
"""simple docstring"""






from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class  lowercase				(   lowercase__		):






			def __init__(self     :  Dict			,SCREAMING_SNAKE_CASE_     :  Dict			,SCREAMING_SNAKE_CASE_     :  List[Any] )					->  Any:





				"""simple docstring"""


				super().__init__()
				self.register_modules(unet=SCREAMING_SNAKE_CASE_			,scheduler=SCREAMING_SNAKE_CASE_ )






			@torch.no_grad()
			def __call__(self     :  Optional[Any]			,SCREAMING_SNAKE_CASE_     :  int = 1			,SCREAMING_SNAKE_CASE_     :  Optional[torch.Generator] = None			,SCREAMING_SNAKE_CASE_     :  int = 50			,SCREAMING_SNAKE_CASE_     :  Optional[str] = "pil"			,SCREAMING_SNAKE_CASE_     :  bool = True			,**SCREAMING_SNAKE_CASE_     :  Optional[int]			,)					->  Union[ImagePipelineOutput, Tuple]:





				"""simple docstring"""


				lowerCAmelCase				       =     torch.randn(
				    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,generator=SCREAMING_SNAKE_CASE_			,)
				lowerCAmelCase				       =     image.to(self.device )

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

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

					# 2. predict previous mean of image x_t-1 and add variance depending on eta
					# eta corresponds to η in paper and should be between [0, 1]
					# do x_t -> x_t-1
					lowerCAmelCase				       =     self.scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ ).prev_sample

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

				if not return_dict:
					return (image,), "This is a local test"

				return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_ ), "This is a local test"




"""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


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 __magic_name__  (							_lowerCamelCase:		dict )				->	Optional[TaskTemplate]:

	'''simple docstring'''




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


