
import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()



class 			UpperCamelCase_			(						unittest.TestCase   ):




							'''simple docstring'''

							@property
							def        lowerCAmelCase__ (				self) ->	Union[str, Any]:
										torch.manual_seed(0)
										UpperCamelCase__    :		Optional[int]     =				UNetaDModel(
										    block_out_channels=(32, 64)     ,						layers_per_block=2     ,						sample_size=32     ,						in_channels=3     ,						out_channels=3     ,						down_block_types=('DownBlock2D', 'AttnDownBlock2D')     ,						up_block_types=('AttnUpBlock2D', 'UpBlock2D')     ,						)
										return model
							def        lowerCAmelCase__ (				self) ->	int:
										UpperCamelCase__    :		Tuple     =				'cpu'
										UpperCamelCase__    :		str     =				self.dummy_uncond_unet
										UpperCamelCase__    :		Optional[int]     =				DDPMScheduler()

										UpperCamelCase__    :		List[Any]     =				DDPMPipeline(unet=UpperCamelCase     ,						scheduler=UpperCamelCase)
										ddpm.to(UpperCamelCase)
										ddpm.set_progress_bar_config(disable=UpperCamelCase)

										UpperCamelCase__    :		int     =				torch.Generator(device=UpperCamelCase).manual_seed(0)
										UpperCamelCase__    :		str     =				ddpm(generator=UpperCamelCase     ,						num_inference_steps=2     ,						output_type='numpy').images

										UpperCamelCase__    :		Any     =				torch.Generator(device=UpperCamelCase).manual_seed(0)
										UpperCamelCase__    :		Optional[int]     =				ddpm(generator=UpperCamelCase     ,						num_inference_steps=2     ,						output_type='numpy'     ,						return_dict=UpperCamelCase)[0]

										UpperCamelCase__    :		List[Any]     =				image[0, -3:, -3:, -1]
										UpperCamelCase__    :		Tuple     =				image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										UpperCamelCase__    :		int     =				np.array(
										    [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.000, 1.1_99E-03, 2.6_48E-04, 5.1_01E-04])

										assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2






							def        lowerCAmelCase__ (				self) ->	str:
										UpperCamelCase__    :		List[Any]     =				self.dummy_uncond_unet
										UpperCamelCase__    :		int     =				DDPMScheduler(prediction_type='sample')

										UpperCamelCase__    :		Optional[Any]     =				DDPMPipeline(unet=UpperCamelCase     ,						scheduler=UpperCamelCase)
										ddpm.to(UpperCamelCase)
										ddpm.set_progress_bar_config(disable=UpperCamelCase)

										UpperCamelCase__    :		Union[str, Any]     =				torch.manual_seed(0)
										UpperCamelCase__    :		Union[str, Any]     =				ddpm(generator=UpperCamelCase     ,						num_inference_steps=2     ,						output_type='numpy').images

										UpperCamelCase__    :		int     =				torch.manual_seed(0)
										UpperCamelCase__    :		Optional[Any]     =				ddpm(generator=UpperCamelCase     ,						num_inference_steps=2     ,						output_type='numpy')[0]

										UpperCamelCase__    :		List[str]     =				image[0, -3:, -3:, -1]
										UpperCamelCase__    :		Union[str, Any]     =				image_eps[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										UpperCamelCase__    :		Optional[Any]     =				1E-2 if torch_device != 'mps' else 3E-2
										assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance



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




							'''simple docstring'''

							def        lowerCAmelCase__ (				self) ->	List[Any]:
										UpperCamelCase__    :		Any     =				'google/ddpm-cifar10-32'

										UpperCamelCase__    :		Optional[Any]     =				UNetaDModel.from_pretrained(UpperCamelCase)
										UpperCamelCase__    :		List[str]     =				DDPMScheduler.from_pretrained(UpperCamelCase)

										UpperCamelCase__    :		Optional[Any]     =				DDPMPipeline(unet=UpperCamelCase     ,						scheduler=UpperCamelCase)
										ddpm.to(UpperCamelCase)
										ddpm.set_progress_bar_config(disable=UpperCamelCase)

										UpperCamelCase__    :		Union[str, Any]     =				torch.manual_seed(0)
										UpperCamelCase__    :		int     =				ddpm(generator=UpperCamelCase     ,						output_type='numpy').images

										UpperCamelCase__    :		Dict     =				image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										UpperCamelCase__    :		Tuple     =				np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385])
										assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2





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__				:  Tuple       					=				[
    '''AutomaticSpeechRecognition''',
    '''AudioClassification''',
    '''ImageClassification''',
    '''LanguageModeling''',
    '''QuestionAnsweringExtractive''',
    '''Summarization''',
    '''TaskTemplate''',
    '''TextClassification''',
]

UpperCAmelCase__				:  Dict       					=				get_logger(__name__)


UpperCAmelCase__				:  Dict       					=				{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     Optional[TaskTemplate]:
			UpperCamelCase__    :		Any     =				task_template_dict.get('task' )
			if task_name is None:
						logger.warning(F"""Couldn't find template for task '{task_name}'. Available templates: {list(__SCREAMING_SNAKE_CASE )}""" )
						return None
			UpperCamelCase__    :		Any     =				NAMEaTEMPLATE.get(__SCREAMING_SNAKE_CASE )
			return template.from_dict(__SCREAMING_SNAKE_CASE )



