






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


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


A__     :      List[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 (	A__     ):

				"""simple docstring"""




				def __init__(					self						:   List[str],  lowerCamelCase						:   UNetaDModel,  lowerCamelCase						:   CMStochasticIterativeScheduler  ):

								'''simple docstring'''
								super().__init__()

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

								lowercase__       			=      None




				def     lowercase__       (					self						:   List[str],  lowerCamelCase						:   str=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'''  )

								lowercase__       			=      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     lowercase__       (					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.'''  )

								lowercase__       			=      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)

								lowercase__       			=      None
								for cpu_offloaded_model in [self.unet]:
												lowercase__			,						lowercase__       			=      cpu_offload_with_hook(lowerCamelCase,  lowerCamelCase,  prev_module_hook=lowerCamelCase  )

								if self.safety_checker is not None:
												lowercase__			,						lowercase__       			=      cpu_offload_with_hook(self.safety_checker,  lowerCamelCase,  prev_module_hook=lowerCamelCase  )

								# We'll offload the last model manually.
								lowercase__       			=      hook




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def     lowercase__       (					self						:   List[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     lowercase__       (					self						:   Any,  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   List[str],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Any,  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   str,  lowerCamelCase						:   Union[str, Any]=None  ):

								'''simple docstring'''
								lowercase__       			=      (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:
												lowercase__       			=      randn_tensor(lowerCamelCase,  generator=lowerCamelCase,  device=lowerCamelCase,  dtype=lowerCamelCase  )
								else:
												lowercase__       			=      latents.to(device=lowerCamelCase,  dtype=lowerCamelCase  )

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




				def     lowercase__       (					self						:   List[str],  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
								lowercase__       			=      (sample / 2 + 0.5).clamp(0,  1  )
								if output_type == "pt":
												return sample

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

								# Output_type must be 'pil'
								lowercase__       			=      self.numpy_to_pil(lowerCamelCase  )
								return sample




				def     lowercase__       (					self						:   Union[str, Any],  lowerCamelCase						:   Any,  lowerCamelCase						:   str,  lowerCamelCase						:   Optional[Any]=None  ):

								'''simple docstring'''
								if self.unet.config.num_class_embeds is not None:
												if isinstance(lowerCamelCase,  lowerCamelCase  ):
																lowercase__       			=      torch.tensor(lowerCamelCase,  dtype=torch.int  )
												elif isinstance(lowerCamelCase,  lowerCamelCase  ):
																assert batch_size == 1, "Batch size must be 1 if classes is an int"
																lowercase__       			=      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
																lowercase__       			=      torch.randint(0,  self.unet.config.num_class_embeds,  size=(batch_size,)  )
												lowercase__       			=      class_labels.to(lowerCamelCase  )
								else:
												lowercase__       			=      None
								return class_labels




				def     lowercase__       (					self						:   int,  lowerCamelCase						:   List[str],  lowerCamelCase						:   str,  lowerCamelCase						:   Any,  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   int,  lowerCamelCase						:   Optional[int]  ):

								'''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:
												lowercase__       			=      (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						:   Union[str, Any],  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'''
								lowercase__       			=      self.unet.config.sample_size
								lowercase__       			=      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)
								lowercase__       			=      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
								lowercase__       			=      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  )
												lowercase__       			=      self.scheduler.timesteps
												lowercase__       			=      len(lowerCamelCase  )
								else:
												self.scheduler.set_timesteps(lowerCamelCase  )
												lowercase__       			=      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  ):
																lowercase__       			=      self.scheduler.scale_model_input(lowerCamelCase,  lowerCamelCase  )
																lowercase__       			=      self.unet(lowerCamelCase,  lowerCamelCase,  class_labels=lowerCamelCase,  return_dict=lowerCamelCase  )[0]

																lowercase__       			=      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
								lowercase__       			=      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


A__     :      Dict             =			{'configuration_sew_d': ['SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWDConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Union[str, Any]             =			[
							    'SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SEWDForCTC',
							    'SEWDForSequenceClassification',
							    'SEWDModel',
							    'SEWDPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_sew_d import (
														    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SEWDForCTC,
														    SEWDForSequenceClassification,
														    SEWDModel,
														    SEWDPreTrainedModel,
														)


else:
							import sys

							A__     :      Tuple             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




