


from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCamelCase			:					List[Any]         =							logging.get_logger(__name__)  # pylint: disable=invalid-name


UpperCamelCase			:					Any         =							'''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''

class      __snake_case				(   _UpperCAmelCase):
					def __init__(						self       :						Optional[int]	,      _lowerCAmelCase       :						UNetaDModel	,      _lowerCAmelCase       :						CMStochasticIterativeScheduler  )							->					None:



											'''simple docstring'''





											super().__init__()

											self.register_modules(
											    unet=_lowerCAmelCase	,      scheduler=_lowerCAmelCase	,      )

											__magic_name__					=      None
					def 	a			(						self       :						Dict	,      _lowerCAmelCase       :						int=0  )							->					List[Any]:



											'''simple docstring'''





											if is_accelerate_available() and is_accelerate_version('>='	,      '0.14.0'  ):
																	from accelerate import cpu_offload
											else:
																	raise ImportError('`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'  )

											__magic_name__					=      torch.device(F'''cuda:{gpu_id}'''  )

											if self.device.type != "cpu":
																	self.to('cpu'	,      silence_dtype_warnings=_lowerCAmelCase  )
																	torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

											for cpu_offloaded_model in [self.unet]:
																	cpu_offload(_lowerCAmelCase	,      _lowerCAmelCase  )

											if self.safety_checker is not None:
																	cpu_offload(self.safety_checker	,      execution_device=_lowerCAmelCase	,      offload_buffers=_lowerCAmelCase  )
					def 	a			(						self       :						Dict	,      _lowerCAmelCase       :						Any=0  )							->					Optional[Any]:



											'''simple docstring'''





											if is_accelerate_available() and is_accelerate_version('>='	,      '0.17.0.dev0'  ):
																	from accelerate import cpu_offload_with_hook
											else:
																	raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'  )

											__magic_name__					=      torch.device(F'''cuda:{gpu_id}'''  )

											if self.device.type != "cpu":
																	self.to('cpu'	,      silence_dtype_warnings=_lowerCAmelCase  )
																	torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

											__magic_name__					=      None
											for cpu_offloaded_model in [self.unet]:
																	__magic_name__		,      __magic_name__					=      cpu_offload_with_hook(_lowerCAmelCase	,      _lowerCAmelCase	,      prev_module_hook=_lowerCAmelCase  )

											if self.safety_checker is not None:
																	__magic_name__		,      __magic_name__					=      cpu_offload_with_hook(self.safety_checker	,      _lowerCAmelCase	,      prev_module_hook=_lowerCAmelCase  )

											# We'll offload the last model manually.
											__magic_name__					=      hook
					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 	a			(						self       :						str  )							->					Optional[int]:



											'''simple docstring'''





											if not hasattr(self.unet	,      '_hf_hook'  ):
																	return self.device
											for module in self.unet.modules():
																	if (
																	    hasattr(_lowerCAmelCase	,      '_hf_hook'  )
																	    and hasattr(module._hf_hook	,      'execution_device'  )
																	    and module._hf_hook.execution_device is not None
																	):
																							return torch.device(module._hf_hook.execution_device  )
											return self.device
					def 	a			(						self       :						Optional[int]	,      _lowerCAmelCase       :						Dict	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						List[Any]	,      _lowerCAmelCase       :						List[str]	,      _lowerCAmelCase       :						Union[str, Any]	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						Any	,      _lowerCAmelCase       :						Dict=None  )							->					List[str]:



											'''simple docstring'''





											__magic_name__					=      (batch_size, num_channels, height, width)
											if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ) and len(_lowerCAmelCase  ) != batch_size:
																	raise ValueError(
																	    F'''You have passed a list of generators of length {len(_lowerCAmelCase  )}, but requested an effective batch'''
																	    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''  )

											if latents is None:
																	__magic_name__					=      randn_tensor(_lowerCAmelCase	,      generator=_lowerCAmelCase	,      device=_lowerCAmelCase	,      dtype=_lowerCAmelCase  )
											else:
																	__magic_name__					=      latents.to(device=_lowerCAmelCase	,      dtype=_lowerCAmelCase  )

											# scale the initial noise by the standard deviation required by the scheduler
											__magic_name__					=      latents * self.scheduler.init_noise_sigma
											return latents
					def 	a			(						self       :						int	,      _lowerCAmelCase       :						torch.FloatTensor	,      _lowerCAmelCase       :						str = "pil"  )							->					str:



											'''simple docstring'''





											if output_type not in ["pt", "np", "pil"]:
																	raise ValueError(
																	    F'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''  )

											# Equivalent to diffusers.VaeImageProcessor.denormalize
											__magic_name__					=      (sample / 2 + 0.5).clamp(0	,      1  )
											if output_type == "pt":
																	return sample

											# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
											__magic_name__					=      sample.cpu().permute(0	,      2	,      3	,      1  ).numpy()
											if output_type == "np":
																	return sample

											# Output_type must be 'pil'
											__magic_name__					=      self.numpy_to_pil(_lowerCAmelCase  )
											return sample
					def 	a			(						self       :						Optional[Any]	,      _lowerCAmelCase       :						str	,      _lowerCAmelCase       :						int	,      _lowerCAmelCase       :						Any=None  )							->					Tuple:



											'''simple docstring'''





											if self.unet.config.num_class_embeds is not None:
																	if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ):
																							__magic_name__					=      torch.tensor(_lowerCAmelCase	,      dtype=torch.int  )
																	elif isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ):
																							assert batch_size == 1, "Batch size must be 1 if classes is an int"
																							__magic_name__					=      torch.tensor([class_labels]	,      dtype=torch.int  )
																	elif class_labels is None:
																							# Randomly generate batch_size class labels
																							# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
																							__magic_name__					=      torch.randint(0	,      self.unet.config.num_class_embeds	,      size=(batch_size,)  )
																	__magic_name__					=      class_labels.to(_lowerCAmelCase  )
											else:
																	__magic_name__					=      None
											return class_labels
					def 	a			(						self       :						str	,      _lowerCAmelCase       :						Any	,      _lowerCAmelCase       :						List[Any]	,      _lowerCAmelCase       :						List[Any]	,      _lowerCAmelCase       :						Dict	,      _lowerCAmelCase       :						str	,      _lowerCAmelCase       :						Any  )							->					List[str]:



											'''simple docstring'''





											if num_inference_steps is None and timesteps is None:
																	raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'  )

											if num_inference_steps is not None and timesteps is not None:
																	logger.warning(
																	    F'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
																	    ' `timesteps` will be used over `num_inference_steps`.'  )

											if latents is not None:
																	__magic_name__					=      (batch_size, 3, img_size, img_size)
																	if latents.shape != expected_shape:
																							raise ValueError(F'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''  )

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







					@torch.no_grad()
					@replace_example_docstring(_lowerCAmelCase  )
					def __call__(						self       :						List[str]	,      _lowerCAmelCase       :						int = 1	,      _lowerCAmelCase       :						Optional[Union[torch.Tensor, List[int], int]] = None	,      _lowerCAmelCase       :						int = 1	,      _lowerCAmelCase       :						List[int] = None	,      _lowerCAmelCase       :						Optional[Union[torch.Generator, List[torch.Generator]]] = None	,      _lowerCAmelCase       :						Optional[torch.FloatTensor] = None	,      _lowerCAmelCase       :						Optional[str] = "pil"	,      _lowerCAmelCase       :						bool = True	,      _lowerCAmelCase       :						Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,      _lowerCAmelCase       :						int = 1	,      )							->					Optional[int]:



											'''simple docstring'''





											__magic_name__					=      self.unet.config.sample_size
											__magic_name__					=      self._execution_device

											# 1. Check inputs
											self.check_inputs(_lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase  )

											# 2. Prepare image latents
											# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
											__magic_name__					=      self.prepare_latents(
											    batch_size=_lowerCAmelCase	,      num_channels=self.unet.config.in_channels	,      height=_lowerCAmelCase	,      width=_lowerCAmelCase	,      dtype=self.unet.dtype	,      device=_lowerCAmelCase	,      generator=_lowerCAmelCase	,      latents=_lowerCAmelCase	,      )

											# 3. Handle class_labels for class-conditional models
											__magic_name__					=      self.prepare_class_labels(_lowerCAmelCase	,      _lowerCAmelCase	,      class_labels=_lowerCAmelCase  )

											# 4. Prepare timesteps
											if timesteps is not None:
																	self.scheduler.set_timesteps(timesteps=_lowerCAmelCase	,      device=_lowerCAmelCase  )
																	__magic_name__					=      self.scheduler.timesteps
																	__magic_name__					=      len(_lowerCAmelCase  )
											else:
																	self.scheduler.set_timesteps(_lowerCAmelCase  )
																	__magic_name__					=      self.scheduler.timesteps

											# 5. Denoising loop
											# Multistep sampling: implements Algorithm 1 in the paper
											with self.progress_bar(total=_lowerCAmelCase  ) as progress_bar:
																	for i, t in enumerate(_lowerCAmelCase  ):
																							__magic_name__					=      self.scheduler.scale_model_input(_lowerCAmelCase	,      _lowerCAmelCase  )
																							__magic_name__					=      self.unet(_lowerCAmelCase	,      _lowerCAmelCase	,      class_labels=_lowerCAmelCase	,      return_dict=_lowerCAmelCase  )[0]

																							__magic_name__					=      self.scheduler.step(_lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase	,      generator=_lowerCAmelCase  )[0]

																							# call the callback, if provided
																							progress_bar.update()
																							if callback is not None and i % callback_steps == 0:
																													callback(_lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase  )

        # 6. Post-process image sample
											__magic_name__					=      self.postprocess_image(_lowerCAmelCase	,      output_type=_lowerCAmelCase  )

											# Offload last model to CPU
											if hasattr(self	,      'final_offload_hook'  ) and self.final_offload_hook is not None:
																	self.final_offload_hook.offload()

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=_lowerCAmelCase  )





def 				UpperCamelCase_	(					__snake_case      :    set						,      __snake_case      :    set  )->   set:




						'''simple docstring'''
						assert isinstance(__snake_case						,      __snake_case  ), F'''The input value of [set_a={set_a}] is not a set'''
						assert isinstance(__snake_case						,      __snake_case  ), F'''The input value of [set_b={set_b}] is not a set'''

						return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
					from doctest import testmod

					testmod()

