



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


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


UpperCamelCase_									=   '''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     UNetaDModel  ,lowerCamelCase_:     CMStochasticIterativeScheduler							)	->      None:
       super().__init__()

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

       UpperCAmelCase_     :       List[str]  		=					None


   def       A__    (    self:     Tuple  ,lowerCamelCase_:     Dict=0							)	->      Any:
       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=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       A__    (    self:     int  ,lowerCamelCase_:     Any=0							)	->      List[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_     :       Any  		=					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)

       UpperCAmelCase_     :       Optional[Any]  		=					None
       for cpu_offloaded_model in [self.unet]:
           UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					cpu_offload_with_hook(lowerCamelCase_  ,lowerCamelCase_  ,prev_module_hook=lowerCamelCase_							)

       if self.safety_checker is not None:
           UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					cpu_offload_with_hook(self.safety_checker  ,lowerCamelCase_  ,prev_module_hook=lowerCamelCase_							)

       # We'll offload the last model manually.
       UpperCAmelCase_     :       Union[str, Any]  		=					hook


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

       # scale the initial noise by the standard deviation required by the scheduler
       UpperCAmelCase_     :       Optional[int]  		=					latents * self.scheduler.init_noise_sigma
       return latents


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

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

       # Output_type must be 'pil'
       UpperCAmelCase_     :       Any  		=					self.numpy_to_pil(lowerCamelCase_							)
       return sample


   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     Optional[Any]=None							)	->      List[Any]:
       if self.unet.config.num_class_embeds is not None:
           if isinstance(lowerCamelCase_  ,lowerCamelCase_							):
               UpperCAmelCase_     :       Tuple  		=					torch.tensor(lowerCamelCase_  ,dtype=torch.int							)
           elif isinstance(lowerCamelCase_  ,lowerCamelCase_							):
               assert batch_size == 1, "Batch size must be 1 if classes is an int"
               UpperCAmelCase_     :       Optional[Any]  		=					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_     :       Optional[int]  		=					class_labels.to(lowerCamelCase_							)
       else:
           UpperCAmelCase_     :       Dict  		=					None
       return class_labels


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

               UpperCAmelCase_     :       Union[str, Any]  		=					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
       UpperCAmelCase_     :       Tuple  		=					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 collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__snake_case												=							logging.get_logger(__name__)

__snake_case												=							{
    '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/config.json''',
    '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/config.json''',
    '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json''',
    '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json''',
    '''microsoft/deberta-xlarge-mnli''': '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json''',
}


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						List[Any]     =		'deberta'


     def __init__(   self     ,					__UpperCAmelCase=50265     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=0.02     ,					__UpperCAmelCase=1e-7     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=-1     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase="gelu"     ,					**__UpperCAmelCase     ,					)							->				Any:
          super().__init__(**__UpperCAmelCase				)

          _a     						=					hidden_size
          _a     						=					num_hidden_layers
          _a     						=					num_attention_heads
          _a     						=					intermediate_size
          _a     						=					hidden_act
          _a     						=					hidden_dropout_prob
          _a     						=					attention_probs_dropout_prob
          _a     						=					max_position_embeddings
          _a     						=					type_vocab_size
          _a     						=					initializer_range
          _a     						=					relative_attention
          _a     						=					max_relative_positions
          _a     						=					pad_token_id
          _a     						=					position_biased_input

          # Backwards compatibility
          if type(__UpperCAmelCase				) == str:
               _a     						=					[x.strip() for x in pos_att_type.lower().split('''|'''				)]

          _a     						=					pos_att_type
          _a     						=					vocab_size
          _a     						=					layer_norm_eps

          _a     						=					kwargs.get('''pooler_hidden_size'''     ,					__UpperCAmelCase				)
          _a     						=					pooler_dropout
          _a     						=					pooler_hidden_act







class      __lowerCamelCase	(       a__					):

     '''simple docstring'''


     @property
     def 				_UpperCAmelCase   (   self				)							->				Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _a     						=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
               _a     						=					{0: '''batch''', 1: '''sequence'''}
          if self._config.type_vocab_size > 0:
               return OrderedDict(
                   [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]				)
          else:
               return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]				)


     @property
     def 				_UpperCAmelCase   (   self				)							->				int:
          return 12





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = 3     ,					__UpperCAmelCase = 40     ,					__UpperCAmelCase = 40     ,					__UpperCAmelCase = None     ,					)							->				Mapping[str, Any]:
          _a     						=					super().generate_dummy_inputs(preprocessor=__UpperCAmelCase     ,					framework=__UpperCAmelCase				)
          if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
               del dummy_inputs["token_type_ids"]
          return dummy_inputs