


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


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


_snake_case				=			"\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  A_ (			a_  ):







 """simple docstring"""





 def __init__(      self       :			str							,     _a       :			UNetaDModel							,     _a       :			CMStochasticIterativeScheduler					)     ->      None:
      super().__init__()

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

      UpperCAmelCase    :					str			=      None
 def 	_UpperCamelCase     (      self       :			Optional[Any]							,     _a       :			Dict=0					)     ->      Dict:
      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"					)

      UpperCAmelCase    :					Tuple			=      torch.device(f'cuda:{gpu_id}'					)

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

      if self.safety_checker is not None:
           cpu_offload(self.safety_checker							,     execution_device=_a							,     offload_buffers=_a					)
 def 	_UpperCamelCase     (      self       :			int							,     _a       :			List[Any]=0					)     ->      Optional[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."					)

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

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

      UpperCAmelCase    :					List[Any]			=      None
      for cpu_offloaded_model in [self.unet]:
           UpperCAmelCase	,			UpperCAmelCase    :					Dict			=      cpu_offload_with_hook(_a							,     _a							,     prev_module_hook=_a					)

      if self.safety_checker is not None:
           UpperCAmelCase	,			UpperCAmelCase    :					Optional[int]			=      cpu_offload_with_hook(self.safety_checker							,     _a							,     prev_module_hook=_a					)

      # We'll offload the last model manually.
      UpperCAmelCase    :					Dict			=      hook
 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 	_UpperCamelCase     (      self       :			Any					)     ->      List[Any]:
      if not hasattr(self.unet							,     "_hf_hook"					):
           return self.device
      for module in self.unet.modules():
           if (
               hasattr(_a							,     "_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 	_UpperCamelCase     (      self       :			int							,     _a       :			Optional[Any]							,     _a       :			Tuple							,     _a       :			List[Any]							,     _a       :			Union[str, Any]							,     _a       :			Optional[int]							,     _a       :			int							,     _a       :			List[Any]							,     _a       :			Tuple=None					)     ->      Any:
      UpperCAmelCase    :					Dict			=      (batch_size, num_channels, height, width)
      if isinstance(_a							,     _a					) and len(_a					) != batch_size:
           raise ValueError(
               f'You have passed a list of generators of length {len(_a					)}, 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:
           UpperCAmelCase    :					Optional[Any]			=      randn_tensor(_a							,     generator=_a							,     device=_a							,     dtype=_a					)
      else:
           UpperCAmelCase    :					Optional[Any]			=      latents.to(device=_a							,     dtype=_a					)

      # scale the initial noise by the standard deviation required by the scheduler
      UpperCAmelCase    :					Optional[Any]			=      latents * self.scheduler.init_noise_sigma
      return latents
 def 	_UpperCamelCase     (      self       :			Union[str, Any]							,     _a       :			torch.FloatTensor							,     _a       :			str = "pil"					)     ->      Union[str, 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
      UpperCAmelCase    :					str			=      (sample / 2 + 0.5).clamp(0							,     1					)
      if output_type == "pt":
           return sample

      # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
      UpperCAmelCase    :					Dict			=      sample.cpu().permute(0							,     2							,     3							,     1					).numpy()
      if output_type == "np":
           return sample

      # Output_type must be 'pil'
      UpperCAmelCase    :					List[Any]			=      self.numpy_to_pil(_a					)
      return sample
 def 	_UpperCamelCase     (      self       :			List[Any]							,     _a       :			int							,     _a       :			str							,     _a       :			Optional[int]=None					)     ->      List[str]:
      if self.unet.config.num_class_embeds is not None:
           if isinstance(_a							,     _a					):
                UpperCAmelCase    :					Tuple			=      torch.tensor(_a							,     dtype=torch.int					)
           elif isinstance(_a							,     _a					):
                assert batch_size == 1, "Batch size must be 1 if classes is an int"
                UpperCAmelCase    :					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
                UpperCAmelCase    :					str			=      torch.randint(0							,     self.unet.config.num_class_embeds							,     size=(batch_size,)					)
           UpperCAmelCase    :					List[Any]			=      class_labels.to(_a					)
      else:
           UpperCAmelCase    :					Optional[int]			=      None
      return class_labels
 def 	_UpperCamelCase     (      self       :			List[Any]							,     _a       :			str							,     _a       :			Tuple							,     _a       :			List[str]							,     _a       :			Dict							,     _a       :			int							,     _a       :			Any					)     ->      Optional[int]:
      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:
           UpperCAmelCase    :					Tuple			=      (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(_a							,     _a					) or callback_steps <= 0)
      ):
           raise ValueError(
               f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
               f' {type(_a					)}.'					)

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

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

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

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

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

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

                UpperCAmelCase    :					Dict			=      self.scheduler.step(_a							,     _a							,     _a							,     generator=_a					)[0]

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

        # 6. Post-process image sample
      UpperCAmelCase    :					Union[str, Any]			=      self.postprocess_image(_a							,     output_type=_a					)

      # 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=_a					)







from typing import TYPE_CHECKING

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


_snake_case				=			{
    "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			[
          "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "NystromformerForMaskedLM",
          "NystromformerForMultipleChoice",
          "NystromformerForQuestionAnswering",
          "NystromformerForSequenceClassification",
          "NystromformerForTokenClassification",
          "NystromformerLayer",
          "NystromformerModel",
          "NystromformerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nystromformer import (
                NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                NystromformerForMaskedLM,
                NystromformerForMultipleChoice,
                NystromformerForQuestionAnswering,
                NystromformerForSequenceClassification,
                NystromformerForTokenClassification,
                NystromformerLayer,
                NystromformerModel,
                NystromformerPreTrainedModel,
            )


else:
      import sys

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



