



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


__A       ="\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				:					Any			,			a_				:					UNetaDModel			,			a_				:					CMStochasticIterativeScheduler					):
					'''simple docstring'''





					super().__init__()

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

					__UpperCAmelCase       :	Union[str, Any]						=      None




			def 					snake_case__	(  self				:					List[str]			,			a_				:					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'''					)

					__UpperCAmelCase       :	Union[str, 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)

					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 					snake_case__	(  self				:					Dict			,			a_				:					List[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.'''					)

					__UpperCAmelCase       :	Union[str, 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       :	Tuple						=      None
					for cpu_offloaded_model in [self.unet]:
							__UpperCAmelCase				,   __UpperCAmelCase       :	str						=      cpu_offload_with_hook(a_			,			a_			,			prev_module_hook=a_					)

					if self.safety_checker is not None:
							__UpperCAmelCase				,   __UpperCAmelCase       :	str						=      cpu_offload_with_hook(self.safety_checker			,			a_			,			prev_module_hook=a_					)

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




			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 					snake_case__	(  self				:					Any					):
					'''simple docstring'''





					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 					snake_case__	(  self				:					Tuple			,			a_				:					str			,			a_				:					Dict			,			a_				:					Tuple			,			a_				:					Optional[int]			,			a_				:					str			,			a_				:					Optional[Any]			,			a_				:					int			,			a_				:					Dict=None					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      (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       :	List[str]						=      randn_tensor(a_			,			generator=a_			,			device=a_			,			dtype=a_					)
					else:
							__UpperCAmelCase       :	Optional[int]						=      latents.to(device=a_			,			dtype=a_					)

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




			def 					snake_case__	(  self				:					Union[str, Any]			,			a_				:					torch.FloatTensor			,			a_				:					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
					__UpperCAmelCase       :	Dict						=      (sample / 2 + 0.5).clamp(0			,			1					)
					if output_type == "pt":
							return sample

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

					# Output_type must be 'pil'
					__UpperCAmelCase       :	Optional[int]						=      self.numpy_to_pil(a_					)
					return sample




			def 					snake_case__	(  self				:					int			,			a_				:					Optional[Any]			,			a_				:					Any			,			a_				:					Tuple=None					):
					'''simple docstring'''





					if self.unet.config.num_class_embeds is not None:
							if isinstance(a_			,			a_					):
									__UpperCAmelCase       :	List[str]						=      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       :	str						=      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       :	Tuple						=      torch.randint(0			,			self.unet.config.num_class_embeds			,			size=(batch_size,)					)
							__UpperCAmelCase       :	Any						=      class_labels.to(a_					)
					else:
							__UpperCAmelCase       :	List[str]						=      None
					return class_labels




			def 					snake_case__	(  self				:					int			,			a_				:					int			,			a_				:					Dict			,			a_				:					Union[str, Any]			,			a_				:					Optional[int]			,			a_				:					Dict			,			a_				:					List[str]					):
					'''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:
							__UpperCAmelCase       :	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(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				:					List[Any]			,			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			,			):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      self.unet.config.sample_size
					__UpperCAmelCase       :	Union[str, 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       :	int						=      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       :	Any						=      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       :	Union[str, Any]						=      self.scheduler.timesteps
							__UpperCAmelCase       :	List[str]						=      len(a_					)
					else:
							self.scheduler.set_timesteps(a_					)
							__UpperCAmelCase       :	int						=      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       :	Union[str, Any]						=      self.scheduler.scale_model_input(a_			,			a_					)
									__UpperCAmelCase       :	List[Any]						=      self.unet(a_			,			a_			,			class_labels=a_			,			return_dict=a_					)[0]

									__UpperCAmelCase       :	Tuple						=      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       :	Dict						=      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_sentencepiece_available, is_tokenizers_available


__A       ={}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       =["BarthezTokenizer"]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       =["BarthezTokenizerFast"]


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_barthez import BarthezTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_barthez_fast import BarthezTokenizerFast

else:
				import sys

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





