



"""simple docstring"""

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		torch.FloatTensor
	_SCREAMING_SNAKE_CASE  :		torch.FloatTensor




def    __snake_case       (  __A			:	str       ,		__A			:	Any=0.9_9_9       ,		__A			:	str="cosine"       ,		)    ->					Dict:






				'''simple docstring'''
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(__A			:	int    ):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2    ) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(__A			:	Union[str, Any]    ):
												return math.exp(t * -1_2.0    )

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

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







class 						lowerCAmelCase__	(  _lowerCamelCase       ,       _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Optional[Any]     	=   1





	@register_to_config
	def __init__(			self							:					Any    ,					_SCREAMING_SNAKE_CASE							:					int = 1_000    ,					_SCREAMING_SNAKE_CASE							:					float = 0.0_0_0_1    ,					_SCREAMING_SNAKE_CASE							:					float = 0.0_2    ,					_SCREAMING_SNAKE_CASE							:					str = "linear"    ,					_SCREAMING_SNAKE_CASE							:					float = 0.0    ,					_SCREAMING_SNAKE_CASE							:					Optional[np.ndarray] = None    ,					_SCREAMING_SNAKE_CASE							:					bool = True    ,					)	->  int:





					"""simple docstring"""


					if trained_betas is not None:
									SCREAMING_SNAKE_CASE	:       int 							=    torch.from_numpy(_SCREAMING_SNAKE_CASE						)
					elif beta_schedule == "linear":
									SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    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.
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    (
									    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
									SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    betas_for_alpha_bar(_SCREAMING_SNAKE_CASE						)
					elif beta_schedule == "sigmoid":
									# GeoDiff sigmoid schedule
									SCREAMING_SNAKE_CASE	:       str 							=    torch.linspace(-6    ,					6    ,					_SCREAMING_SNAKE_CASE						)
									SCREAMING_SNAKE_CASE	:       List[Any] 							=    torch.sigmoid(_SCREAMING_SNAKE_CASE						) * (beta_end - beta_start) + beta_start
					else:
									raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""						)

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    1.0 - self.betas
					SCREAMING_SNAKE_CASE	:       Tuple 							=    torch.cumprod(self.alphas    ,					dim=0						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    torch.tensor(1.0						)

					SCREAMING_SNAKE_CASE	:       Dict 							=    torch.tensor(1.0						)

					# standard deviation of the initial noise distribution
					SCREAMING_SNAKE_CASE	:       Any 							=    1.0

					# setable values
					SCREAMING_SNAKE_CASE	:       str 							=    None
					SCREAMING_SNAKE_CASE	:       Dict 							=    torch.from_numpy(np.arange(0    ,					_SCREAMING_SNAKE_CASE						)[::-1].copy()						)

					SCREAMING_SNAKE_CASE	:       str 							=    eta





	def  _lowerCAmelCase    (			self							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					torch.FloatTensor    ,					_SCREAMING_SNAKE_CASE							:					Optional[int] = None						)	->  torch.FloatTensor:





					"""simple docstring"""


					return sample





	def  _lowerCAmelCase    (			self							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					int = 10    ,					_SCREAMING_SNAKE_CASE							:					int = 10    ,					_SCREAMING_SNAKE_CASE							:					Union[str, torch.device] = None    ,					)	->  Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    min(self.config.num_train_timesteps    ,					_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    num_inference_steps

					SCREAMING_SNAKE_CASE	:       str 							=    []

					SCREAMING_SNAKE_CASE	:       Tuple 							=    {}
					for j in range(0    ,					num_inference_steps - jump_length    ,					_SCREAMING_SNAKE_CASE						):
									SCREAMING_SNAKE_CASE	:       Tuple 							=    jump_n_sample - 1

					SCREAMING_SNAKE_CASE	:       Dict 							=    num_inference_steps
					while t >= 1:
									SCREAMING_SNAKE_CASE	:       Dict 							=    t - 1
									timesteps.append(_SCREAMING_SNAKE_CASE						)

									if jumps.get(_SCREAMING_SNAKE_CASE    ,					0						) > 0:
													SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    jumps[t] - 1
													for _ in range(_SCREAMING_SNAKE_CASE						):
																	SCREAMING_SNAKE_CASE	:       str 							=    t + 1
																	timesteps.append(_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Tuple 							=    np.array(_SCREAMING_SNAKE_CASE						) * (self.config.num_train_timesteps // self.num_inference_steps)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    torch.from_numpy(_SCREAMING_SNAKE_CASE						).to(_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Dict    ,					_SCREAMING_SNAKE_CASE							:					Dict						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    t - self.config.num_train_timesteps // self.num_inference_steps

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.alphas_cumprod[t]
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
					SCREAMING_SNAKE_CASE	:       int 							=    1 - alpha_prod_t
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    1 - alpha_prod_t_prev

					# For t > 0, compute predicted variance βt (see formula (6) and (7) from
					# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
					# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
					# variance to pred_sample
					# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
					# without eta.
					# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

					return variance





	def  _lowerCAmelCase    (			self							:					str    ,					_SCREAMING_SNAKE_CASE							:					torch.FloatTensor    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					torch.FloatTensor    ,					_SCREAMING_SNAKE_CASE							:					torch.FloatTensor    ,					_SCREAMING_SNAKE_CASE							:					torch.FloatTensor    ,					_SCREAMING_SNAKE_CASE							:					Optional[torch.Generator] = None    ,					_SCREAMING_SNAKE_CASE							:					bool = True    ,					)	->  Union[RePaintSchedulerOutput, Tuple]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    timestep
					SCREAMING_SNAKE_CASE	:       str 							=    timestep - self.config.num_train_timesteps // self.num_inference_steps

					# 1. compute alphas, betas
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.alphas_cumprod[t]
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    1 - alpha_prod_t

					# 2. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
					SCREAMING_SNAKE_CASE	:       List[str] 							=    (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
									SCREAMING_SNAKE_CASE	:       Tuple 							=    torch.clamp(_SCREAMING_SNAKE_CASE    ,					-1    ,					1						)

					# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
					# substitute formula (7) in the algorithm coming from DDPM paper
					# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
					# DDIM schedule gives the same results as DDPM with eta = 1.0
					# Noise is being reused in 7. and 8., but no impact on quality has
					# been observed.

					# 5. Add noise
					SCREAMING_SNAKE_CASE	:       int 							=    model_output.device
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    randn_tensor(model_output.shape    ,					generator=_SCREAMING_SNAKE_CASE    ,					device=_SCREAMING_SNAKE_CASE    ,					dtype=model_output.dtype						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.eta * self._get_variance(_SCREAMING_SNAKE_CASE						) ** 0.5

					SCREAMING_SNAKE_CASE	:       int 							=    0
					if t > 0 and self.eta > 0:
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    std_dev_t * noise

					# 6. compute "direction pointing to x_t" of formula (12)
					# from https://arxiv.org/pdf/2010.02502.pdf
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

					# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					SCREAMING_SNAKE_CASE	:       int 							=    alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

					# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

					# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    mask * prev_known_part + (1.0 - mask) * prev_unknown_part

					if not return_dict:
									return (
									    pred_prev_sample,
									    pred_original_sample,
									)

					return RePaintSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE    ,					pred_original_sample=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=None						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    self.config.num_train_timesteps // self.num_inference_steps

					for i in range(_SCREAMING_SNAKE_CASE						):
									SCREAMING_SNAKE_CASE	:       int 							=    self.betas[timestep + i]
									if sample.device.type == "mps":
													# randn does not work reproducibly on mps
													SCREAMING_SNAKE_CASE	:       Optional[int] 							=    randn_tensor(sample.shape    ,					dtype=sample.dtype    ,					generator=_SCREAMING_SNAKE_CASE						)
													SCREAMING_SNAKE_CASE	:       Optional[int] 							=    noise.to(sample.device						)
									else:
													SCREAMING_SNAKE_CASE	:       Dict 							=    randn_tensor(sample.shape    ,					generator=_SCREAMING_SNAKE_CASE    ,					device=sample.device    ,					dtype=sample.dtype						)

									# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
									SCREAMING_SNAKE_CASE	:       str 							=    (1 - beta) ** 0.5 * sample + beta**0.5 * noise

					return sample





	def  _lowerCAmelCase    (			self							:					List[Any]    ,					_SCREAMING_SNAKE_CASE							:					torch.FloatTensor    ,					_SCREAMING_SNAKE_CASE							:					torch.FloatTensor    ,					_SCREAMING_SNAKE_CASE							:					torch.IntTensor    ,					)	->  torch.FloatTensor:





					"""simple docstring"""


					raise NotImplementedError('Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'						)







	def __len__(			self							:					Optional[int]						)	->  List[str]:





					"""simple docstring"""


					return self.config.num_train_timesteps










"""simple docstring"""

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


A_	:						Tuple				=			logging.get_logger(__name__)

A_	:						Optional[Any]				=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

A_	:						Any				=			{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

A_	:						int				=			{
    'allenai/longformer-base-4096': 4096,
    'allenai/longformer-large-4096': 4096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4096,
}
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Optional[int]     	=   VOCAB_FILES_NAMES
	_SCREAMING_SNAKE_CASE  :		List[Any]     	=   PRETRAINED_VOCAB_FILES_MAP
	_SCREAMING_SNAKE_CASE  :		int     	=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_SCREAMING_SNAKE_CASE  :		Union[str, Any]     	=   ['''input_ids''', '''attention_mask''']
	_SCREAMING_SNAKE_CASE  :		Optional[int]     	=   LongformerTokenizer





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





					"""simple docstring"""


					super().__init__(
					    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					tokenizer_file=_SCREAMING_SNAKE_CASE    ,					errors=_SCREAMING_SNAKE_CASE    ,					bos_token=_SCREAMING_SNAKE_CASE    ,					eos_token=_SCREAMING_SNAKE_CASE    ,					sep_token=_SCREAMING_SNAKE_CASE    ,					cls_token=_SCREAMING_SNAKE_CASE    ,					unk_token=_SCREAMING_SNAKE_CASE    ,					pad_token=_SCREAMING_SNAKE_CASE    ,					mask_token=_SCREAMING_SNAKE_CASE    ,					add_prefix_space=_SCREAMING_SNAKE_CASE    ,					trim_offsets=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE    ,					)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
					if pre_tok_state.get('add_prefix_space'    ,					_SCREAMING_SNAKE_CASE						) != add_prefix_space:
									SCREAMING_SNAKE_CASE	:       Dict 							=    getattr(_SCREAMING_SNAKE_CASE    ,					pre_tok_state.pop('type'						)						)
									SCREAMING_SNAKE_CASE	:       Any 							=    add_prefix_space
									SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    pre_tok_class(**_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Dict 							=    add_prefix_space

					SCREAMING_SNAKE_CASE	:       List[str] 							=    'post_processor'
					SCREAMING_SNAKE_CASE	:       str 							=    getattr(self.backend_tokenizer    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)
					if tokenizer_component_instance:
									SCREAMING_SNAKE_CASE	:       Any 							=    json.loads(tokenizer_component_instance.__getstate__()						)

									# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
									if "sep" in state:
													SCREAMING_SNAKE_CASE	:       Optional[int] 							=    tuple(state['sep']						)
									if "cls" in state:
													SCREAMING_SNAKE_CASE	:       Tuple 							=    tuple(state['cls']						)

									SCREAMING_SNAKE_CASE	:       List[str] 							=    False

									if state.get('add_prefix_space'    ,					_SCREAMING_SNAKE_CASE						) != add_prefix_space:
													SCREAMING_SNAKE_CASE	:       Dict 							=    add_prefix_space
													SCREAMING_SNAKE_CASE	:       Dict 							=    True

									if state.get('trim_offsets'    ,					_SCREAMING_SNAKE_CASE						) != trim_offsets:
													SCREAMING_SNAKE_CASE	:       str 							=    trim_offsets
													SCREAMING_SNAKE_CASE	:       int 							=    True

									if changes_to_apply:
													SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    getattr(_SCREAMING_SNAKE_CASE    ,					state.pop('type'						)						)
													SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    component_class(**_SCREAMING_SNAKE_CASE						)
													setattr(self.backend_tokenizer    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)





	@property
	def  _lowerCAmelCase    (			self							:					Dict						)	->  str:





					"""simple docstring"""


					if self._mask_token is None:
									if self.verbose:
													logger.error('Using mask_token, but it is not set yet.'						)
									return None
					return str(self._mask_token						)





	@mask_token.setter
	def  _lowerCAmelCase    (			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					str						)	->  Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    AddedToken(_SCREAMING_SNAKE_CASE    ,					lstrip=_SCREAMING_SNAKE_CASE    ,					rstrip=_SCREAMING_SNAKE_CASE						) if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						) else value
					SCREAMING_SNAKE_CASE	:       Any 							=    value





	def  _lowerCAmelCase    (			self							:					Optional[Any]    ,					*_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					**_SCREAMING_SNAKE_CASE							:					int						)	->  BatchEncoding:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    kwargs.get('is_split_into_words'    ,					_SCREAMING_SNAKE_CASE						)
					assert self.add_prefix_space or not is_split_into_words, (
					    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
					    "to use it with pretokenized inputs."
					)

					return super()._batch_encode_plus(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					List[Any]    ,					*_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					**_SCREAMING_SNAKE_CASE							:					str						)	->  BatchEncoding:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[Any] 							=    kwargs.get('is_split_into_words'    ,					_SCREAMING_SNAKE_CASE						)

					assert self.add_prefix_space or not is_split_into_words, (
					    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
					    "to use it with pretokenized inputs."
					)

					return super()._encode_plus(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Any    ,					_SCREAMING_SNAKE_CASE							:					str    ,					_SCREAMING_SNAKE_CASE							:					Optional[str] = None						)	->  Tuple[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self._tokenizer.model.save(_SCREAMING_SNAKE_CASE    ,					name=_SCREAMING_SNAKE_CASE						)
					return tuple(_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Dict=None						)	->  Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
					if token_ids_a is None:
									return output

					return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







	def  _lowerCAmelCase    (			self							:					Dict    ,					_SCREAMING_SNAKE_CASE							:					List[int]    ,					_SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None						)	->  List[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    [self.sep_token_id]
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    [self.cls_token_id]

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





