




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


lowercase_		=							logging.get_logger(__name__)

lowercase_		=							{
    '''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 		A__				(   __SCREAMING_SNAKE_CASE ):
	lowerCamelCase__		: Any				    ="deberta"






	def __init__(	self				,     lowerCamelCase=50265				,     lowerCamelCase=768				,     lowerCamelCase=12				,     lowerCamelCase=12				,     lowerCamelCase=3072				,     lowerCamelCase="gelu"				,     lowerCamelCase=0.1				,     lowerCamelCase=0.1				,     lowerCamelCase=512				,     lowerCamelCase=0				,     lowerCamelCase=0.0_2				,     lowerCamelCase=1e-7				,     lowerCamelCase=False				,     lowerCamelCase=-1				,     lowerCamelCase=0				,     lowerCamelCase=True				,     lowerCamelCase=None				,     lowerCamelCase=0				,     lowerCamelCase="gelu"				,     **lowerCamelCase				,     )      ->   Optional[int]:




						"""simple docstring"""






						super().__init__(**lowerCamelCase						)

						__magic_name__					:							Any		   =							hidden_size
						__magic_name__					:							Optional[int]		   =							num_hidden_layers
						__magic_name__					:							str		   =							num_attention_heads
						__magic_name__					:							Tuple		   =							intermediate_size
						__magic_name__					:							List[str]		   =							hidden_act
						__magic_name__					:							Union[str, Any]		   =							hidden_dropout_prob
						__magic_name__					:							str		   =							attention_probs_dropout_prob
						__magic_name__					:							int		   =							max_position_embeddings
						__magic_name__					:							Union[str, Any]		   =							type_vocab_size
						__magic_name__					:							List[str]		   =							initializer_range
						__magic_name__					:							Optional[Any]		   =							relative_attention
						__magic_name__					:							Tuple		   =							max_relative_positions
						__magic_name__					:							Optional[Any]		   =							pad_token_id
						__magic_name__					:							Optional[int]		   =							position_biased_input

						# Backwards compatibility
						if type(lowerCamelCase						) == str:
											__magic_name__					:							List[Any]		   =							[x.strip() for x in pos_att_type.lower().split('''|'''						)]

						__magic_name__					:							Any		   =							pos_att_type
						__magic_name__					:							Tuple		   =							vocab_size
						__magic_name__					:							Optional[Any]		   =							layer_norm_eps

						__magic_name__					:							str		   =							kwargs.get('''pooler_hidden_size'''				,     lowerCamelCase						)
						__magic_name__					:							Tuple		   =							pooler_dropout
						__magic_name__					:							str		   =							pooler_hidden_act





class 		A__				(   __SCREAMING_SNAKE_CASE ):






	@property
	def  lowercase      (	self						)      ->   Mapping[str, Mapping[int, str]]:




						"""simple docstring"""






						if self.task == "multiple-choice":
											__magic_name__					:							Tuple		   =							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
						else:
											__magic_name__					:							Any		   =							{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  lowercase      (	self						)      ->   int:




						"""simple docstring"""






						return 12






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase = -1				,     lowerCamelCase = -1				,     lowerCamelCase = -1				,     lowerCamelCase = False				,     lowerCamelCase = None				,     lowerCamelCase = 3				,     lowerCamelCase = 40				,     lowerCamelCase = 40				,     lowerCamelCase = None				,     )      ->   Mapping[str, Any]:




						"""simple docstring"""






						__magic_name__					:							List[str]		   =							super().generate_dummy_inputs(preprocessor=lowerCamelCase				,     framework=lowerCamelCase						)
						if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
											del dummy_inputs["token_type_ids"]
						return dummy_inputs







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 lowerCAmelCase_						( lowerCamelCase__       ):

 '''simple docstring'''

 def __init__(	self		,      _UpperCAmelCase		,      _UpperCAmelCase					):
       super().__init__()

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

       snake_case_       =      None



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase=0					):
       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=_UpperCAmelCase					)
             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(_UpperCAmelCase		,      _UpperCAmelCase					)

       if self.safety_checker is not None:
             cpu_offload(self.safety_checker		,      execution_device=_UpperCAmelCase		,      offload_buffers=_UpperCAmelCase					)



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase=0					):
       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=_UpperCAmelCase					)
             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_       ,    snake_case_       =      cpu_offload_with_hook(_UpperCAmelCase		,      _UpperCAmelCase		,      prev_module_hook=_UpperCAmelCase					)

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

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



 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 						UpperCamelCase__     (	self					):
       if not hasattr(self.unet		,      '''_hf_hook'''					):
             return self.device
       for module in self.unet.modules():
             if (
                 hasattr(_UpperCAmelCase		,      '''_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 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=None					):
       snake_case_       =      (batch_size, num_channels, height, width)
       if isinstance(_UpperCAmelCase		,      _UpperCAmelCase					) and len(_UpperCAmelCase					) != batch_size:
             raise ValueError(
                 F'''You have passed a list of generators of length {len(_UpperCAmelCase					)}, 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(_UpperCAmelCase		,      generator=_UpperCAmelCase		,      device=_UpperCAmelCase		,      dtype=_UpperCAmelCase					)
       else:
             snake_case_       =      latents.to(device=_UpperCAmelCase		,      dtype=_UpperCAmelCase					)

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



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase = "pil"					):
       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(_UpperCAmelCase					)
       return sample



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=None					):
       if self.unet.config.num_class_embeds is not None:
             if isinstance(_UpperCAmelCase		,      _UpperCAmelCase					):
                   snake_case_       =      torch.tensor(_UpperCAmelCase		,      dtype=torch.int					)
             elif isinstance(_UpperCAmelCase		,      _UpperCAmelCase					):
                   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(_UpperCAmelCase					)
       else:
             snake_case_       =      None
       return class_labels



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					):
       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(_UpperCAmelCase		,      _UpperCAmelCase					) or callback_steps <= 0)
       ):
             raise ValueError(
                 F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                 F''' {type(_UpperCAmelCase					)}.'''					)





 @torch.no_grad()
 @replace_example_docstring(_UpperCAmelCase					)
 def __call__(	self		,      _UpperCAmelCase = 1		,      _UpperCAmelCase = None		,      _UpperCAmelCase = 1		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = "pil"		,      _UpperCAmelCase = True		,      _UpperCAmelCase = None		,      _UpperCAmelCase = 1		,      ):
       snake_case_       =      self.unet.config.sample_size
       snake_case_       =      self._execution_device

       # 1. Check inputs
       self.check_inputs(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)

       # 2. Prepare image latents
       # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
       snake_case_       =      self.prepare_latents(
           batch_size=_UpperCAmelCase		,      num_channels=self.unet.config.in_channels		,      height=_UpperCAmelCase		,      width=_UpperCAmelCase		,      dtype=self.unet.dtype		,      device=_UpperCAmelCase		,      generator=_UpperCAmelCase		,      latents=_UpperCAmelCase		,      )

       # 3. Handle class_labels for class-conditional models
       snake_case_       =      self.prepare_class_labels(_UpperCAmelCase		,      _UpperCAmelCase		,      class_labels=_UpperCAmelCase					)

       # 4. Prepare timesteps
       if timesteps is not None:
             self.scheduler.set_timesteps(timesteps=_UpperCAmelCase		,      device=_UpperCAmelCase					)
             snake_case_       =      self.scheduler.timesteps
             snake_case_       =      len(_UpperCAmelCase					)
       else:
             self.scheduler.set_timesteps(_UpperCAmelCase					)
             snake_case_       =      self.scheduler.timesteps

       # 5. Denoising loop
       # Multistep sampling: implements Algorithm 1 in the paper
       with self.progress_bar(total=_UpperCAmelCase					) as progress_bar:
             for i, t in enumerate(_UpperCAmelCase					):
                   snake_case_       =      self.scheduler.scale_model_input(_UpperCAmelCase		,      _UpperCAmelCase					)
                   snake_case_       =      self.unet(_UpperCAmelCase		,      _UpperCAmelCase		,      class_labels=_UpperCAmelCase		,      return_dict=_UpperCAmelCase					)[0]

                   snake_case_       =      self.scheduler.step(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      generator=_UpperCAmelCase					)[0]

                   # call the callback, if provided
                   progress_bar.update()
                   if callback is not None and i % callback_steps == 0:
                         callback(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)

        # 6. Post-process image sample
       snake_case_       =      self.postprocess_image(_UpperCAmelCase		,      output_type=_UpperCAmelCase					)

       # 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=_UpperCAmelCase					)