
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__						=			{
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						=			["ImageGPTFeatureExtractor"]
						lowercase__						=			["ImageGPTImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						=			[
						    "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ImageGPTForCausalImageModeling",
						    "ImageGPTForImageClassification",
						    "ImageGPTModel",
						    "ImageGPTPreTrainedModel",
						    "load_tf_weights_in_imagegpt",
						]


if TYPE_CHECKING:
						from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
												from .image_processing_imagegpt import ImageGPTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_imagegpt import (
												    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ImageGPTForCausalImageModeling,
												    ImageGPTForImageClassification,
												    ImageGPTModel,
												    ImageGPTPreTrainedModel,
												    load_tf_weights_in_imagegpt,
												)

else:
						import sys

						lowercase__						=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




'''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__						=			"\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = \"cuda\"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")\n        ```\n"






class   snake_case__					(			__SCREAMING_SNAKE_CASE				):







		"""simple docstring"""

		def __init__(			self							: List[Any]     ,  UpperCamelCase__							: UNetaDModel     ,  UpperCamelCase__							: CMStochasticIterativeScheduler			)	-> None:






				"""simple docstring"""







				super().__init__()

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

				snake_case     :					str     =	None



		def 			lowerCAmelCase							(			self							: Tuple     ,  UpperCamelCase__							: str=0			)	-> int:






				"""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'''			)

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

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

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



		def 			lowerCAmelCase							(			self							: Optional[int]     ,  UpperCamelCase__							: int=0			)	-> Dict:






				"""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.'''			)

				snake_case     :					Union[str, Any]     =	torch.device(f'cuda:{gpu_id}'			)

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

				snake_case     :					List[Any]     =	None
				for cpu_offloaded_model in [self.unet]:
						snake_case ,snake_case     :					Union[str, Any]     =	cpu_offload_with_hook(UpperCamelCase__     ,  UpperCamelCase__     ,  prev_module_hook=UpperCamelCase__			)

				if self.safety_checker is not None:
						snake_case ,snake_case     :					Optional[Any]     =	cpu_offload_with_hook(self.safety_checker     ,  UpperCamelCase__     ,  prev_module_hook=UpperCamelCase__			)

				# 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							: int			)	-> List[str]:






				"""simple docstring"""







				if not hasattr(self.unet     ,  '''_hf_hook'''			):
						return self.device
				for module in self.unet.modules():
						if (
						    hasattr(UpperCamelCase__     ,  '''_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							: int     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: Optional[int]=None			)	-> Tuple:






				"""simple docstring"""







				snake_case     :					Union[str, Any]     =	(batch_size, num_channels, height, width)
				if isinstance(UpperCamelCase__     ,  UpperCamelCase__			) and len(UpperCamelCase__			) != batch_size:
						raise ValueError(
						    f'You have passed a list of generators of length {len(UpperCamelCase__			)}, 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     :					List[Any]     =	randn_tensor(UpperCamelCase__     ,  generator=UpperCamelCase__     ,  device=UpperCamelCase__     ,  dtype=UpperCamelCase__			)
				else:
						snake_case     :					Tuple     =	latents.to(device=UpperCamelCase__     ,  dtype=UpperCamelCase__			)

				# scale the initial noise by the standard deviation required by the scheduler
				snake_case     :					str     =	latents * self.scheduler.init_noise_sigma
				return latents



		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: torch.FloatTensor     ,  UpperCamelCase__							: str = "pil"			)	-> Union[str, Any]:






				"""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
				snake_case     :					Any     =	(sample / 2 + 0.5).clamp(0     ,  1			)
				if output_type == "pt":
						return sample

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

				# Output_type must be 'pil'
				snake_case     :					Tuple     =	self.numpy_to_pil(UpperCamelCase__			)
				return sample



		def 			lowerCAmelCase							(			self							: Tuple     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: List[str]=None			)	-> List[str]:






				"""simple docstring"""







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



		def 			lowerCAmelCase							(			self							: List[Any]     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Union[str, Any]			)	-> Union[str, Any]:






				"""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:
						snake_case     :					List[Any]     =	(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(UpperCamelCase__     ,  UpperCamelCase__			) or callback_steps <= 0)
				):
						raise ValueError(
						    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
						    f' {type(UpperCamelCase__			)}.'			)



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






				"""simple docstring"""







				snake_case     :					Optional[int]     =	self.unet.config.sample_size
				snake_case     :					List[Any]     =	self._execution_device

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

				# 2. Prepare image latents
				# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
				snake_case     :					List[str]     =	self.prepare_latents(
				    batch_size=UpperCamelCase__     ,  num_channels=self.unet.config.in_channels     ,  height=UpperCamelCase__     ,  width=UpperCamelCase__     ,  dtype=self.unet.dtype     ,  device=UpperCamelCase__     ,  generator=UpperCamelCase__     ,  latents=UpperCamelCase__     ,  )

				# 3. Handle class_labels for class-conditional models
				snake_case     :					List[str]     =	self.prepare_class_labels(UpperCamelCase__     ,  UpperCamelCase__     ,  class_labels=UpperCamelCase__			)

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

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

								snake_case     :					Dict     =	self.scheduler.step(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  generator=UpperCamelCase__			)[0]

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

        # 6. Post-process image sample
				snake_case     :					Any     =	self.postprocess_image(UpperCamelCase__     ,  output_type=UpperCamelCase__			)

				# 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=UpperCamelCase__			)


