





"""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_	:		Any         =   logging.get_logger(__name__)  # pylint: disable=invalid-name


lowerCamelCase_	:		Union[str, 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 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_    ):

        """simple docstring"""

        super().__init__()

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

        A_    :			List[Any]									=    None

      def    lowerCamelCase_     (	self   , snake_case_=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_    :			Optional[int]									=    torch.device(F"""cuda:{gpu_id}"""    )

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

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

      def    lowerCamelCase_     (	self   , snake_case_=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_    :			Any									=    torch.device(F"""cuda:{gpu_id}"""    )

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

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

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

        # We'll offload the last model manually.
        A_    :			Any									=    hook

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

        """simple docstring"""

        if not hasattr(self.unet   , '_hf_hook'    ):
          return self.device
        for module in self.unet.modules():
          if (
              hasattr(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   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_=None    ):

        """simple docstring"""

        A_    :			Optional[int]									=    (batch_size, num_channels, height, width)
        if isinstance(snake_case_   , snake_case_    ) and len(snake_case_    ) != batch_size:
          raise ValueError(
              F"""You have passed a list of generators of length {len(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:
          A_    :			List[Any]									=    randn_tensor(snake_case_   , generator=snake_case_   , device=snake_case_   , dtype=snake_case_    )
        else:
          A_    :			Optional[int]									=    latents.to(device=snake_case_   , dtype=snake_case_    )

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = "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_    :			Tuple									=    (sample / 2 + 0.5).clamp(0   , 1    )
        if output_type == "pt":
          return sample

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

        # Output_type must be 'pil'
        A_    :			str									=    self.numpy_to_pil(snake_case_    )
        return sample

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_=None    ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_    ):

        """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(snake_case_   , 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(snake_case_    )}."""    )

      @torch.no_grad()
      @replace_example_docstring(snake_case_    )
      def __call__(	self   , snake_case_ = 1   , snake_case_ = None   , snake_case_ = 1   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = "pil"   , snake_case_ = True   , snake_case_ = None   , snake_case_ = 1   , ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    self.unet.config.sample_size
        A_    :			Any									=    self._execution_device

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

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

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

        # 4. Prepare timesteps
        if timesteps is not None:
          self.scheduler.set_timesteps(timesteps=snake_case_   , device=snake_case_    )
          A_    :			Tuple									=    self.scheduler.timesteps
          A_    :			List[Any]									=    len(snake_case_    )
        else:
          self.scheduler.set_timesteps(snake_case_    )
          A_    :			str									=    self.scheduler.timesteps

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

            A_    :			Any									=    self.scheduler.step(snake_case_   , snake_case_   , snake_case_   , generator=snake_case_    )[0]

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

        # 6. Post-process image sample
        A_    :			str									=    self.postprocess_image(snake_case_   , output_type=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=snake_case_    )







'''simple docstring'''







import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	(KDPMaAncestralDiscreteScheduler,)
     _snake_case							   =	10






     def      _UpperCamelCase (self     :						int					,					**lowercase__     :						Optional[int]       )	->							Dict:
           __A		 =   {
               "num_train_timesteps": 1100,
               "beta_start": 0.00_01,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**lowercase__       )
           return config






     def      _UpperCamelCase (self     :						str       )	->							Optional[Any]:
           for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]       )	->							int:
           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,					[0.00_02, 0.0_02, 0.02]       ):
                 self.check_over_configs(beta_start=lowercase__					,					beta_end=lowercase__       )






     def      _UpperCamelCase (self     :						int       )	->							Tuple:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=lowercase__       )






     def      _UpperCamelCase (self     :						int       )	->							Any:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config()
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps       )

           __A		 =   torch.manual_seed(0       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter * scheduler.init_noise_sigma
           __A		 =   sample.to(lowercase__       )

           for i, t in enumerate(scheduler.timesteps       ):
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 1_38_49.38_77       ) < 1e-2
           assert abs(result_mean.item() - 18.03_31       ) < 5e-3






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							str:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]       )	->							List[Any]:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config(prediction_type="v_prediction"       )
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter * scheduler.init_noise_sigma
           __A		 =   sample.to(lowercase__       )

           __A		 =   torch.manual_seed(0       )

           for i, t in enumerate(scheduler.timesteps       ):
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 3_28.99_70       ) < 1e-2
           assert abs(result_mean.item() - 0.42_84       ) < 1e-3






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							int:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config()
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps					,					device=lowercase__       )
           __A		 =   torch.manual_seed(0       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter.to(lowercase__       ) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 1_38_49.38_18       ) < 1e-1
           assert abs(result_mean.item() - 18.03_31       ) < 1e-3


