
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__				=							"""
    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       A__  (		lowerCAmelCase__							):





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



											"""simple docstring"""


											super().__init__()

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

											__lowercase								=   None





							def      a__					(					self    :  Any	,					_UpperCAmelCase    :  Optional[int]=0							)		->						int:



											"""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'							)

											__lowercase								=   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      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  Any=0							)		->						str:



											"""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.'							)

											__lowercase								=   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)

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

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

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





							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def      a__					(					self    :  Tuple							)		->						Any:



											"""simple docstring"""


											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      a__					(					self    :  str	,					_UpperCAmelCase    :  List[Any]	,					_UpperCAmelCase    :  Union[str, Any]	,					_UpperCAmelCase    :  Union[str, Any]	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  Any	,					_UpperCAmelCase    :  Any	,					_UpperCAmelCase    :  List[Any]	,					_UpperCAmelCase    :  int=None							)		->						List[str]:



											"""simple docstring"""


											__lowercase								=   (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:
															__lowercase								=   randn_tensor(_UpperCAmelCase	,					generator=_UpperCAmelCase	,					device=_UpperCAmelCase	,					dtype=_UpperCAmelCase							)
											else:
															__lowercase								=   latents.to(device=_UpperCAmelCase	,					dtype=_UpperCAmelCase							)

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





							def      a__					(					self    :  Optional[int]	,					_UpperCAmelCase    :  torch.FloatTensor	,					_UpperCAmelCase    :  str = "pil"							)		->						str:



											"""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
											__lowercase								=   (sample / 2 + 0.5).clamp(0	,					1							)
											if output_type == "pt":
															return sample

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

											# Output_type must be 'pil'
											__lowercase								=   self.numpy_to_pil(_UpperCAmelCase							)
											return sample





							def      a__					(					self    :  int	,					_UpperCAmelCase    :  str	,					_UpperCAmelCase    :  Union[str, Any]	,					_UpperCAmelCase    :  Optional[Any]=None							)		->						str:



											"""simple docstring"""


											if self.unet.config.num_class_embeds is not None:
															if isinstance(_UpperCAmelCase	,					_UpperCAmelCase							):
																			__lowercase								=   torch.tensor(_UpperCAmelCase	,					dtype=torch.int							)
															elif isinstance(_UpperCAmelCase	,					_UpperCAmelCase							):
																			assert batch_size == 1, "Batch size must be 1 if classes is an int"
																			__lowercase								=   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
																			__lowercase								=   torch.randint(0	,					self.unet.config.num_class_embeds	,					size=(batch_size,)							)
															__lowercase								=   class_labels.to(_UpperCAmelCase							)
											else:
															__lowercase								=   None
											return class_labels





							def      a__					(					self    :  List[str]	,					_UpperCAmelCase    :  int	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  Dict	,					_UpperCAmelCase    :  Any	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  int							)		->						Optional[int]:



											"""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:
															__lowercase								=   (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    :  Dict	,					_UpperCAmelCase    :  int = 1	,					_UpperCAmelCase    :  Optional[Union[torch.Tensor, List[int], int]] = None	,					_UpperCAmelCase    :  int = 1	,					_UpperCAmelCase    :  List[int] = None	,					_UpperCAmelCase    :  Optional[Union[torch.Generator, List[torch.Generator]]] = None	,					_UpperCAmelCase    :  Optional[torch.FloatTensor] = None	,					_UpperCAmelCase    :  Optional[str] = "pil"	,					_UpperCAmelCase    :  bool = True	,					_UpperCAmelCase    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,					_UpperCAmelCase    :  int = 1	,					)		->						Tuple:



											"""simple docstring"""


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

																			__lowercase								=   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
											__lowercase								=   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							)







from ..utils import DummyObject, requires_backends
class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Tuple            =				["tokenizers"]





							def __init__(					self    :  str	,					*_UpperCAmelCase    :  List[str]	,					**_UpperCAmelCase    :  List[str]							)		->						int:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Dict            =				["tokenizers"]





							def __init__(					self    :  str	,					*_UpperCAmelCase    :  Union[str, Any]	,					**_UpperCAmelCase    :  Tuple							)		->						Optional[Any]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Union[str, Any]            =				["tokenizers"]





							def __init__(					self    :  Union[str, Any]	,					*_UpperCAmelCase    :  Union[str, Any]	,					**_UpperCAmelCase    :  int							)		->						Dict:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Optional[int]            =				["tokenizers"]





							def __init__(					self    :  int	,					*_UpperCAmelCase    :  Dict	,					**_UpperCAmelCase    :  Optional[Any]							)		->						List[str]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		str            =				["tokenizers"]





							def __init__(					self    :  Optional[Any]	,					*_UpperCAmelCase    :  Optional[int]	,					**_UpperCAmelCase    :  Optional[int]							)		->						Optional[int]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Optional[int]            =				["tokenizers"]





							def __init__(					self    :  Any	,					*_UpperCAmelCase    :  Union[str, Any]	,					**_UpperCAmelCase    :  List[str]							)		->						Tuple:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Optional[Any]            =				["tokenizers"]





							def __init__(					self    :  str	,					*_UpperCAmelCase    :  Optional[int]	,					**_UpperCAmelCase    :  Union[str, Any]							)		->						Optional[Any]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		List[str]            =				["tokenizers"]





							def __init__(					self    :  Optional[int]	,					*_UpperCAmelCase    :  Union[str, Any]	,					**_UpperCAmelCase    :  List[str]							)		->						Optional[Any]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Any            =				["tokenizers"]





							def __init__(					self    :  Optional[Any]	,					*_UpperCAmelCase    :  Tuple	,					**_UpperCAmelCase    :  Any							)		->						Optional[Any]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Dict            =				["tokenizers"]





							def __init__(					self    :  List[str]	,					*_UpperCAmelCase    :  Tuple	,					**_UpperCAmelCase    :  List[str]							)		->						List[Any]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		List[Any]            =				["tokenizers"]





							def __init__(					self    :  Tuple	,					*_UpperCAmelCase    :  Union[str, Any]	,					**_UpperCAmelCase    :  Any							)		->						Any:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Tuple            =				["tokenizers"]





							def __init__(					self    :  int	,					*_UpperCAmelCase    :  int	,					**_UpperCAmelCase    :  Optional[Any]							)		->						List[str]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Optional[int]            =				["tokenizers"]





							def __init__(					self    :  Tuple	,					*_UpperCAmelCase    :  List[Any]	,					**_UpperCAmelCase    :  int							)		->						Tuple:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Tuple            =				["tokenizers"]





							def __init__(					self    :  List[str]	,					*_UpperCAmelCase    :  Any	,					**_UpperCAmelCase    :  str							)		->						int:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		str            =				["tokenizers"]





							def __init__(					self    :  Any	,					*_UpperCAmelCase    :  str	,					**_UpperCAmelCase    :  List[Any]							)		->						List[str]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Dict            =				["tokenizers"]





							def __init__(					self    :  int	,					*_UpperCAmelCase    :  Tuple	,					**_UpperCAmelCase    :  Optional[Any]							)		->						Dict:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Tuple            =				["tokenizers"]





							def __init__(					self    :  Tuple	,					*_UpperCAmelCase    :  str	,					**_UpperCAmelCase    :  List[Any]							)		->						Union[str, Any]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Optional[Any]            =				["tokenizers"]





							def __init__(					self    :  Dict	,					*_UpperCAmelCase    :  Optional[Any]	,					**_UpperCAmelCase    :  List[Any]							)		->						Optional[Any]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Dict            =				["tokenizers"]





							def __init__(					self    :  Optional[Any]	,					*_UpperCAmelCase    :  Optional[int]	,					**_UpperCAmelCase    :  List[str]							)		->						Optional[int]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Tuple            =				["tokenizers"]





							def __init__(					self    :  Union[str, Any]	,					*_UpperCAmelCase    :  Optional[int]	,					**_UpperCAmelCase    :  Any							)		->						Tuple:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		List[str]            =				["tokenizers"]





							def __init__(					self    :  Union[str, Any]	,					*_UpperCAmelCase    :  List[Any]	,					**_UpperCAmelCase    :  Any							)		->						Tuple:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		int            =				["tokenizers"]





							def __init__(					self    :  Optional[Any]	,					*_UpperCAmelCase    :  Tuple	,					**_UpperCAmelCase    :  Tuple							)		->						List[str]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		int            =				["tokenizers"]





							def __init__(					self    :  Tuple	,					*_UpperCAmelCase    :  Tuple	,					**_UpperCAmelCase    :  Tuple							)		->						Tuple:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		List[str]            =				["tokenizers"]





							def __init__(					self    :  Tuple	,					*_UpperCAmelCase    :  List[Any]	,					**_UpperCAmelCase    :  Tuple							)		->						List[Any]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		List[str]            =				["tokenizers"]





							def __init__(					self    :  int	,					*_UpperCAmelCase    :  Optional[Any]	,					**_UpperCAmelCase    :  List[Any]							)		->						List[Any]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Optional[int]            =				["tokenizers"]





							def __init__(					self    :  Any	,					*_UpperCAmelCase    :  Optional[Any]	,					**_UpperCAmelCase    :  str							)		->						Tuple:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		str            =				["tokenizers"]





							def __init__(					self    :  Optional[int]	,					*_UpperCAmelCase    :  Any	,					**_UpperCAmelCase    :  Union[str, Any]							)		->						int:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Dict            =				["tokenizers"]





							def __init__(					self    :  Union[str, Any]	,					*_UpperCAmelCase    :  Dict	,					**_UpperCAmelCase    :  Any							)		->						Any:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Union[str, Any]            =				["tokenizers"]





							def __init__(					self    :  Any	,					*_UpperCAmelCase    :  Tuple	,					**_UpperCAmelCase    :  Optional[int]							)		->						int:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Union[str, Any]            =				["tokenizers"]





							def __init__(					self    :  Tuple	,					*_UpperCAmelCase    :  Optional[int]	,					**_UpperCAmelCase    :  Tuple							)		->						List[str]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Optional[int]            =				["tokenizers"]





							def __init__(					self    :  Dict	,					*_UpperCAmelCase    :  Optional[Any]	,					**_UpperCAmelCase    :  Optional[int]							)		->						Dict:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Tuple            =				["tokenizers"]





							def __init__(					self    :  Optional[int]	,					*_UpperCAmelCase    :  Dict	,					**_UpperCAmelCase    :  str							)		->						Optional[int]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Tuple            =				["tokenizers"]





							def __init__(					self    :  Optional[int]	,					*_UpperCAmelCase    :  Optional[Any]	,					**_UpperCAmelCase    :  List[Any]							)		->						Optional[Any]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		int            =				["tokenizers"]





							def __init__(					self    :  Optional[Any]	,					*_UpperCAmelCase    :  Tuple	,					**_UpperCAmelCase    :  Any							)		->						str:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Tuple            =				["tokenizers"]





							def __init__(					self    :  Dict	,					*_UpperCAmelCase    :  List[Any]	,					**_UpperCAmelCase    :  Tuple							)		->						Optional[Any]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Any            =				["tokenizers"]





							def __init__(					self    :  int	,					*_UpperCAmelCase    :  str	,					**_UpperCAmelCase    :  Any							)		->						Optional[int]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		List[Any]            =				["tokenizers"]





							def __init__(					self    :  Any	,					*_UpperCAmelCase    :  Any	,					**_UpperCAmelCase    :  Tuple							)		->						List[str]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Any            =				["tokenizers"]





							def __init__(					self    :  Union[str, Any]	,					*_UpperCAmelCase    :  Optional[Any]	,					**_UpperCAmelCase    :  Dict							)		->						str:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Union[str, Any]            =				["tokenizers"]





							def __init__(					self    :  List[str]	,					*_UpperCAmelCase    :  List[str]	,					**_UpperCAmelCase    :  List[str]							)		->						Union[str, Any]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Optional[int]            =				["tokenizers"]





							def __init__(					self    :  Dict	,					*_UpperCAmelCase    :  List[Any]	,					**_UpperCAmelCase    :  List[Any]							)		->						Union[str, Any]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Optional[Any]            =				["tokenizers"]





							def __init__(					self    :  Optional[int]	,					*_UpperCAmelCase    :  Optional[int]	,					**_UpperCAmelCase    :  str							)		->						Tuple:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Any            =				["tokenizers"]





							def __init__(					self    :  Optional[int]	,					*_UpperCAmelCase    :  str	,					**_UpperCAmelCase    :  Optional[int]							)		->						Optional[int]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		List[str]            =				["tokenizers"]





							def __init__(					self    :  Tuple	,					*_UpperCAmelCase    :  Tuple	,					**_UpperCAmelCase    :  Optional[Any]							)		->						Tuple:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Optional[Any]            =				["tokenizers"]





							def __init__(					self    :  int	,					*_UpperCAmelCase    :  Tuple	,					**_UpperCAmelCase    :  Dict							)		->						Union[str, Any]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Union[str, Any]            =				["tokenizers"]





							def __init__(					self    :  List[str]	,					*_UpperCAmelCase    :  Optional[Any]	,					**_UpperCAmelCase    :  str							)		->						Union[str, Any]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Optional[Any]            =				["tokenizers"]





							def __init__(					self    :  Dict	,					*_UpperCAmelCase    :  Tuple	,					**_UpperCAmelCase    :  List[str]							)		->						Any:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		int            =				["tokenizers"]





							def __init__(					self    :  Optional[Any]	,					*_UpperCAmelCase    :  int	,					**_UpperCAmelCase    :  Optional[int]							)		->						Dict:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Tuple            =				["tokenizers"]





							def __init__(					self    :  Optional[int]	,					*_UpperCAmelCase    :  List[str]	,					**_UpperCAmelCase    :  List[Any]							)		->						List[str]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Any            =				["tokenizers"]





							def __init__(					self    :  Optional[int]	,					*_UpperCAmelCase    :  Any	,					**_UpperCAmelCase    :  Tuple							)		->						Tuple:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Optional[int]            =				["tokenizers"]





							def __init__(					self    :  Any	,					*_UpperCAmelCase    :  Optional[int]	,					**_UpperCAmelCase    :  Any							)		->						int:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Tuple            =				["tokenizers"]





							def __init__(					self    :  Tuple	,					*_UpperCAmelCase    :  Any	,					**_UpperCAmelCase    :  Any							)		->						List[str]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		int            =				["tokenizers"]





							def __init__(					self    :  int	,					*_UpperCAmelCase    :  Any	,					**_UpperCAmelCase    :  Any							)		->						str:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		List[Any]            =				["tokenizers"]





							def __init__(					self    :  int	,					*_UpperCAmelCase    :  List[Any]	,					**_UpperCAmelCase    :  Any							)		->						Any:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Optional[int]            =				["tokenizers"]





							def __init__(					self    :  List[Any]	,					*_UpperCAmelCase    :  Any	,					**_UpperCAmelCase    :  Dict							)		->						Optional[Any]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Optional[Any]            =				["tokenizers"]





							def __init__(					self    :  Optional[int]	,					*_UpperCAmelCase    :  List[Any]	,					**_UpperCAmelCase    :  Optional[int]							)		->						List[str]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Any            =				["tokenizers"]





							def __init__(					self    :  Tuple	,					*_UpperCAmelCase    :  List[str]	,					**_UpperCAmelCase    :  Optional[int]							)		->						List[str]:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Any            =				["tokenizers"]





							def __init__(					self    :  List[str]	,					*_UpperCAmelCase    :  Optional[Any]	,					**_UpperCAmelCase    :  str							)		->						int:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Dict            =				["tokenizers"]





							def __init__(					self    :  Optional[Any]	,					*_UpperCAmelCase    :  Tuple	,					**_UpperCAmelCase    :  str							)		->						Any:



											"""simple docstring"""


											requires_backends(self	,					['tokenizers']							)





