



"""simple docstring"""

import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor
class 						lowerCAmelCase__	:


	'''simple docstring'''







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    cuda
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       List[str] 							=    AutoModelForCTC.from_pretrained(_SCREAMING_SNAKE_CASE						)
					self.model.eval()
					if self.cuda:
									self.model.to(device='cuda'						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    AutoProcessor.from_pretrained(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    torchaudio.transforms.Resample(_SCREAMING_SNAKE_CASE    ,					16_000						)
					SCREAMING_SNAKE_CASE	:       str 							=    0
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    list(self.processor.tokenizer.get_vocab().keys()						)
					for i in range(len(_SCREAMING_SNAKE_CASE						)						):
									if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
													SCREAMING_SNAKE_CASE	:       List[str] 							=    i
					print('Blank Token id [PAD]/<pad>'    ,					_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    blank_id





	def  _lowerCAmelCase    (			self							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					List[Any]						)	->  Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    torchaudio.load(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.resampler(_SCREAMING_SNAKE_CASE						).squeeze().numpy()
					return speech





	def  _lowerCAmelCase    (			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					Any						)	->  Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.blank_id
					SCREAMING_SNAKE_CASE	:       List[str] 							=    '|'.join(item['sent'].split(' '						)						)
					if not os.path.isfile(item['wav_path']						):
									print(item['wav_path']    ,					'not found in wavs directory'						)

					SCREAMING_SNAKE_CASE	:       int 							=    self.speech_file_to_array_fn(item['wav_path']						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.processor(_SCREAMING_SNAKE_CASE    ,					sampling_rate=16_000    ,					return_tensors='pt'    ,					padding=_SCREAMING_SNAKE_CASE						)
					if self.cuda:
									SCREAMING_SNAKE_CASE	:       Any 							=    inputs.to(device='cuda'						)

					with torch.no_grad():
									SCREAMING_SNAKE_CASE	:       Dict 							=    self.model(inputs.input_values						).logits

					# get the emission probability at frame level
					SCREAMING_SNAKE_CASE	:       Dict 							=    torch.log_softmax(_SCREAMING_SNAKE_CASE    ,					dim=-1						)
					SCREAMING_SNAKE_CASE	:       List[str] 							=    emissions[0].cpu().detach()

					# get labels from vocab
					SCREAMING_SNAKE_CASE	:       str 							=    ([''] + list(self.processor.tokenizer.get_vocab().keys()						))[
					    :-1
					]  # logits don't align with the tokenizer's vocab

					SCREAMING_SNAKE_CASE	:       List[str] 							=    {c: i for i, c in enumerate(_SCREAMING_SNAKE_CASE						)}
					SCREAMING_SNAKE_CASE	:       Dict 							=    []
					for c in transcript:
									if c in dictionary:
													tokens.append(dictionary[c]						)

					def get_trellis(_SCREAMING_SNAKE_CASE							:					str    ,					_SCREAMING_SNAKE_CASE							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					List[str]=0						):
									SCREAMING_SNAKE_CASE	:       Dict 							=    emission.size(0						)
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    len(_SCREAMING_SNAKE_CASE						)

									# Trellis has extra diemsions for both time axis and tokens.
									# The extra dim for tokens represents <SoS> (start-of-sentence)
									# The extra dim for time axis is for simplification of the code.
									SCREAMING_SNAKE_CASE	:       int 							=    torch.full((num_frames + 1, num_tokens + 1)    ,					-float('inf'						)						)
									SCREAMING_SNAKE_CASE	:       str 							=    0
									for t in range(_SCREAMING_SNAKE_CASE						):
													SCREAMING_SNAKE_CASE	:       Optional[int] 							=    torch.maximum(
													    # Score for staying at the same token
													    trellis[t, 1:] + emission[t, blank_id]    ,					trellis[t, :-1] + emission[t, tokens]    ,					)
									return trellis

					SCREAMING_SNAKE_CASE	:       str 							=    get_trellis(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)
					@dataclass
					class 						lowerCAmelCase__	:


						'''simple docstring'''







						_SCREAMING_SNAKE_CASE  :		int
						_SCREAMING_SNAKE_CASE  :		int
						_SCREAMING_SNAKE_CASE  :		float

					def backtrack(_SCREAMING_SNAKE_CASE							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					str    ,					_SCREAMING_SNAKE_CASE							:					Any    ,					_SCREAMING_SNAKE_CASE							:					Any=0						):
									SCREAMING_SNAKE_CASE	:       Any 							=    trellis.size(1						) - 1
									SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    torch.argmax(trellis[:, j]						).item()

									SCREAMING_SNAKE_CASE	:       str 							=    []
									for t in range(_SCREAMING_SNAKE_CASE    ,					0    ,					-1						):
													# 1. Figure out if the current position was stay or change
													# Note (again):
													# `emission[J-1]` is the emission at time frame `J` of trellis dimension.
													# Score for token staying the same from time frame J-1 to T.
													SCREAMING_SNAKE_CASE	:       Optional[int] 							=    trellis[t - 1, j] + emission[t - 1, blank_id]
													# Score for token changing from C-1 at T-1 to J at T.
													SCREAMING_SNAKE_CASE	:       Dict 							=    trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

													# 2. Store the path with frame-wise probability.
													SCREAMING_SNAKE_CASE	:       int 							=    emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
													# Return token index and time index in non-trellis coordinate.
													path.append(Point(j - 1    ,					t - 1    ,					_SCREAMING_SNAKE_CASE						)						)

													# 3. Update the token
													if changed > stayed:
																	j -= 1
																	if j == 0:
																					break
									else:
													raise ValueError('Failed to align'						)
									return path[::-1]

					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    backtrack(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)
					@dataclass
					class 						lowerCAmelCase__	:


						'''simple docstring'''







						_SCREAMING_SNAKE_CASE  :		str
						_SCREAMING_SNAKE_CASE  :		int
						_SCREAMING_SNAKE_CASE  :		int
						_SCREAMING_SNAKE_CASE  :		float





						def __repr__(			self							:					int						)	->  Tuple:





										"""simple docstring"""


										return f"""{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"""







						@property
						def  _lowerCAmelCase    (			self							:					Any						)	->  Any:





										"""simple docstring"""


										return self.end - self.start

					def merge_repeats(_SCREAMING_SNAKE_CASE							:					Optional[Any]						):
									SCREAMING_SNAKE_CASE	:       int 							=    0, 0
									SCREAMING_SNAKE_CASE	:       str 							=    []
									while ia < len(_SCREAMING_SNAKE_CASE						):
													while ia < len(_SCREAMING_SNAKE_CASE						) and path[ia].token_index == path[ia].token_index:
																	ia += 1
													SCREAMING_SNAKE_CASE	:       Any 							=    sum(path[k].score for k in range(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)						) / (ia - ia)
													segments.append(
													    Segment(
													        transcript[path[ia].token_index]    ,					path[ia].time_index    ,					path[ia - 1].time_index + 1    ,					_SCREAMING_SNAKE_CASE    ,					)						)
													SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    ia
									return segments

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    merge_repeats(_SCREAMING_SNAKE_CASE						)
					with open(item['out_path']    ,					'w'						) as out_align:
									for seg in segments:
													out_align.write(str(_SCREAMING_SNAKE_CASE						) + '\n'						)







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





					"""simple docstring"""


					if not os.path.exists(_SCREAMING_SNAKE_CASE						):
									os.makedirs(_SCREAMING_SNAKE_CASE						)

					# load text file
					SCREAMING_SNAKE_CASE	:       Any 							=    open(_SCREAMING_SNAKE_CASE    ,					encoding='utf8'						).readlines()

					SCREAMING_SNAKE_CASE	:       int 							=    []
					for line in lines:
									if len(line.strip().split('\t'						)						) != 2:
													print('Script must be in format: 00001  this is my sentence'						)
													exit()

									SCREAMING_SNAKE_CASE	:       Tuple 							=    line.strip().split('\t'						)
									SCREAMING_SNAKE_CASE	:       Dict 							=    os.path.join(_SCREAMING_SNAKE_CASE    ,					wav_name + '.wav'						)
									SCREAMING_SNAKE_CASE	:       int 							=    os.path.join(_SCREAMING_SNAKE_CASE    ,					wav_name + '.txt'						)

									items.append({'sent': sentence, 'wav_path': wav_path, 'out_path': out_path}						)
					print('Number of samples found in script file'    ,					len(_SCREAMING_SNAKE_CASE						)						)

					for item in tqdm(_SCREAMING_SNAKE_CASE						):
									self.align_single_sample(_SCREAMING_SNAKE_CASE						)




def    __snake_case       (  )    ->					List[str]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       Tuple 							=    argparse.ArgumentParser()

				parser.add_argument(
				    '--model_name'       ,		type=__A       ,		default='arijitx/wav2vec2-xls-r-300m-bengali'       ,		help='wav2vec model name'    )
				parser.add_argument('--wav_dir'       ,		type=__A       ,		default='./wavs'       ,		help='directory containing wavs'    )
				parser.add_argument('--text_file'       ,		type=__A       ,		default='script.txt'       ,		help='file containing text'    )
				parser.add_argument('--input_wavs_sr'       ,		type=__A       ,		default=16000       ,		help='sampling rate of input audios'    )
				parser.add_argument(
				    '--output_dir'       ,		type=__A       ,		default='./out_alignment'       ,		help='output directory containing the alignment files'    )
				parser.add_argument('--cuda'       ,		action='store_true'    )

				SCREAMING_SNAKE_CASE	:       Optional[int] 							=    parser.parse_args()

				SCREAMING_SNAKE_CASE	:       Any 							=    WavaVecaAligner(args.model_name       ,		args.input_wavs_sr       ,		args.cuda    )
				aligner.align_data(args.wav_dir       ,		args.text_file       ,		args.output_dir    )


if __name__ == "__main__":
							main()











"""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]





