


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


lowerCAmelCase__   	=				'\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   lowerCAmelCase__ (       a):




     '''simple docstring'''


     def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       None:
            super().__init__()

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

            _A   : int       					=				None


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase=0)    ->       int:
            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   : Optional[Any]       					=				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 				_lowerCamelCase				(		self				,		__lowerCamelCase=0)    ->       List[Any]:
            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   : Union[str, Any]       					=				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   : List[str]       					=				None
            for cpu_offloaded_model in [self.unet]:
                   _A					,    _A   : Union[str, Any]       					=				cpu_offload_with_hook(__lowerCamelCase				,		__lowerCamelCase				,		prev_module_hook=__lowerCamelCase)

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

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


     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 				_lowerCamelCase				(		self)    ->       Optional[int]:
            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 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase=None)    ->       Optional[int]:
            _A   : 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   : Optional[Any]       					=				randn_tensor(__lowerCamelCase				,		generator=__lowerCamelCase				,		device=__lowerCamelCase				,		dtype=__lowerCamelCase)
            else:
                   _A   : Any       					=				latents.to(device=__lowerCamelCase				,		dtype=__lowerCamelCase)

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


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = "pil")    ->       Any:
            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   : Optional[int]       					=				(sample / 2 + 0.5).clamp(0				,		1)
            if output_type == "pt":
                   return sample

            # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
            _A   : str       					=				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 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase=None)    ->       Tuple:
            if self.unet.config.num_class_embeds is not None:
                   if isinstance(__lowerCamelCase				,		__lowerCamelCase):
                          _A   : str       					=				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   : 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
                          _A   : List[Any]       					=				torch.randint(0				,		self.unet.config.num_class_embeds				,		size=(batch_size,))
                   _A   : str       					=				class_labels.to(__lowerCamelCase)
            else:
                   _A   : Any       					=				None
            return class_labels


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)    ->       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:
                   _A   : List[str]       					=				(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				,		__lowerCamelCase = 1				,		__lowerCamelCase = None				,		__lowerCamelCase = 1				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = "pil"				,		__lowerCamelCase = True				,		__lowerCamelCase = None				,		__lowerCamelCase = 1				,		)    ->       Optional[Any]:
            _A   : Any       					=				self.unet.config.sample_size
            _A   : Any       					=				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   : 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   : List[Any]       					=				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   : Union[str, Any]       					=				self.scheduler.timesteps
                   _A   : List[Any]       					=				len(__lowerCamelCase)
            else:
                   self.scheduler.set_timesteps(__lowerCamelCase)
                   _A   : Optional[int]       					=				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   : Tuple       					=				self.scheduler.scale_model_input(__lowerCamelCase				,		__lowerCamelCase)
                          _A   : str       					=				self.unet(__lowerCamelCase				,		__lowerCamelCase				,		class_labels=__lowerCamelCase				,		return_dict=__lowerCamelCase)[0]

                          _A   : 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   : Tuple       					=				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)




from typing import TYPE_CHECKING

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


lowerCAmelCase__   	=				{
    'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				['DetaImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				[
        'DETA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'DetaForObjectDetection',
        'DetaModel',
        'DetaPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_deta import DetaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deta import (
            DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
            DetaForObjectDetection,
            DetaModel,
            DetaPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
