self      ,						__snake_case = 1      ,						__snake_case = None      ,						__snake_case = 1000      ,						__snake_case = "pil"      ,						__snake_case = True      ,						)     ->				Union[ImagePipelineOutput, Tuple]:




						'''simple docstring'''
						# Sample gaussian noise to begin loop
						if isinstance(self.unet.config.sample_size      ,						__snake_case      ):
									__a						   =(
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
						else:
									__a						   =(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

						if self.device.type == "mps":
									# randn does not work reproducibly on mps
									__a						   =randn_tensor(__snake_case      ,						generator=__snake_case      )
									__a						   =image.to(self.device      )
						else:
									__a						   =randn_tensor(__snake_case      ,						generator=__snake_case      ,						device=self.device      )

						# set step values
						self.scheduler.set_timesteps(__snake_case      )

						for t in self.progress_bar(self.scheduler.timesteps      ):
									# 1. predict noise model_output
									__a						   =self.unet(__snake_case      ,						__snake_case      ).sample

									# 2. compute previous image: x_t -> x_t-1
									__a						   =self.scheduler.step(__snake_case      ,						__snake_case      ,						__snake_case      ,						generator=__snake_case      ).prev_sample

						__a						   =(image / 2 + 0.5).clamp(0      ,						1      )
						__a						   =image.cpu().permute(0      ,						2      ,						3      ,						1      ).numpy()
						if output_type == "pil":
									__a						   =self.numpy_to_pil(__snake_case      )

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__snake_case      )









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      :    List[Any]						       =    [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

_lowerCAmelCase      :    List[str]						       =    get_logger(__name__)


_lowerCAmelCase      :    Tuple						       =    {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}






def  UpperCamelCase_(							_snake_case     :						dict			):




			"""simple docstring"""






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






