




"""simple docstring"""

import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


_lowerCamelCase					:     List[Any]				       =    logging.get_logger(__name__)

class     lowercase (      __UpperCAmelCase):







				def __init__(							self				:   int ,       *_lowerCamelCase				:   Optional[Any] ,       **_lowerCamelCase				:   Any    ):



					"""simple docstring"""

					warnings.warn(
					    '''The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
					    ''' Please use ConditionalDetrImageProcessor instead.''' ,       _lowerCamelCase ,       )
					super().__init__(*_lowerCamelCase ,       **_lowerCamelCase    )











"""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


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


_lowerCamelCase					:     Any				       =    '\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     lowercase (      __UpperCAmelCase):







				def __init__(							self				:   Union[str, Any] ,       _lowerCamelCase				:   UNetaDModel ,       _lowerCamelCase				:   CMStochasticIterativeScheduler    ):



					"""simple docstring"""

					super().__init__()

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

					A_					:			Optional[Any]						=						None







				def    a_					(							self				:   Any ,       _lowerCamelCase				:   Any=0    ):



					"""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'''    )

					A_					:			Tuple						=						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				:   Optional[int] ,       _lowerCamelCase				:   Optional[Any]=0    ):



					"""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.'''    )

					A_					:			Optional[int]						=						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)

					A_					:			Any						=						None
					for cpu_offloaded_model in [self.unet]:
						A_				,	A_					:			Optional[Any]						=						cpu_offload_with_hook(_lowerCamelCase ,       _lowerCamelCase ,       prev_module_hook=_lowerCamelCase    )

					if self.safety_checker is not None:
						A_				,	A_					:			List[Any]						=						cpu_offload_with_hook(self.safety_checker ,       _lowerCamelCase ,       prev_module_hook=_lowerCamelCase    )

					# We'll offload the last model manually.
					A_					:			Union[str, Any]						=						hook







				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def    a_					(							self				:   Any    ):



					"""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				:   List[Any] ,       _lowerCamelCase				:   Union[str, Any] ,       _lowerCamelCase				:   List[Any] ,       _lowerCamelCase				:   int ,       _lowerCamelCase				:   Optional[int] ,       _lowerCamelCase				:   Tuple ,       _lowerCamelCase				:   Dict ,       _lowerCamelCase				:   Tuple ,       _lowerCamelCase				:   str=None    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						(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:
						A_					:			Dict						=						randn_tensor(_lowerCamelCase ,       generator=_lowerCamelCase ,       device=_lowerCamelCase ,       dtype=_lowerCamelCase    )
					else:
						A_					:			List[Any]						=						latents.to(device=_lowerCamelCase ,       dtype=_lowerCamelCase    )

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







				def    a_					(							self				:   int ,       _lowerCamelCase				:   torch.FloatTensor ,       _lowerCamelCase				:   str = "pil"    ):



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

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

					# Output_type must be 'pil'
					A_					:			Optional[Any]						=						self.numpy_to_pil(_lowerCamelCase    )
					return sample







				def    a_					(							self				:   str ,       _lowerCamelCase				:   Any ,       _lowerCamelCase				:   Tuple ,       _lowerCamelCase				:   Dict=None    ):



					"""simple docstring"""

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







				def    a_					(							self				:   List[Any] ,       _lowerCamelCase				:   Any ,       _lowerCamelCase				:   str ,       _lowerCamelCase				:   Optional[Any] ,       _lowerCamelCase				:   Any ,       _lowerCamelCase				:   Tuple ,       _lowerCamelCase				:   Optional[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:
						A_					:			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(_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				:   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 ,       ):



					"""simple docstring"""

					A_					:			Dict						=						self.unet.config.sample_size
					A_					:			int						=						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)
					A_					:			Optional[int]						=						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
					A_					:			Tuple						=						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    )
						A_					:			Optional[int]						=						self.scheduler.timesteps
						A_					:			Dict						=						len(_lowerCamelCase    )
					else:
						self.scheduler.set_timesteps(_lowerCamelCase    )
						A_					:			Dict						=						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    ):
							A_					:			List[Any]						=						self.scheduler.scale_model_input(_lowerCamelCase ,       _lowerCamelCase    )
							A_					:			int						=						self.unet(_lowerCamelCase ,       _lowerCamelCase ,       class_labels=_lowerCamelCase ,       return_dict=_lowerCamelCase    )[0]

							A_					:			Optional[int]						=						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
					A_					:			Union[str, Any]						=						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    )





