



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    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									torch.manual_seed(0     )
									UpperCamelCase_			= 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     ):

									'''simple docstring'''


									UpperCamelCase_			= "cpu"
									UpperCamelCase_			= self.dummy_uncond_unet
									UpperCamelCase_			= DDPMScheduler()

									UpperCamelCase_			= DDPMPipeline(unet=snake_case__  ,			scheduler=snake_case__     )
									ddpm.to(snake_case__     )
									ddpm.set_progress_bar_config(disable=snake_case__     )

									UpperCamelCase_			= torch.Generator(device=snake_case__     ).manual_seed(0     )
									UpperCamelCase_			= ddpm(generator=snake_case__  ,			num_inference_steps=2  ,			output_type="numpy"     ).images

									UpperCamelCase_			= torch.Generator(device=snake_case__     ).manual_seed(0     )
									UpperCamelCase_			= ddpm(generator=snake_case__  ,			num_inference_steps=2  ,			output_type="numpy"  ,			return_dict=snake_case__     )[0]

									UpperCamelCase_			= image[0, -3:, -3:, -1]
									UpperCamelCase_			= image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									UpperCamelCase_			= np.array(
									    [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-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     ):

									'''simple docstring'''


									UpperCamelCase_			= self.dummy_uncond_unet
									UpperCamelCase_			= DDPMScheduler(prediction_type="sample"     )

									UpperCamelCase_			= DDPMPipeline(unet=snake_case__  ,			scheduler=snake_case__     )
									ddpm.to(snake_case__     )
									ddpm.set_progress_bar_config(disable=snake_case__     )

									UpperCamelCase_			= torch.manual_seed(0     )
									UpperCamelCase_			= ddpm(generator=snake_case__  ,			num_inference_steps=2  ,			output_type="numpy"     ).images

									UpperCamelCase_			= torch.manual_seed(0     )
									UpperCamelCase_			= ddpm(generator=snake_case__  ,			num_inference_steps=2  ,			output_type="numpy"     )[0]

									UpperCamelCase_			= image[0, -3:, -3:, -1]
									UpperCamelCase_			= image_eps[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									UpperCamelCase_			= 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    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= "google/ddpm-cifar10-32"

									UpperCamelCase_			= UNetaDModel.from_pretrained(snake_case__     )
									UpperCamelCase_			= DDPMScheduler.from_pretrained(snake_case__     )

									UpperCamelCase_			= DDPMPipeline(unet=snake_case__  ,			scheduler=snake_case__     )
									ddpm.to(snake_case__     )
									ddpm.set_progress_bar_config(disable=snake_case__     )

									UpperCamelCase_			= torch.manual_seed(0     )
									UpperCamelCase_			= ddpm(generator=snake_case__  ,			output_type="numpy"     ).images

									UpperCamelCase_			= image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									UpperCamelCase_			= np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]     )
									assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


UpperCAmelCase :				Union[str, Any]							=logging.get_logger(__name__)


class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    """speech-encoder-decoder"""
				lowercase__     =    True

				def __init__(			self  ,			**snake_case__     ):

									'''simple docstring'''


									super().__init__(**snake_case__     )
									if "encoder" not in kwargs or "decoder" not in kwargs:
														raise ValueError(
														    F"""A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"""
														    F""" `decoder` sub-configurations are passed, but only {kwargs}"""     )

									UpperCamelCase_			= kwargs.pop("encoder"     )
									UpperCamelCase_			= encoder_config.pop("model_type"     )
									UpperCamelCase_			= kwargs.pop("decoder"     )
									UpperCamelCase_			= decoder_config.pop("model_type"     )

									UpperCamelCase_			= AutoConfig.for_model(snake_case__  ,			**snake_case__     )
									UpperCamelCase_			= AutoConfig.for_model(snake_case__  ,			**snake_case__     )
									UpperCamelCase_			= True

				@classmethod
				def    _lowerCamelCase       (			cls  ,			snake_case__  ,			snake_case__  ,			**snake_case__     ):

									'''simple docstring'''


									logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"     )
									UpperCamelCase_			= True
									UpperCamelCase_			= True

									return cls(encoder=encoder_config.to_dict()  ,			decoder=decoder_config.to_dict()  ,			**snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= copy.deepcopy(self.__dict__     )
									UpperCamelCase_			= self.encoder.to_dict()
									UpperCamelCase_			= self.decoder.to_dict()
									UpperCamelCase_			= self.__class__.model_type
									return output
