
import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 			A_  (     __lowerCamelCase ,      unittest.TestCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	Dict									=			DDIMPipeline
	_UpperCamelCase	:	int									=			UNCONDITIONAL_IMAGE_GENERATION_PARAMS
	_UpperCamelCase	:	Any									=			PipelineTesterMixin.required_optional_params - {
	    """num_images_per_prompt""",
	    """latents""",
	    """callback""",
	    """callback_steps""",
	}
	_UpperCamelCase	:	List[Any]									=			UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
	_UpperCamelCase	:	Any									=			False





	def       SCREAMING_SNAKE_CASE__		(			self			):
		torch.manual_seed(0			)
		lowercase	    =       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')						,       )
		lowercase	    =       DDIMScheduler()
		lowercase	    =       {'unet': unet, 'scheduler': scheduler}
		return components





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case=0			):
		if str(snake_case			).startswith('mps'			):
			lowercase	    =       torch.manual_seed(snake_case			)
		else:
			lowercase	    =       torch.Generator(device=snake_case			).manual_seed(snake_case			)
		lowercase	    =       {
		    'batch_size': 1,
		    'generator': generator,
		    'num_inference_steps': 2,
		    'output_type': 'numpy',
		}
		return inputs





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       'cpu'

		lowercase	    =       self.get_dummy_components()
		lowercase	    =       self.pipeline_class(**snake_case			)
		pipe.to(snake_case			)
		pipe.set_progress_bar_config(disable=snake_case			)

		lowercase	    =       self.get_dummy_inputs(snake_case			)
		lowercase	    =       pipe(**snake_case			).images
		lowercase	    =       image[0, -3:, -3:, -1]

		self.assertEqual(image.shape						,       (1, 32, 32, 3)			)
		lowercase	    =       np.array(
		    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]			)
		lowercase	    =       np.abs(image_slice.flatten() - expected_slice			).max()
		self.assertLessEqual(snake_case						,       1E-3			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		super().test_save_load_local(expected_max_difference=3E-3			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		super().test_save_load_optional_components(expected_max_difference=3E-3			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		super().test_inference_batch_single_identical(expected_max_diff=3E-3			)







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




	'''simple docstring'''


	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       'google/ddpm-cifar10-32'

		lowercase	    =       UNetaDModel.from_pretrained(snake_case			)
		lowercase	    =       DDIMScheduler()

		lowercase	    =       DDIMPipeline(unet=snake_case						,       scheduler=snake_case			)
		ddim.to(snake_case			)
		ddim.set_progress_bar_config(disable=snake_case			)

		lowercase	    =       torch.manual_seed(0			)
		lowercase	    =       ddim(generator=snake_case						,       eta=0.0						,       output_type='numpy'			).images

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

		assert image.shape == (1, 32, 32, 3)
		lowercase	    =       np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453]			)

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       'google/ddpm-ema-bedroom-256'

		lowercase	    =       UNetaDModel.from_pretrained(snake_case			)
		lowercase	    =       DDIMScheduler.from_pretrained(snake_case			)

		lowercase	    =       DDIMPipeline(unet=snake_case						,       scheduler=snake_case			)
		ddpm.to(snake_case			)
		ddpm.set_progress_bar_config(disable=snake_case			)

		lowercase	    =       torch.manual_seed(0			)
		lowercase	    =       ddpm(generator=snake_case						,       output_type='numpy'			).images

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

		assert image.shape == (1, 256, 256, 3)
		lowercase	    =       np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069]			)

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








import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             super().__init__()
             self.register_modules(vqvae=a__   ,				unet=a__   ,				scheduler=a__)



      @torch.no_grad()
      def __call__(		self   ,				a__ = 1   ,				a__ = None   ,				a__ = 0.0   ,				a__ = 50   ,				a__ = "pil"   ,				a__ = True   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		randn_tensor(
                 (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,				generator=a__   ,				)
             _lowerCamelCase				:  Optional[int]						=		latents.to(self.device)

             # scale the initial noise by the standard deviation required by the scheduler
             _lowerCamelCase				:  Tuple						=		latents * self.scheduler.init_noise_sigma

             self.scheduler.set_timesteps(a__)

             # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
             _lowerCamelCase				:  Optional[int]						=		'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())

             _lowerCamelCase				:  Union[str, Any]						=		{}
             if accepts_eta:
                    _lowerCamelCase				:  List[Any]						=		eta

             for t in self.progress_bar(self.scheduler.timesteps):
                    _lowerCamelCase				:  List[Any]						=		self.scheduler.scale_model_input(a__   ,				a__)
                    # predict the noise residual
                    _lowerCamelCase				:  Optional[int]						=		self.unet(a__   ,				a__).sample
                    # compute the previous noisy sample x_t -> x_t-1
                    _lowerCamelCase				:  List[str]						=		self.scheduler.step(a__   ,				a__   ,				a__   ,				**a__).prev_sample

             # decode the image latents with the VAE
             _lowerCamelCase				:  Optional[int]						=		self.vqvae.decode(a__).sample

             _lowerCamelCase				:  Dict						=		(image / 2 + 0.5).clamp(0   ,				1)
             _lowerCamelCase				:  List[str]						=		image.cpu().permute(0   ,				2   ,				3   ,				1).numpy()
             if output_type == "pil":
                    _lowerCamelCase				:  List[Any]						=		self.numpy_to_pil(a__)

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=a__)

