


import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase=0.9_99   ,						_lowerCAmelCase="cosine"   ,						)    ->						Tuple:


						'''simple docstring'''






						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_lowerCAmelCase			):
																		return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2			) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(_lowerCAmelCase			):
																		return math.exp(t * -12.0			)

						else:
												raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''			)

						__snake_case				=		[]
						for i in range(_lowerCAmelCase			):
												__snake_case				=		i / num_diffusion_timesteps
												__snake_case				=		(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_lowerCAmelCase			) / alpha_bar_fn(_lowerCAmelCase			)   ,						_lowerCAmelCase			)			)
						return torch.tensor(_lowerCAmelCase   ,						dtype=torch.floataa			)






class      UpperCamelCase(	_a  ,				_a ):
					snake_case_		:					Optional[Any]    					=						[e.name for e in KarrasDiffusionSchedulers]
					snake_case_		:					Union[str, Any]    					=						2


					@register_to_config
					def __init__(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		int = 1_0_0_0  ,     SCREAMING_SNAKE_CASE						:		float = 0.00085  ,     SCREAMING_SNAKE_CASE						:		float = 0.012  ,     SCREAMING_SNAKE_CASE						:		str = "linear"  ,     SCREAMING_SNAKE_CASE						:		Optional[Union[np.ndarray, List[float]]] = None  ,     SCREAMING_SNAKE_CASE						:		str = "epsilon"  ,     SCREAMING_SNAKE_CASE						:		Optional[bool] = False  ,     SCREAMING_SNAKE_CASE						:		Optional[bool] = False  ,     SCREAMING_SNAKE_CASE						:		float = 1.0  ,     SCREAMING_SNAKE_CASE						:		str = "linspace"  ,     SCREAMING_SNAKE_CASE						:		int = 0  ,     )      ->						List[Any]:




											'''simple docstring'''

											if trained_betas is not None:
																	__snake_case				=		torch.tensor(SCREAMING_SNAKE_CASE  ,     dtype=torch.floataa    )
											elif beta_schedule == "linear":
																	__snake_case				=		torch.linspace(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     dtype=torch.floataa    )
											elif beta_schedule == "scaled_linear":
																	# this schedule is very specific to the latent diffusion model.
																	__snake_case				=		(
																	    torch.linspace(beta_start**0.5  ,     beta_end**0.5  ,     SCREAMING_SNAKE_CASE  ,     dtype=torch.floataa    ) ** 2
																	)
											elif beta_schedule == "squaredcos_cap_v2":
																	# Glide cosine schedule
																	__snake_case				=		betas_for_alpha_bar(SCREAMING_SNAKE_CASE  ,     alpha_transform_type="cosine"    )
											elif beta_schedule == "exp":
																	__snake_case				=		betas_for_alpha_bar(SCREAMING_SNAKE_CASE  ,     alpha_transform_type="exp"    )
											else:
																	raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''    )

											__snake_case				=		1.0 - self.betas
											__snake_case				=		torch.cumprod(self.alphas  ,     dim=0    )

											#  set all values
											self.set_timesteps(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )
											__snake_case				=		use_karras_sigmas


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any  ,     SCREAMING_SNAKE_CASE						:		Dict  ,     SCREAMING_SNAKE_CASE						:		str=None    )      ->						Optional[int]:




											'''simple docstring'''

											if schedule_timesteps is None:
																	__snake_case				=		self.timesteps

											__snake_case				=		(schedule_timesteps == timestep).nonzero()

											# The sigma index that is taken for the **very** first `step`
											# is always the second index (or the last index if there is only 1)
											# This way we can ensure we don't accidentally skip a sigma in
											# case we start in the middle of the denoising schedule (e.g. for image-to-image)
											if len(self._index_counter    ) == 0:
																	__snake_case				=		1 if len(SCREAMING_SNAKE_CASE    ) > 1 else 0
											else:
																	__snake_case				=		timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE    ) else timestep
																	__snake_case				=		self._index_counter[timestep_int]

											return indices[pos].item()


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Optional[int]:




											'''simple docstring'''

											if self.config.timestep_spacing in ["linspace", "trailing"]:
																	return self.sigmas.max()

											return (self.sigmas.max() ** 2 + 1) ** 0.5


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		torch.FloatTensor  ,     SCREAMING_SNAKE_CASE						:		Union[float, torch.FloatTensor]  ,     )      ->						torch.FloatTensor:




											'''simple docstring'''

											__snake_case				=		self.index_for_timestep(SCREAMING_SNAKE_CASE    )

											__snake_case				=		self.sigmas[step_index]
											__snake_case				=		sample / ((sigma**2 + 1) ** 0.5)
											return sample


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		Union[str, torch.device] = None  ,     SCREAMING_SNAKE_CASE						:		Optional[int] = None  ,     )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		num_inference_steps

											__snake_case				=		num_train_timesteps or self.config.num_train_timesteps

											# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
											if self.config.timestep_spacing == "linspace":
																	__snake_case				=		np.linspace(0  ,     num_train_timesteps - 1  ,     SCREAMING_SNAKE_CASE  ,     dtype=SCREAMING_SNAKE_CASE    )[::-1].copy()
											elif self.config.timestep_spacing == "leading":
																	__snake_case				=		num_train_timesteps // self.num_inference_steps
																	# creates integer timesteps by multiplying by ratio
																	# casting to int to avoid issues when num_inference_step is power of 3
																	__snake_case				=		(np.arange(0  ,     SCREAMING_SNAKE_CASE    ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE    )
																	timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
																	__snake_case				=		num_train_timesteps / self.num_inference_steps
																	# creates integer timesteps by multiplying by ratio
																	# casting to int to avoid issues when num_inference_step is power of 3
																	__snake_case				=		(np.arange(SCREAMING_SNAKE_CASE  ,     0  ,     -step_ratio    )).round().copy().astype(SCREAMING_SNAKE_CASE    )
																	timesteps -= 1
											else:
																	raise ValueError(
																	    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''    )

											__snake_case				=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
											__snake_case				=		np.log(SCREAMING_SNAKE_CASE    )
											__snake_case				=		np.interp(SCREAMING_SNAKE_CASE  ,     np.arange(0  ,     len(SCREAMING_SNAKE_CASE    )    )  ,     SCREAMING_SNAKE_CASE    )

											if self.config.use_karras_sigmas:
																	__snake_case				=		self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE  ,     num_inference_steps=self.num_inference_steps    )
																	__snake_case				=		np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    ) for sigma in sigmas]    )

											__snake_case				=		np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
											__snake_case				=		torch.from_numpy(SCREAMING_SNAKE_CASE    ).to(device=SCREAMING_SNAKE_CASE    )
											__snake_case				=		torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2    ), sigmas[-1:]]    )

											__snake_case				=		torch.from_numpy(SCREAMING_SNAKE_CASE    )
											__snake_case				=		torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2    )]    )

											if str(SCREAMING_SNAKE_CASE    ).startswith("mps"    ):
																	# mps does not support float64
																	__snake_case				=		timesteps.to(SCREAMING_SNAKE_CASE  ,     dtype=torch.floataa    )
											else:
																	__snake_case				=		timesteps.to(device=SCREAMING_SNAKE_CASE    )

											# empty dt and derivative
											__snake_case				=		None
											__snake_case				=		None

											# for exp beta schedules, such as the one for `pipeline_shap_e.py`
											# we need an index counter
											__snake_case				=		defaultdict(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any  ,     SCREAMING_SNAKE_CASE						:		Any  ,     SCREAMING_SNAKE_CASE						:		List[str]    )      ->						Any:




											'''simple docstring'''

											__snake_case				=		np.log(SCREAMING_SNAKE_CASE    )

											# get distribution
											__snake_case				=		log_sigma - log_sigmas[:, np.newaxis]

											# get sigmas range
											__snake_case				=		np.cumsum((dists >= 0)  ,     axis=0    ).argmax(axis=0    ).clip(max=log_sigmas.shape[0] - 2    )
											__snake_case				=		low_idx + 1

											__snake_case				=		log_sigmas[low_idx]
											__snake_case				=		log_sigmas[high_idx]

											# interpolate sigmas
											__snake_case				=		(low - log_sigma) / (low - high)
											__snake_case				=		np.clip(SCREAMING_SNAKE_CASE  ,     0  ,     1    )

											# transform interpolation to time range
											__snake_case				=		(1 - w) * low_idx + w * high_idx
											__snake_case				=		t.reshape(sigma.shape    )
											return t


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		torch.FloatTensor  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						torch.FloatTensor:




											'''simple docstring'''

											__snake_case				=		in_sigmas[-1].item()
											__snake_case				=		in_sigmas[0].item()

											__snake_case				=		7.0  # 7.0 is the value used in the paper
											__snake_case				=		np.linspace(0  ,     1  ,     SCREAMING_SNAKE_CASE    )
											__snake_case				=		sigma_min ** (1 / rho)
											__snake_case				=		sigma_max ** (1 / rho)
											__snake_case				=		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
											return sigmas


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						Optional[int]:




											'''simple docstring'''

											return self.dt is None


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		Union[torch.FloatTensor, np.ndarray]  ,     SCREAMING_SNAKE_CASE						:		Union[float, torch.FloatTensor]  ,     SCREAMING_SNAKE_CASE						:		Union[torch.FloatTensor, np.ndarray]  ,     SCREAMING_SNAKE_CASE						:		bool = True  ,     )      ->						Union[SchedulerOutput, Tuple]:




											'''simple docstring'''

											__snake_case				=		self.index_for_timestep(SCREAMING_SNAKE_CASE    )

											# advance index counter by 1
											__snake_case				=		timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE    ) else timestep
											self._index_counter[timestep_int] += 1

											if self.state_in_first_order:
																	__snake_case				=		self.sigmas[step_index]
																	__snake_case				=		self.sigmas[step_index + 1]
											else:
																	# 2nd order / Heun's method
																	__snake_case				=		self.sigmas[step_index - 1]
																	__snake_case				=		self.sigmas[step_index]

											# currently only gamma=0 is supported. This usually works best anyways.
											# We can support gamma in the future but then need to scale the timestep before
											# passing it to the model which requires a change in API
											__snake_case				=		0
											__snake_case				=		sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											if self.config.prediction_type == "epsilon":
																	__snake_case				=		sigma_hat if self.state_in_first_order else sigma_next
																	__snake_case				=		sample - sigma_input * model_output
											elif self.config.prediction_type == "v_prediction":
																	__snake_case				=		sigma_hat if self.state_in_first_order else sigma_next
																	__snake_case				=		model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																	    sample / (sigma_input**2 + 1)
																	)
											elif self.config.prediction_type == "sample":
																	__snake_case				=		model_output
											else:
																	raise ValueError(
																	    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''    )

											if self.config.clip_sample:
																	__snake_case				=		pred_original_sample.clamp(
																	    -self.config.clip_sample_range  ,     self.config.clip_sample_range    )

											if self.state_in_first_order:
																	# 2. Convert to an ODE derivative for 1st order
																	__snake_case				=		(sample - pred_original_sample) / sigma_hat
																	# 3. delta timestep
																	__snake_case				=		sigma_next - sigma_hat

																	# store for 2nd order step
																	__snake_case				=		derivative
																	__snake_case				=		dt
																	__snake_case				=		sample
											else:
																	# 2. 2nd order / Heun's method
																	__snake_case				=		(sample - pred_original_sample) / sigma_next
																	__snake_case				=		(self.prev_derivative + derivative) / 2

																	# 3. take prev timestep & sample
																	__snake_case				=		self.dt
																	__snake_case				=		self.sample

																	# free dt and derivative
																	# Note, this puts the scheduler in "first order mode"
																	__snake_case				=		None
																	__snake_case				=		None
																	__snake_case				=		None

											__snake_case				=		sample + derivative * dt

											if not return_dict:
																	return (prev_sample,)

											return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		torch.FloatTensor  ,     SCREAMING_SNAKE_CASE						:		torch.FloatTensor  ,     SCREAMING_SNAKE_CASE						:		torch.FloatTensor  ,     )      ->						torch.FloatTensor:




											'''simple docstring'''

											__snake_case				=		self.sigmas.to(device=original_samples.device  ,     dtype=original_samples.dtype    )
											if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE    ):
																	# mps does not support float64
																	__snake_case				=		self.timesteps.to(original_samples.device  ,     dtype=torch.floataa    )
																	__snake_case				=		timesteps.to(original_samples.device  ,     dtype=torch.floataa    )
											else:
																	__snake_case				=		self.timesteps.to(original_samples.device    )
																	__snake_case				=		timesteps.to(original_samples.device    )

											__snake_case				=		[self.index_for_timestep(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    ) for t in timesteps]

											__snake_case				=		sigmas[step_indices].flatten()
											while len(sigma.shape    ) < len(original_samples.shape    ):
																	__snake_case				=		sigma.unsqueeze(-1    )

											__snake_case				=		original_samples + noise * sigma
											return noisy_samples






					def __len__(    self						:		Any    )      ->						Tuple:




											'''simple docstring'''

											return self.config.num_train_timesteps









import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


A				:       List[str]      =  logging.get_logger(__name__)

A				:       List[Any]      =  {'vocab_file': 'spiece.model'}

A				:       Tuple      =  {
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

A				:       Any      =  {
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
A				:       Tuple      =  0
A				:       str      =  1
A				:       str      =  2
A				:       Union[str, Any]      =  3
A				:       Optional[Any]      =  4






class      UpperCamelCase(	_a ):
					snake_case_		:					Union[str, Any]    					=						VOCAB_FILES_NAMES
					snake_case_		:					List[Any]    					=						PRETRAINED_VOCAB_FILES_MAP
					snake_case_		:					str    					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					snake_case_		:					Tuple    					=						"""left"""


					def __init__(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		Any=False  ,     SCREAMING_SNAKE_CASE						:		List[Any]=True  ,     SCREAMING_SNAKE_CASE						:		Dict=False  ,     SCREAMING_SNAKE_CASE						:		Tuple="<s>"  ,     SCREAMING_SNAKE_CASE						:		Optional[int]="</s>"  ,     SCREAMING_SNAKE_CASE						:		List[str]="<unk>"  ,     SCREAMING_SNAKE_CASE						:		List[str]="<sep>"  ,     SCREAMING_SNAKE_CASE						:		Optional[int]="<pad>"  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]="<cls>"  ,     SCREAMING_SNAKE_CASE						:		str="<mask>"  ,     SCREAMING_SNAKE_CASE						:		int=["<eop>", "<eod>"]  ,     SCREAMING_SNAKE_CASE						:		Optional[Dict[str, Any]] = None  ,     **SCREAMING_SNAKE_CASE						:		Dict  ,     )      ->						None:




											'''simple docstring'''

											__snake_case				=		AddedToken(SCREAMING_SNAKE_CASE  ,     lstrip=SCREAMING_SNAKE_CASE  ,     rstrip=SCREAMING_SNAKE_CASE    ) if isinstance(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    ) else mask_token

											__snake_case				=		{} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    do_lower_case=SCREAMING_SNAKE_CASE  ,     remove_space=SCREAMING_SNAKE_CASE  ,     keep_accents=SCREAMING_SNAKE_CASE  ,     bos_token=SCREAMING_SNAKE_CASE  ,     eos_token=SCREAMING_SNAKE_CASE  ,     unk_token=SCREAMING_SNAKE_CASE  ,     sep_token=SCREAMING_SNAKE_CASE  ,     pad_token=SCREAMING_SNAKE_CASE  ,     cls_token=SCREAMING_SNAKE_CASE  ,     mask_token=SCREAMING_SNAKE_CASE  ,     additional_special_tokens=SCREAMING_SNAKE_CASE  ,     sp_model_kwargs=self.sp_model_kwargs  ,     **SCREAMING_SNAKE_CASE  ,     )

											__snake_case				=		3

											__snake_case				=		do_lower_case
											__snake_case				=		remove_space
											__snake_case				=		keep_accents
											__snake_case				=		vocab_file

											__snake_case				=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
											self.sp_model.Load(SCREAMING_SNAKE_CASE    )


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Union[str, Any]:




											'''simple docstring'''

											return len(self.sp_model    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE    ): i for i in range(self.vocab_size    )}
											vocab.update(self.added_tokens_encoder    )
											return vocab


					def __getstate__(    self						:		Any    )      ->						str:




											'''simple docstring'''

											__snake_case				=		self.__dict__.copy()
											__snake_case				=		None
											return state


					def __setstate__(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		Optional[int]    )      ->						int:




											'''simple docstring'''

											__snake_case				=		d

											# for backward compatibility
											if not hasattr(self  ,     "sp_model_kwargs"    ):
																	__snake_case				=		{}

											__snake_case				=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
											self.sp_model.Load(self.vocab_file    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		List[Any]    )      ->						int:




											'''simple docstring'''

											if self.remove_space:
																	__snake_case				=		" ".join(inputs.strip().split()    )
											else:
																	__snake_case				=		inputs
											__snake_case				=		outputs.replace("``"  ,     "\""    ).replace("''"  ,     "\""    )

											if not self.keep_accents:
																	__snake_case				=		unicodedata.normalize("NFKD"  ,     SCREAMING_SNAKE_CASE    )
																	__snake_case				=		"".join([c for c in outputs if not unicodedata.combining(SCREAMING_SNAKE_CASE    )]    )
											if self.do_lower_case:
																	__snake_case				=		outputs.lower()

											return outputs


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		self.preprocess_text(SCREAMING_SNAKE_CASE    )
											__snake_case				=		self.sp_model.encode(SCREAMING_SNAKE_CASE  ,     out_type=SCREAMING_SNAKE_CASE    )
											__snake_case				=		[]
											for piece in pieces:
																	if len(SCREAMING_SNAKE_CASE    ) > 1 and piece[-1] == str(","    ) and piece[-2].isdigit():
																							__snake_case				=		self.sp_model.EncodeAsPieces(piece[:-1].replace(SCREAMING_SNAKE_CASE  ,     ""    )    )
																							if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																													if len(cur_pieces[0]    ) == 1:
																																			__snake_case				=		cur_pieces[1:]
																													else:
																																			__snake_case				=		cur_pieces[0][1:]
																							cur_pieces.append(piece[-1]    )
																							new_pieces.extend(SCREAMING_SNAKE_CASE    )
																	else:
																							new_pieces.append(SCREAMING_SNAKE_CASE    )

											return new_pieces


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Optional[int]    )      ->						Optional[Any]:




											'''simple docstring'''

											return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]    )      ->						Optional[int]:




											'''simple docstring'''

											return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		int    )      ->						List[Any]:




											'''simple docstring'''

											__snake_case				=		"".join(SCREAMING_SNAKE_CASE    ).replace(SCREAMING_SNAKE_CASE  ,     " "    ).strip()
											return out_string


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		List[int]  ,     SCREAMING_SNAKE_CASE						:		bool = False  ,     SCREAMING_SNAKE_CASE						:		bool = None  ,     SCREAMING_SNAKE_CASE						:		bool = True  ,     **SCREAMING_SNAKE_CASE						:		Optional[int]  ,     )      ->						str:




											'''simple docstring'''

											__snake_case				=		kwargs.pop("use_source_tokenizer"  ,     SCREAMING_SNAKE_CASE    )

											__snake_case				=		self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE  ,     skip_special_tokens=SCREAMING_SNAKE_CASE    )

											# To avoid mixing byte-level and unicode for byte-level BPT
											# we need to build string separately for added tokens and byte-level tokens
											# cf. https://github.com/huggingface/transformers/issues/1133
											__snake_case				=		[]
											__snake_case				=		[]
											for token in filtered_tokens:
																	if skip_special_tokens and token in self.all_special_ids:
																							continue
																	if token in self.added_tokens_encoder:
																							if current_sub_text:
																													sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE    )    )
																													__snake_case				=		[]
																							sub_texts.append(SCREAMING_SNAKE_CASE    )
																	else:
																							current_sub_text.append(SCREAMING_SNAKE_CASE    )
											if current_sub_text:
																	sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE    )    )

											# Mimic the behavior of the Rust tokenizer:
											# By default, there are no spaces between special tokens
											__snake_case				=		"".join(SCREAMING_SNAKE_CASE    )

											__snake_case				=		(
											    clean_up_tokenization_spaces
											    if clean_up_tokenization_spaces is not None
											    else self.clean_up_tokenization_spaces
											)
											if clean_up_tokenization_spaces:
																	__snake_case				=		self.clean_up_tokenization(SCREAMING_SNAKE_CASE    )
																	return clean_text
											else:
																	return text


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		List[int]  ,     SCREAMING_SNAKE_CASE						:		Optional[List[int]] = None    )      ->						List[int]:




											'''simple docstring'''

											__snake_case				=		[self.sep_token_id]
											__snake_case				=		[self.cls_token_id]
											if token_ids_a is None:
																	return token_ids_a + sep + cls
											return token_ids_a + sep + token_ids_a + sep + cls


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		List[int]  ,     SCREAMING_SNAKE_CASE						:		Optional[List[int]] = None  ,     SCREAMING_SNAKE_CASE						:		bool = False    )      ->						List[int]:




											'''simple docstring'''

											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=SCREAMING_SNAKE_CASE  ,     token_ids_a=SCREAMING_SNAKE_CASE  ,     already_has_special_tokens=SCREAMING_SNAKE_CASE    )

											if token_ids_a is not None:
																	return ([0] * len(SCREAMING_SNAKE_CASE    )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE    )) + [1, 1]
											return ([0] * len(SCREAMING_SNAKE_CASE    )) + [1, 1]


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		List[int]  ,     SCREAMING_SNAKE_CASE						:		Optional[List[int]] = None    )      ->						List[int]:




											'''simple docstring'''

											__snake_case				=		[self.sep_token_id]
											__snake_case				=		[2]

											if token_ids_a is None:
																	return len(token_ids_a + sep    ) * [0] + cls_segment_id
											return len(token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1] + cls_segment_id






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Optional[str] = None    )      ->						Tuple[str]:




											'''simple docstring'''

											if not os.path.isdir(SCREAMING_SNAKE_CASE    ):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
																	return
											__snake_case				=		os.path.join(
											    SCREAMING_SNAKE_CASE  ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

											if os.path.abspath(self.vocab_file    ) != os.path.abspath(SCREAMING_SNAKE_CASE    ) and os.path.isfile(self.vocab_file    ):
																	copyfile(self.vocab_file  ,     SCREAMING_SNAKE_CASE    )
											elif not os.path.isfile(self.vocab_file    ):
																	with open(SCREAMING_SNAKE_CASE  ,     "wb"    ) as fi:
																							__snake_case				=		self.sp_model.serialized_model_proto()
																							fi.write(SCREAMING_SNAKE_CASE    )

											return (out_vocab_file,)





