





"""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


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


a__       :  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   lowercase_					(  UpperCAmelCase__				):

       def __init__(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        super().__init__()

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

        snake_case__     				=					None

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_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"  )

        snake_case__     				=					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 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_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."  )

        snake_case__     				=					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)

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

        if self.safety_checker is not None:
         snake_case__     				=					cpu_offload_with_hook(self.safety_checker ,       snake_case_ ,       prev_module_hook=snake_case_  )

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

       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 		_SCREAMING_SNAKE_CASE(      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 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE=None  ):






        '''simple docstring'''

        snake_case__     				=					(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:
         snake_case__     				=					randn_tensor(snake_case_ ,       generator=snake_case_ ,       device=snake_case_ ,       dtype=snake_case_  )
        else:
         snake_case__     				=					latents.to(device=snake_case_ ,       dtype=snake_case_  )

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

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_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
        snake_case__     				=					(sample / 2 + 0.5).clamp(0 ,       1  )
        if output_type == "pt":
         return sample

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

        # Output_type must be 'pil'
        snake_case__     				=					self.numpy_to_pil(snake_case_  )
        return sample

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE=None  ):






        '''simple docstring'''

        if self.unet.config.num_class_embeds is not None:
         if isinstance(snake_case_ ,       snake_case_  ):
          snake_case__     				=					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"
          snake_case__     				=					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
          snake_case__     				=					torch.randint(0 ,       self.unet.config.num_class_embeds ,       size=(batch_size,)  )
         snake_case__     				=					class_labels.to(snake_case_  )
        else:
         snake_case__     				=					None
        return class_labels

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_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:
         snake_case__     				=					(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 ,       __SCREAMING_SNAKE_CASE = 1 ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = 1 ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = "pil" ,       __SCREAMING_SNAKE_CASE = True ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = 1 ,       ):






        '''simple docstring'''

        snake_case__     				=					self.unet.config.sample_size
        snake_case__     				=					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)
        snake_case__     				=					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
        snake_case__     				=					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_  )
         snake_case__     				=					self.scheduler.timesteps
         snake_case__     				=					len(snake_case_  )
        else:
         self.scheduler.set_timesteps(snake_case_  )
         snake_case__     				=					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_  ):
          snake_case__     				=					self.scheduler.scale_model_input(snake_case_ ,       snake_case_  )
          snake_case__     				=					self.unet(snake_case_ ,       snake_case_ ,       class_labels=snake_case_ ,       return_dict=snake_case_  )[0]

          snake_case__     				=					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
        snake_case__     				=					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"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_	:		Union[str, Any]         =   {
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		Any         =   ['CamembertTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		int         =   ['CamembertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		Optional[Any]         =   [
       'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'CamembertForCausalLM',
       'CamembertForMaskedLM',
       'CamembertForMultipleChoice',
       'CamembertForQuestionAnswering',
       'CamembertForSequenceClassification',
       'CamembertForTokenClassification',
       'CamembertModel',
       'CamembertPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		int         =   [
       'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFCamembertForCausalLM',
       'TFCamembertForMaskedLM',
       'TFCamembertForMultipleChoice',
       'TFCamembertForQuestionAnswering',
       'TFCamembertForSequenceClassification',
       'TFCamembertForTokenClassification',
       'TFCamembertModel',
       'TFCamembertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_camembert import CamembertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_camembert_fast import CamembertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_camembert import (
          CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CamembertForCausalLM,
          CamembertForMaskedLM,
          CamembertForMultipleChoice,
          CamembertForQuestionAnswering,
          CamembertForSequenceClassification,
          CamembertForTokenClassification,
          CamembertModel,
          CamembertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_camembert import (
          TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCamembertForCausalLM,
          TFCamembertForMaskedLM,
          TFCamembertForMultipleChoice,
          TFCamembertForQuestionAnswering,
          TFCamembertForSequenceClassification,
          TFCamembertForTokenClassification,
          TFCamembertModel,
          TFCamembertPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_	:		int         =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)