




"""simple docstring"""





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


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


_lowercase		 =      """
    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  __lowercase    (     snake_case_		):


				'''simple docstring'''


				def __init__(				self				:       str ,SCREAMING_SNAKE_CASE_				:       UNetaDModel ,SCREAMING_SNAKE_CASE_				:       CMStochasticIterativeScheduler ):
										super().__init__()

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

										snake_case__    :   Optional[int]							     =   None

				def  _lowerCamelCase   (				self				:       List[str] ,SCREAMING_SNAKE_CASE_				:       List[str]=0 ):
										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''' )

										snake_case__    :   List[Any]							     =   torch.device(F'''cuda:{gpu_id}''' )

										if self.device.type != "cpu":
																self.to('''cpu''' ,silence_dtype_warnings=SCREAMING_SNAKE_CASE_ )
																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(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )

										if self.safety_checker is not None:
																cpu_offload(self.safety_checker ,execution_device=SCREAMING_SNAKE_CASE_ ,offload_buffers=SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Dict=0 ):
										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.''' )

										snake_case__    :   Optional[Any]							     =   torch.device(F'''cuda:{gpu_id}''' )

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

										snake_case__    :   Tuple							     =   None
										for cpu_offloaded_model in [self.unet]:
																snake_case__    :   Tuple							     =   cpu_offload_with_hook(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,prev_module_hook=SCREAMING_SNAKE_CASE_ )

										if self.safety_checker is not None:
																snake_case__    :   Union[str, Any]							     =   cpu_offload_with_hook(self.safety_checker ,SCREAMING_SNAKE_CASE_ ,prev_module_hook=SCREAMING_SNAKE_CASE_ )

										# We'll offload the last model manually.
										snake_case__    :   Optional[Any]							     =   hook

				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def  _lowerCamelCase   (				self				:       Union[str, Any] ):
										if not hasattr(self.unet ,'''_hf_hook''' ):
																return self.device
										for module in self.unet.modules():
																if (
																    hasattr(SCREAMING_SNAKE_CASE_ ,'''_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  _lowerCamelCase   (				self				:       Tuple ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Optional[int]=None ):
										snake_case__    :   List[str]							     =   (batch_size, num_channels, height, width)
										if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) and len(SCREAMING_SNAKE_CASE_ ) != batch_size:
																raise ValueError(
																    F'''You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_ )}, 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:
																snake_case__    :   Dict							     =   randn_tensor(SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,device=SCREAMING_SNAKE_CASE_ ,dtype=SCREAMING_SNAKE_CASE_ )
										else:
																snake_case__    :   int							     =   latents.to(device=SCREAMING_SNAKE_CASE_ ,dtype=SCREAMING_SNAKE_CASE_ )

										# scale the initial noise by the standard deviation required by the scheduler
										snake_case__    :   Optional[Any]							     =   latents * self.scheduler.init_noise_sigma
										return latents

				def  _lowerCamelCase   (				self				:       List[str] ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,SCREAMING_SNAKE_CASE_				:       str = "pil" ):
										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
										snake_case__    :   List[Any]							     =   (sample / 2 + 0.5).clamp(0 ,1 )
										if output_type == "pt":
																return sample

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

										# Output_type must be 'pil'
										snake_case__    :   Any							     =   self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
										return sample

				def  _lowerCamelCase   (				self				:       List[str] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Optional[int]=None ):
										if self.unet.config.num_class_embeds is not None:
																if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
																						snake_case__    :   Union[str, Any]							     =   torch.tensor(SCREAMING_SNAKE_CASE_ ,dtype=torch.int )
																elif isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
																						assert batch_size == 1, "Batch size must be 1 if classes is an int"
																						snake_case__    :   Optional[Any]							     =   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
																						snake_case__    :   str							     =   torch.randint(0 ,self.unet.config.num_class_embeds ,size=(batch_size,) )
																snake_case__    :   Optional[Any]							     =   class_labels.to(SCREAMING_SNAKE_CASE_ )
										else:
																snake_case__    :   Optional[Any]							     =   None
										return class_labels

				def  _lowerCamelCase   (				self				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       Tuple ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       str ):
										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:
																snake_case__    :   Dict							     =   (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(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) or callback_steps <= 0)
										):
																raise ValueError(
																    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
																    F''' {type(SCREAMING_SNAKE_CASE_ )}.''' )





				@torch.no_grad()
				@replace_example_docstring(SCREAMING_SNAKE_CASE_ )
				def __call__(				self				:       Any ,SCREAMING_SNAKE_CASE_				:       int = 1 ,SCREAMING_SNAKE_CASE_				:       Optional[Union[torch.Tensor, List[int], int]] = None ,SCREAMING_SNAKE_CASE_				:       int = 1 ,SCREAMING_SNAKE_CASE_				:       List[int] = None ,SCREAMING_SNAKE_CASE_				:       Optional[Union[torch.Generator, List[torch.Generator]]] = None ,SCREAMING_SNAKE_CASE_				:       Optional[torch.FloatTensor] = None ,SCREAMING_SNAKE_CASE_				:       Optional[str] = "pil" ,SCREAMING_SNAKE_CASE_				:       bool = True ,SCREAMING_SNAKE_CASE_				:       Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,SCREAMING_SNAKE_CASE_				:       int = 1 ,):
										snake_case__    :   Optional[int]							     =   self.unet.config.sample_size
										snake_case__    :   Optional[int]							     =   self._execution_device

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

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

										# 3. Handle class_labels for class-conditional models
										snake_case__    :   Tuple							     =   self.prepare_class_labels(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,class_labels=SCREAMING_SNAKE_CASE_ )

										# 4. Prepare timesteps
										if timesteps is not None:
																self.scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_ ,device=SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Union[str, Any]							     =   self.scheduler.timesteps
																snake_case__    :   Dict							     =   len(SCREAMING_SNAKE_CASE_ )
										else:
																self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Optional[int]							     =   self.scheduler.timesteps

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

																						snake_case__    :   Union[str, Any]							     =   self.scheduler.step(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ )[0]

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

        # 6. Post-process image sample
										snake_case__    :   Tuple							     =   self.postprocess_image(SCREAMING_SNAKE_CASE_ ,output_type=SCREAMING_SNAKE_CASE_ )

										# 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=SCREAMING_SNAKE_CASE_ )








"""simple docstring"""





def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						return int(lowerCamelCase						) if x - int(lowerCamelCase						) <= 0 else int(lowerCamelCase						) + 1


if __name__ == "__main__":
							import doctest

							doctest.testmod()


