


import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference







@slow
@require_torch_gpu
class     lowerCamelCase__		(							unittest.TestCase ):






			"""simple docstring"""






			def 			snake_case__      (							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    'runwayml/stable-diffusion-v1-5'
						UpperCamelCase__   =    TextToVideoZeroPipeline.from_pretrained(_snake_case ,  torch_dtype=torch.floataa       ).to("cuda"       )
						UpperCamelCase__   =    DDIMScheduler.from_config(pipe.scheduler.config       )
						UpperCamelCase__   =    torch.Generator(device="cuda"       ).manual_seed(0       )

						UpperCamelCase__   =    'A bear is playing a guitar on Times Square'
						UpperCamelCase__   =    pipe(prompt=_snake_case ,  generator=_snake_case       ).images

						UpperCamelCase__   =    load_pt(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt"       )

						assert_mean_pixel_difference(_snake_case ,  _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


__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					)


