





import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
	from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


_lowerCamelCase 					=      logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCamelCase 					=      256




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       ["melgan"]


							def __init__(				self     :List[Any]				,					__A     :SpectrogramNotesEncoder				,					__A     :SpectrogramContEncoder				,					__A     :TaFilmDecoder				,					__A     :DDPMScheduler				,					__A     :OnnxRuntimeModel if is_onnx_available() else Any				,					) -> None:



									"""simple docstring"""



									super().__init__()

									# From MELGAN
									SCREAMING_SNAKE_CASE__								=   math.log(1E-5		)  # Matches MelGAN training.
									SCREAMING_SNAKE_CASE__								=   4.0  # Largest value for most examples
									SCREAMING_SNAKE_CASE__								=   128

									self.register_modules(
									    notes_encoder=__A				,					continuous_encoder=__A				,					decoder=__A				,					scheduler=__A				,					melgan=__A				,					)


							def   _snake_case	(				self     :str				,					__A     :List[Any]				,					__A     :Optional[int]=(-1.0, 1.0)				,					__A     :Optional[Any]=False		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   output_range
									if clip:
											SCREAMING_SNAKE_CASE__								=   torch.clip(__A				,					self.min_value				,					self.max_value		)
									# Scale to [0, 1].
									SCREAMING_SNAKE_CASE__								=   (features - self.min_value) / (self.max_value - self.min_value)
									# Scale to [min_out, max_out].
									return zero_one * (max_out - min_out) + min_out


							def   _snake_case	(				self     :Dict				,					__A     :Tuple				,					__A     :str=(-1.0, 1.0)				,					__A     :List[str]=False		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   input_range
									SCREAMING_SNAKE_CASE__								=   torch.clip(__A				,					__A				,					__A		) if clip else outputs
									# Scale to [0, 1].
									SCREAMING_SNAKE_CASE__								=   (outputs - min_out) / (max_out - min_out)
									# Scale to [self.min_value, self.max_value].
									return zero_one * (self.max_value - self.min_value) + self.min_value


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :Any				,					__A     :List[Any]				,					__A     :str		) -> List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   input_tokens > 0
									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.notes_encoder(
									    encoder_input_tokens=__A				,					encoder_inputs_mask=__A		)

									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.continuous_encoder(
									    encoder_inputs=__A				,					encoder_inputs_mask=__A		)

									return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]


							def   _snake_case	(				self     :Any				,					__A     :int				,					__A     :str				,					__A     :Dict		) -> List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   noise_time
									if not torch.is_tensor(__A		):
											SCREAMING_SNAKE_CASE__								=   torch.tensor([timesteps]				,					dtype=torch.long				,					device=input_tokens.device		)
									elif torch.is_tensor(__A		) and len(timesteps.shape		) == 0:
											SCREAMING_SNAKE_CASE__								=   timesteps[None].to(input_tokens.device		)

									# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
									SCREAMING_SNAKE_CASE__								=   timesteps * torch.ones(input_tokens.shape[0]				,					dtype=timesteps.dtype				,					device=timesteps.device		)

									SCREAMING_SNAKE_CASE__								=   self.decoder(
									    encodings_and_masks=__A				,					decoder_input_tokens=__A				,					decoder_noise_time=__A		)
									return logits


							@torch.no_grad()
							def __call__(				self     :Dict				,					__A     :List[List[int]]				,					__A     :Optional[torch.Generator] = None				,					__A     :int = 100				,					__A     :bool = True				,					__A     :str = "numpy"				,					__A     :Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,					__A     :int = 1				,					) -> Union[AudioPipelineOutput, Tuple]:



									"""simple docstring"""



									if (callback_steps is None) or (
									    callback_steps is not None and (not isinstance(__A				,					__A		) or callback_steps <= 0)
									):
											raise ValueError(
											    f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
											    f''' {type(__A		)}.'''		)

									SCREAMING_SNAKE_CASE__								=   np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]				,					dtype=np.floataa		)
									SCREAMING_SNAKE_CASE__								=   np.zeros([1, 0, self.n_dims]				,					np.floataa		)
									SCREAMING_SNAKE_CASE__								=   torch.ones((1, TARGET_FEATURE_LENGTH)				,					dtype=__A				,					device=self.device		)

									for i, encoder_input_tokens in enumerate(__A		):
											if i == 0:
													SCREAMING_SNAKE_CASE__								=   torch.from_numpy(pred_mel[:1].copy()		).to(
													    device=self.device				,					dtype=self.decoder.dtype		)
													# The first chunk has no previous context.
													SCREAMING_SNAKE_CASE__								=   torch.zeros((1, TARGET_FEATURE_LENGTH)				,					dtype=__A				,					device=self.device		)
											else:
													# The full song pipeline does not feed in a context feature, so the mask
													# will be all 0s after the feature converter. Because we know we're
													# feeding in a full context chunk from the previous prediction, set it
													# to all 1s.
													SCREAMING_SNAKE_CASE__								=   ones

											SCREAMING_SNAKE_CASE__								=   self.scale_features(
											    __A				,					output_range=[-1.0, 1.0]				,					clip=__A		)

											SCREAMING_SNAKE_CASE__								=   self.encode(
											    input_tokens=torch.IntTensor([encoder_input_tokens]		).to(device=self.device		)				,					continuous_inputs=__A				,					continuous_mask=__A				,					)

											# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
											SCREAMING_SNAKE_CASE__								=   randn_tensor(
											    shape=encoder_continuous_inputs.shape				,					generator=__A				,					device=self.device				,					dtype=self.decoder.dtype				,					)

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

											# Denoising diffusion loop
											for j, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
													SCREAMING_SNAKE_CASE__								=   self.decode(
													    encodings_and_masks=__A				,					input_tokens=__A				,					noise_time=t / self.scheduler.config.num_train_timesteps				,					)

													# Compute previous output: x_t -> x_t-1
													SCREAMING_SNAKE_CASE__								=   self.scheduler.step(__A				,					__A				,					__A				,					generator=__A		).prev_sample

											SCREAMING_SNAKE_CASE__								=   self.scale_to_features(__A				,					input_range=[-1.0, 1.0]		)
											SCREAMING_SNAKE_CASE__								=   mel[:1]
											SCREAMING_SNAKE_CASE__								=   mel.cpu().float().numpy()

											SCREAMING_SNAKE_CASE__								=   np.concatenate([full_pred_mel, pred_mel[:1]]				,					axis=1		)

											# call the callback, if provided
											if callback is not None and i % callback_steps == 0:
													callback(__A				,					__A		)

											logger.info("""Generated segment"""				,					__A		)

									if output_type == "numpy" and not is_onnx_available():
											raise ValueError(
											    """Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'."""		)
									elif output_type == "numpy" and self.melgan is None:
											raise ValueError(
											    """Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'."""		)

									if output_type == "numpy":
											SCREAMING_SNAKE_CASE__								=   self.melgan(input_features=full_pred_mel.astype(np.floataa		)		)
									else:
											SCREAMING_SNAKE_CASE__								=   full_pred_mel

									if not return_dict:
											return (output,)

									return AudioPipelineOutput(audios=__A		)






import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

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


enable_full_determinism()





class 				a			(      __lowercase   ,unittest.TestCase			):
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     ConsistencyModelPipeline
				SCREAMING_SNAKE_CASE__     :   Any		      =     UNCONDITIONAL_IMAGE_GENERATION_PARAMS
				SCREAMING_SNAKE_CASE__     :   str		      =     UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

				# Override required_optional_params to remove num_images_per_prompt
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     frozenset(
				    [
				        '''num_inference_steps''',
				        '''generator''',
				        '''latents''',
				        '''output_type''',
				        '''return_dict''',
				        '''callback''',
				        '''callback_steps''',
				    ]			)






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   UNetaDModel.from_pretrained(
										    '''diffusers/consistency-models-test'''			, subfolder='''test_unet'''			, )
										return unet






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   UNetaDModel.from_pretrained(
										    '''diffusers/consistency-models-test'''			, subfolder='''test_unet_class_cond'''			, )
										return unet






				def 			snake_case_    (  self			, _lowerCAmelCase=False					):

										"""simple docstring"""




										if class_cond:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   self.dummy_cond_unet
										else:
																__SCREAMING_SNAKE_CASE:				Tuple           =   self.dummy_uncond_unet

										# Default to CM multistep sampler
										__SCREAMING_SNAKE_CASE:				Tuple           =   CMStochasticIterativeScheduler(
										    num_train_timesteps=40			, sigma_min=0.002			, sigma_max=80.0			, )

										__SCREAMING_SNAKE_CASE:				List[str]           =   {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										}

										return components






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=0					):

										"""simple docstring"""




										if str(_lowerCAmelCase					).startswith('''mps'''					):
																__SCREAMING_SNAKE_CASE:				List[str]           =   torch.manual_seed(_lowerCAmelCase					)
										else:
																__SCREAMING_SNAKE_CASE:				List[str]           =   torch.Generator(device=_lowerCAmelCase					).manual_seed(_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   {
										    '''batch_size''': 1,
										    '''num_inference_steps''': None,
										    '''timesteps''': [22, 0],
										    '''generator''': generator,
										    '''output_type''': '''np''',
										}

										return inputs






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.get_dummy_components()
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   ConsistencyModelPipeline(**_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Any           =   pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.get_dummy_inputs(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Any           =   pipe(**_lowerCAmelCase					).images
										assert image.shape == (1, 32, 32, 3)

										__SCREAMING_SNAKE_CASE:				int           =   image[0, -3:, -3:, -1]
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										__SCREAMING_SNAKE_CASE:				str           =   self.get_dummy_components(class_cond=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Any           =   ConsistencyModelPipeline(**_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Any           =   pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Dict           =   self.get_dummy_inputs(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Dict           =   0
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   pipe(**_lowerCAmelCase					).images
										assert image.shape == (1, 32, 32, 3)

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   image[0, -3:, -3:, -1]
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.get_dummy_components()
										__SCREAMING_SNAKE_CASE:				Tuple           =   ConsistencyModelPipeline(**_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				List[Any]           =   self.get_dummy_inputs(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				int           =   1
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   None
										__SCREAMING_SNAKE_CASE:				List[str]           =   pipe(**_lowerCAmelCase					).images
										assert image.shape == (1, 32, 32, 3)

										__SCREAMING_SNAKE_CASE:				Tuple           =   image[0, -3:, -3:, -1]
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.get_dummy_components(class_cond=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   ConsistencyModelPipeline(**_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Tuple           =   self.get_dummy_inputs(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Any           =   1
										__SCREAMING_SNAKE_CASE:				List[Any]           =   None
										__SCREAMING_SNAKE_CASE:				Tuple           =   0
										__SCREAMING_SNAKE_CASE:				List[str]           =   pipe(**_lowerCAmelCase					).images
										assert image.shape == (1, 32, 32, 3)

										__SCREAMING_SNAKE_CASE:				List[Any]           =   image[0, -3:, -3:, -1]
										__SCREAMING_SNAKE_CASE:				Any           =   np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3





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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






				def 			snake_case_    (  self			, _lowerCAmelCase=0			, _lowerCAmelCase=False			, _lowerCAmelCase="cpu"			, _lowerCAmelCase=torch.floataa			, _lowerCAmelCase=(1, 3, 64, 64)					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   torch.manual_seed(_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Dict           =   {
										    '''num_inference_steps''': None,
										    '''timesteps''': [22, 0],
										    '''class_labels''': 0,
										    '''generator''': generator,
										    '''output_type''': '''np''',
										}

										if get_fixed_latents:
																__SCREAMING_SNAKE_CASE:				Dict           =   self.get_fixed_latents(seed=_lowerCAmelCase			, device=_lowerCAmelCase			, dtype=_lowerCAmelCase			, shape=_lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   latents

										return inputs






				def 			snake_case_    (  self			, _lowerCAmelCase=0			, _lowerCAmelCase="cpu"			, _lowerCAmelCase=torch.floataa			, _lowerCAmelCase=(1, 3, 64, 64)					):

										"""simple docstring"""




										if type(_lowerCAmelCase					) == str:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   torch.device(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Dict           =   torch.Generator(device=_lowerCAmelCase					).manual_seed(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				int           =   randn_tensor(_lowerCAmelCase			, generator=_lowerCAmelCase			, device=_lowerCAmelCase			, dtype=_lowerCAmelCase					)
										return latents






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   UNetaDModel.from_pretrained('''diffusers/consistency_models'''			, subfolder='''diffusers_cd_imagenet64_l2'''					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   CMStochasticIterativeScheduler(
										    num_train_timesteps=40			, sigma_min=0.002			, sigma_max=80.0			, )
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   ConsistencyModelPipeline(unet=_lowerCAmelCase			, scheduler=_lowerCAmelCase					)
										pipe.to(torch_device=_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				int           =   self.get_inputs()
										__SCREAMING_SNAKE_CASE:				List[Any]           =   pipe(**_lowerCAmelCase					).images
										assert image.shape == (1, 64, 64, 3)

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

										__SCREAMING_SNAKE_CASE:				Any           =   np.array([0.0888, 0.0881, 0.0666, 0.0479, 0.0292, 0.0195, 0.0201, 0.0163, 0.0254]					)

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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   UNetaDModel.from_pretrained('''diffusers/consistency_models'''			, subfolder='''diffusers_cd_imagenet64_l2'''					)
										__SCREAMING_SNAKE_CASE:				str           =   CMStochasticIterativeScheduler(
										    num_train_timesteps=40			, sigma_min=0.002			, sigma_max=80.0			, )
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   ConsistencyModelPipeline(unet=_lowerCAmelCase			, scheduler=_lowerCAmelCase					)
										pipe.to(torch_device=_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				List[str]           =   self.get_inputs()
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   1
										__SCREAMING_SNAKE_CASE:				List[Any]           =   None
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   pipe(**_lowerCAmelCase					).images
										assert image.shape == (1, 64, 64, 3)

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

										__SCREAMING_SNAKE_CASE:				Any           =   np.array([0.0340, 0.0152, 0.0063, 0.0267, 0.0221, 0.0107, 0.0416, 0.0186, 0.0217]					)

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






				@require_torch_a
				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   UNetaDModel.from_pretrained('''diffusers/consistency_models'''			, subfolder='''diffusers_cd_imagenet64_l2'''					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   CMStochasticIterativeScheduler(
										    num_train_timesteps=40			, sigma_min=0.002			, sigma_max=80.0			, )
										__SCREAMING_SNAKE_CASE:				List[Any]           =   ConsistencyModelPipeline(unet=_lowerCAmelCase			, scheduler=_lowerCAmelCase					)
										pipe.to(torch_device=_lowerCAmelCase			, torch_dtype=torch.floataa					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				int           =   self.get_inputs(get_fixed_latents=_lowerCAmelCase			, device=_lowerCAmelCase					)
										# Ensure usage of flash attention in torch 2.0
										with sdp_kernel(enable_flash=_lowerCAmelCase			, enable_math=_lowerCAmelCase			, enable_mem_efficient=_lowerCAmelCase					):
																__SCREAMING_SNAKE_CASE:				Dict           =   pipe(**_lowerCAmelCase					).images
										assert image.shape == (1, 64, 64, 3)

										__SCREAMING_SNAKE_CASE:				List[Any]           =   image[0, -3:, -3:, -1]

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   np.array([0.1875, 0.1428, 0.1289, 0.2151, 0.2092, 0.1477, 0.1877, 0.1641, 0.1353]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3






				@require_torch_a
				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   UNetaDModel.from_pretrained('''diffusers/consistency_models'''			, subfolder='''diffusers_cd_imagenet64_l2'''					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   CMStochasticIterativeScheduler(
										    num_train_timesteps=40			, sigma_min=0.002			, sigma_max=80.0			, )
										__SCREAMING_SNAKE_CASE:				List[Any]           =   ConsistencyModelPipeline(unet=_lowerCAmelCase			, scheduler=_lowerCAmelCase					)
										pipe.to(torch_device=_lowerCAmelCase			, torch_dtype=torch.floataa					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				int           =   self.get_inputs(get_fixed_latents=_lowerCAmelCase			, device=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   1
										__SCREAMING_SNAKE_CASE:				List[Any]           =   None
										# Ensure usage of flash attention in torch 2.0
										with sdp_kernel(enable_flash=_lowerCAmelCase			, enable_math=_lowerCAmelCase			, enable_mem_efficient=_lowerCAmelCase					):
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   pipe(**_lowerCAmelCase					).images
										assert image.shape == (1, 64, 64, 3)

										__SCREAMING_SNAKE_CASE:				Tuple           =   image[0, -3:, -3:, -1]

										__SCREAMING_SNAKE_CASE:				Tuple           =   np.array([0.1663, 0.1948, 0.2275, 0.1680, 0.1204, 0.1245, 0.1858, 0.1338, 0.2095]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3



