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


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


_SCREAMING_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        UpperCAmelCase_					(				__SCREAMING_SNAKE_CASE     ):




    def __init__(       self	: List[str]   ,   lowerCAmelCase	: UNetaDModel   ,   lowerCAmelCase	: CMStochasticIterativeScheduler )						->			List[str]:
      super().__init__()

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

      a__  		=  None




    def     __lowerCamelCase			(       self	: Optional[Any]   ,   lowerCAmelCase	: Union[str, Any]=0 )						->			str:
      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__  		=  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	: Any   ,   lowerCAmelCase	: Union[str, Any]=0 )						->			Union[str, 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__  		=  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__  		=  None
      for cpu_offloaded_model in [self.unet]:
        a__		,a__  		=  cpu_offload_with_hook(lowerCamelCase_   ,   lowerCamelCase_   ,   prev_module_hook=lowerCamelCase_ )

      if self.safety_checker is not None:
        a__		,a__  		=  cpu_offload_with_hook(self.safety_checker   ,   lowerCamelCase_   ,   prev_module_hook=lowerCamelCase_ )

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




    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def     __lowerCamelCase			(       self	: List[Any] )						->			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	: Any   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: List[str]   ,   lowerCAmelCase	: List[str]   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: Optional[Any]   ,   lowerCAmelCase	: List[Any]   ,   lowerCAmelCase	: int   ,   lowerCAmelCase	: Optional[Any]=None )						->			Union[str, Any]:
      a__  		=  (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__  		=  randn_tensor(lowerCamelCase_   ,   generator=lowerCamelCase_   ,   device=lowerCamelCase_   ,   dtype=lowerCamelCase_ )
      else:
        a__  		=  latents.to(device=lowerCamelCase_   ,   dtype=lowerCamelCase_ )

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




    def     __lowerCamelCase			(       self	: List[Any]   ,   lowerCAmelCase	: torch.FloatTensor   ,   lowerCAmelCase	: str = "pil" )						->			int:
      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__  		=  (sample / 2 + 0.5).clamp(0   ,   1 )
      if output_type == "pt":
        return sample

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

      # Output_type must be 'pil'
      a__  		=  self.numpy_to_pil(lowerCamelCase_ )
      return sample




    def     __lowerCamelCase			(       self	: Any   ,   lowerCAmelCase	: Dict   ,   lowerCAmelCase	: int   ,   lowerCAmelCase	: Any=None )						->			int:
      if self.unet.config.num_class_embeds is not None:
        if isinstance(lowerCamelCase_   ,   lowerCamelCase_ ):
          a__  		=  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__  		=  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__  		=  torch.randint(0   ,   self.unet.config.num_class_embeds   ,   size=(batch_size,) )
        a__  		=  class_labels.to(lowerCamelCase_ )
      else:
        a__  		=  None
      return class_labels




    def     __lowerCamelCase			(       self	: Any   ,   lowerCAmelCase	: Optional[int]   ,   lowerCAmelCase	: List[str]   ,   lowerCAmelCase	: List[str]   ,   lowerCAmelCase	: Tuple   ,   lowerCAmelCase	: Dict   ,   lowerCAmelCase	: Any )						->			List[Any]:
      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__  		=  (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	: Dict   ,   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   ,   )						->			Any:
      a__  		=  self.unet.config.sample_size
      a__  		=  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__  		=  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__  		=  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__  		=  self.scheduler.timesteps
        a__  		=  len(lowerCamelCase_ )
      else:
        self.scheduler.set_timesteps(lowerCamelCase_ )
        a__  		=  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__  		=  self.scheduler.scale_model_input(lowerCamelCase_   ,   lowerCamelCase_ )
          a__  		=  self.unet(lowerCamelCase_   ,   lowerCamelCase_   ,   class_labels=lowerCamelCase_   ,   return_dict=lowerCamelCase_ )[0]

          a__  		=  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__  		=  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_ )





"""simple docstring"""







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__)