






"""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 	__UpperCamelCase    :
					def __init__(       self       :  int    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  Dict    ,   UpperCAmelCase       :  Tuple      )  ->					int:
											lowerCAmelCase      :Dict				  = cuda
											lowerCAmelCase      :Dict				  = AutoConfig.from_pretrained(UpperCAmelCase      )
											lowerCAmelCase      :Any				  = AutoModelForCTC.from_pretrained(UpperCAmelCase      )
											self.model.eval()
											if self.cuda:
																	self.model.to(device='cuda'      )
											lowerCAmelCase      :Optional[int]				  = AutoProcessor.from_pretrained(UpperCAmelCase      )
											lowerCAmelCase      :str				  = torchaudio.transforms.Resample(UpperCAmelCase    ,   1_6000      )
											lowerCAmelCase      :str				  = 0
											lowerCAmelCase      :Dict				  = list(self.processor.tokenizer.get_vocab().keys()      )
											for i in range(len(UpperCAmelCase      )      ):
																	if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																							lowerCAmelCase      :List[Any]				  = i
											print('Blank Token id [PAD]/<pad>'    ,   UpperCAmelCase      )
											lowerCAmelCase      :Optional[Any]				  = blank_id
					def 					UpperCAmelCase__   (       self       :  Any    ,   UpperCAmelCase       :  List[str]      )  ->					List[Any]:
											lowerCAmelCase  ,   lowerCAmelCase      :Union[str, Any]				  = torchaudio.load(UpperCAmelCase      )
											lowerCAmelCase      :Any				  = self.resampler(UpperCAmelCase      ).squeeze().numpy()
											return speech
					def 					UpperCAmelCase__   (       self       :  List[str]    ,   UpperCAmelCase       :  Dict      )  ->					List[str]:
											lowerCAmelCase      :Union[str, Any]				  = self.blank_id
											lowerCAmelCase      :Optional[int]				  = '|'.join(item['sent'].split(' '      )      )
											if not os.path.isfile(item['wav_path']      ):
																	print(item['wav_path']    ,   'not found in wavs directory'      )

											lowerCAmelCase      :List[Any]				  = self.speech_file_to_array_fn(item['wav_path']      )
											lowerCAmelCase      :Dict				  = self.processor(UpperCAmelCase    ,   sampling_rate=1_6000    ,   return_tensors='pt'    ,   padding=UpperCAmelCase      )
											if self.cuda:
																	lowerCAmelCase      :str				  = inputs.to(device='cuda'      )

											with torch.no_grad():
																	lowerCAmelCase      :List[Any]				  = self.model(inputs.input_values      ).logits

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

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

											lowerCAmelCase      :List[str]				  = {c: i for i, c in enumerate(UpperCAmelCase      )}
											lowerCAmelCase      :Optional[Any]				  = []
											for c in transcript:
																	if c in dictionary:
																							tokens.append(dictionary[c]      )

											def get_trellis(UpperCAmelCase       :  Any    ,   UpperCAmelCase       :  Tuple    ,   UpperCAmelCase       :  Dict=0      ):
																	lowerCAmelCase      :Optional[Any]				  = emission.size(0      )
																	lowerCAmelCase      :Any				  = len(UpperCAmelCase      )

																	# 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.
																	lowerCAmelCase      :str				  = torch.full((num_frames + 1, num_tokens + 1)    ,   -float('inf'      )      )
																	lowerCAmelCase      :int				  = 0
																	for t in range(UpperCAmelCase      ):
																							lowerCAmelCase      :Union[str, Any]				  = torch.maximum(
																							    # Score for staying at the same token
																							    trellis[t, 1:] + emission[t, blank_id]    ,   trellis[t, :-1] + emission[t, tokens]    ,   )
																	return trellis

											lowerCAmelCase      :Union[str, Any]				  = get_trellis(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      )




											@dataclass
											class 	__UpperCamelCase    :
																lowercase_						:							int
																lowercase_						:							int
																lowercase_						:							float

											def backtrack(UpperCAmelCase       :  Tuple    ,   UpperCAmelCase       :  Optional[Any]    ,   UpperCAmelCase       :  Optional[int]    ,   UpperCAmelCase       :  Optional[Any]=0      ):
																	lowerCAmelCase      :List[str]				  = trellis.size(1      ) - 1
																	lowerCAmelCase      :List[Any]				  = torch.argmax(trellis[:, j]      ).item()

																	lowerCAmelCase      :Any				  = []
																	for t in range(UpperCAmelCase    ,   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.
																							lowerCAmelCase      :str				  = trellis[t - 1, j] + emission[t - 1, blank_id]
																							# Score for token changing from C-1 at T-1 to J at T.
																							lowerCAmelCase      :Optional[Any]				  = trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

																							# 2. Store the path with frame-wise probability.
																							lowerCAmelCase      :Any				  = 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    ,   UpperCAmelCase      )      )

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

											lowerCAmelCase      :Tuple				  = backtrack(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      )




											@dataclass
											class 	__UpperCamelCase    :
																lowercase_						:							str
																lowercase_						:							int
																lowercase_						:							int
																lowercase_						:							float
																def __repr__(       self       :  Any      )  ->					Optional[int]:
																						return f"""{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"""
																@property
																def 					UpperCAmelCase__   (       self       :  str      )  ->					Union[str, Any]:
																						return self.end - self.start

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

											lowerCAmelCase      :Tuple				  = merge_repeats(UpperCAmelCase      )
											with open(item['out_path']    ,   'w'      ) as out_align:
																	for seg in segments:
																							out_align.write(str(UpperCAmelCase      ) + '\n'      )
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  Optional[int]    ,   UpperCAmelCase       :  Tuple      )  ->					Optional[Any]:
											if not os.path.exists(UpperCAmelCase      ):
																	os.makedirs(UpperCAmelCase      )

											# load text file
											lowerCAmelCase      :int				  = open(UpperCAmelCase    ,   encoding='utf8'      ).readlines()

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

																	lowerCAmelCase  ,   lowerCAmelCase      :Any				  = line.strip().split('\t'      )
																	lowerCAmelCase      :str				  = os.path.join(UpperCAmelCase    ,   wav_name + '.wav'      )
																	lowerCAmelCase      :Union[str, Any]				  = os.path.join(UpperCAmelCase    ,   wav_name + '.txt'      )

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

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


def 					UpperCAmelCase			(				):





						'''simple docstring'''






						lowerCAmelCase      :Optional[int]				  = 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=1_60_00    ,			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'   )

						lowerCAmelCase      :Union[str, Any]				  = parser.parse_args()

						lowerCAmelCase      :Tuple				  = 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
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE      =      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE      =      {
    'src_vocab_file': 'vocab-src.json',
    'tgt_vocab_file': 'vocab-tgt.json',
    'merges_file': 'merges.txt',
}

__SCREAMING_SNAKE_CASE      =      {
    'src_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'
    },
    'tgt_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'
    },
    'merges_file': {'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'},
}

__SCREAMING_SNAKE_CASE      =      {'stas/tiny-wmt19-en-de': 10_24}
__SCREAMING_SNAKE_CASE      =      {
    'stas/tiny-wmt19-en-de': {
        'langs': ['en', 'de'],
        'model_max_length': 10_24,
        'special_tokens_map_file': None,
        'full_tokenizer_file': None,
    }
}


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :int				  = set()
						lowerCAmelCase      :Optional[Any]				  = word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)   )
												lowerCAmelCase      :int				  = char
						return pairs


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :List[str]				  = text.replace('，'    ,			','   )
						lowerCAmelCase      :str				  = re.sub(R'。\s*'    ,			'. '    ,			a__   )
						lowerCAmelCase      :int				  = text.replace('、'    ,			','   )
						lowerCAmelCase      :str				  = text.replace('”'    ,			'"'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('“'    ,			'"'   )
						lowerCAmelCase      :List[Any]				  = text.replace('∶'    ,			':'   )
						lowerCAmelCase      :List[Any]				  = text.replace('：'    ,			':'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('？'    ,			'?'   )
						lowerCAmelCase      :Tuple				  = text.replace('《'    ,			'"'   )
						lowerCAmelCase      :Dict				  = text.replace('》'    ,			'"'   )
						lowerCAmelCase      :List[str]				  = text.replace('）'    ,			')'   )
						lowerCAmelCase      :Dict				  = text.replace('！'    ,			'!'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('（'    ,			'('   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('；'    ,			';'   )
						lowerCAmelCase      :Any				  = text.replace('１'    ,			'1'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('」'    ,			'"'   )
						lowerCAmelCase      :Any				  = text.replace('「'    ,			'"'   )
						lowerCAmelCase      :str				  = text.replace('０'    ,			'0'   )
						lowerCAmelCase      :Dict				  = text.replace('３'    ,			'3'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('２'    ,			'2'   )
						lowerCAmelCase      :str				  = text.replace('５'    ,			'5'   )
						lowerCAmelCase      :Tuple				  = text.replace('６'    ,			'6'   )
						lowerCAmelCase      :Optional[int]				  = text.replace('９'    ,			'9'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('７'    ,			'7'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('８'    ,			'8'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('４'    ,			'4'   )
						lowerCAmelCase      :int				  = re.sub(R'．\s*'    ,			'. '    ,			a__   )
						lowerCAmelCase      :List[Any]				  = text.replace('～'    ,			'~'   )
						lowerCAmelCase      :int				  = text.replace('’'    ,			'\''   )
						lowerCAmelCase      :Dict				  = text.replace('…'    ,			'...'   )
						lowerCAmelCase      :Any				  = text.replace('━'    ,			'-'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('〈'    ,			'<'   )
						lowerCAmelCase      :Any				  = text.replace('〉'    ,			'>'   )
						lowerCAmelCase      :str				  = text.replace('【'    ,			'['   )
						lowerCAmelCase      :Any				  = text.replace('】'    ,			']'   )
						lowerCAmelCase      :int				  = text.replace('％'    ,			'%'   )
						return text


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[int]				  = []
						for char in text:
												lowerCAmelCase      :Optional[Any]				  = unicodedata.category(a__   )
												if cat.startswith('C'   ):
																		continue
												output.append(a__   )
						return "".join(a__   )







class 	__UpperCamelCase    (    UpperCamelCase	):
					lowercase_						:							Dict			=     VOCAB_FILES_NAMES
					lowercase_						:							Any			=     PRETRAINED_VOCAB_FILES_MAP
					lowercase_						:							Dict			=     PRETRAINED_INIT_CONFIGURATION
					lowercase_						:							Optional[Any]			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase_						:							Union[str, Any]			=     ["""input_ids""", """attention_mask"""]
					def __init__(       self       :  int    ,   UpperCAmelCase       :  Tuple=None    ,   UpperCAmelCase       :  Dict=None    ,   UpperCAmelCase       :  Dict=None    ,   UpperCAmelCase       :  Tuple=None    ,   UpperCAmelCase       :  str=False    ,   UpperCAmelCase       :  Optional[int]="<unk>"    ,   UpperCAmelCase       :  List[Any]="<s>"    ,   UpperCAmelCase       :  List[str]="</s>"    ,   UpperCAmelCase       :  Optional[int]="<pad>"    ,   **UpperCAmelCase       :  int    ,   )  ->					int:
											super().__init__(
											    langs=UpperCAmelCase    ,   src_vocab_file=UpperCAmelCase    ,   tgt_vocab_file=UpperCAmelCase    ,   merges_file=UpperCAmelCase    ,   do_lower_case=UpperCAmelCase    ,   unk_token=UpperCAmelCase    ,   bos_token=UpperCAmelCase    ,   sep_token=UpperCAmelCase    ,   pad_token=UpperCAmelCase    ,   **UpperCAmelCase    ,   )

											try:
																	import sacremoses
											except ImportError:
																	raise ImportError(
																	    'You need to install sacremoses to use XLMTokenizer. '
																	    'See https://pypi.org/project/sacremoses/ for installation.'      )

											lowerCAmelCase      :str				  = sacremoses

											lowerCAmelCase      :str				  = src_vocab_file
											lowerCAmelCase      :List[str]				  = tgt_vocab_file
											lowerCAmelCase      :str				  = merges_file
											lowerCAmelCase      :Optional[Any]				  = do_lower_case

											# cache of sm.MosesPunctNormalizer instance
											lowerCAmelCase      :str				  = {}
											# cache of sm.MosesTokenizer instance
											lowerCAmelCase      :Any				  = {}
											lowerCAmelCase      :List[str]				  = {}

											if langs and len(UpperCAmelCase      ) == 2:
																	lowerCAmelCase  ,   lowerCAmelCase      :List[Any]				  = langs
											else:
																	raise ValueError(
																	    f"""arg `langs` needs to be a list of 2 langs, e.g. ['en', 'ru'], but got {langs}. """
																	    'Usually that means that tokenizer can\'t find a mapping for the given model path '
																	    'in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer.'      )

											with open(UpperCAmelCase    ,   encoding='utf-8'      ) as src_vocab_handle:
																	lowerCAmelCase      :Tuple				  = json.load(UpperCAmelCase      )
											with open(UpperCAmelCase    ,   encoding='utf-8'      ) as tgt_vocab_handle:
																	lowerCAmelCase      :str				  = json.load(UpperCAmelCase      )
																	lowerCAmelCase      :int				  = {v: k for k, v in tgt_vocab.items()}
											with open(UpperCAmelCase    ,   encoding='utf-8'      ) as merges_handle:
																	lowerCAmelCase      :Optional[int]				  = merges_handle.read().split('\n'      )[:-1]
											lowerCAmelCase      :List[Any]				  = [tuple(merge.split()[:2]      ) for merge in merges]
											lowerCAmelCase      :int				  = dict(zip(UpperCAmelCase    ,   range(len(UpperCAmelCase      )      )      )      )
											lowerCAmelCase      :str				  = {}
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					Dict[str, int]:
											return self.get_src_vocab()
					@property
					def 					UpperCAmelCase__   (       self       :  Dict      )  ->					int:
											return self.src_vocab_size
					def 					UpperCAmelCase__   (       self       :  int    ,   UpperCAmelCase       :  Tuple    ,   UpperCAmelCase       :  Tuple      )  ->					List[Any]:
											if lang not in self.cache_moses_punct_normalizer:
																	lowerCAmelCase      :str				  = self.sm.MosesPunctNormalizer(lang=UpperCAmelCase      )
																	lowerCAmelCase      :List[str]				  = punct_normalizer
											return self.cache_moses_punct_normalizer[lang].normalize(UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Tuple    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  Optional[int]      )  ->					str:
											if lang not in self.cache_moses_tokenizer:
																	lowerCAmelCase      :Optional[int]				  = self.sm.MosesTokenizer(lang=UpperCAmelCase      )
																	lowerCAmelCase      :Any				  = moses_tokenizer
											return self.cache_moses_tokenizer[lang].tokenize(
											    UpperCAmelCase    ,   aggressive_dash_splits=UpperCAmelCase    ,   return_str=UpperCAmelCase    ,   escape=UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Optional[Any]    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  str      )  ->					List[Any]:
											if lang not in self.cache_moses_detokenizer:
																	lowerCAmelCase      :Any				  = self.sm.MosesDetokenizer(lang=UpperCAmelCase      )
																	lowerCAmelCase      :str				  = moses_detokenizer
											return self.cache_moses_detokenizer[lang].detokenize(UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  List[Any]      )  ->					int:
											lowerCAmelCase      :Dict				  = replace_unicode_punct(UpperCAmelCase      )
											lowerCAmelCase      :Union[str, Any]				  = self.moses_punct_norm(UpperCAmelCase    ,   UpperCAmelCase      )
											lowerCAmelCase      :Dict				  = remove_non_printing_char(UpperCAmelCase      )
											return text
					@property
					def 					UpperCAmelCase__   (       self       :  str      )  ->					Union[str, Any]:
											return len(self.encoder      )
					@property
					def 					UpperCAmelCase__   (       self       :  int      )  ->					Optional[int]:
											return len(self.decoder      )
					def 					UpperCAmelCase__   (       self       :  List[Any]      )  ->					Tuple:
											return dict(self.encoder    ,   **self.added_tokens_encoder      )
					def 					UpperCAmelCase__   (       self       :  Any      )  ->					str:
											return dict(self.decoder    ,   **self.added_tokens_decoder      )
					def 					UpperCAmelCase__   (       self       :  List[str]    ,   UpperCAmelCase       :  List[str]      )  ->					Tuple:
											lowerCAmelCase      :List[str]				  = tuple(token[:-1]      ) + (token[-1] + '</w>',)
											if token in self.cache:
																	return self.cache[token]
											lowerCAmelCase      :Dict				  = get_pairs(UpperCAmelCase      )

											if not pairs:
																	return token + "</w>"

											while True:
																	lowerCAmelCase      :Optional[Any]				  = min(UpperCAmelCase    ,   key=lambda UpperCAmelCase      : self.bpe_ranks.get(UpperCAmelCase    ,   float('inf'      )      )      )
																	if bigram not in self.bpe_ranks:
																							break
																	lowerCAmelCase  ,   lowerCAmelCase      :Tuple				  = bigram
																	lowerCAmelCase      :int				  = []
																	lowerCAmelCase      :Union[str, Any]				  = 0
																	while i < len(UpperCAmelCase      ):
																							try:
																													lowerCAmelCase      :List[Any]				  = word.index(UpperCAmelCase    ,   UpperCAmelCase      )
																							except ValueError:
																													new_word.extend(word[i:]      )
																													break
																							else:
																													new_word.extend(word[i:j]      )
																													lowerCAmelCase      :Any				  = j

																							if word[i] == first and i < len(UpperCAmelCase      ) - 1 and word[i + 1] == second:
																													new_word.append(first + second      )
																													i += 2
																							else:
																													new_word.append(word[i]      )
																													i += 1
																	lowerCAmelCase      :Optional[Any]				  = tuple(UpperCAmelCase      )
																	lowerCAmelCase      :Dict				  = new_word
																	if len(UpperCAmelCase      ) == 1:
																							break
																	else:
																							lowerCAmelCase      :Any				  = get_pairs(UpperCAmelCase      )
											lowerCAmelCase      :Dict				  = ' '.join(UpperCAmelCase      )
											if word == "\n  </w>":
																	lowerCAmelCase      :List[Any]				  = '\n</w>'
											lowerCAmelCase      :Optional[Any]				  = word
											return word
					def 					UpperCAmelCase__   (       self       :  List[Any]    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  List[str]="en"    ,   UpperCAmelCase       :  int=False      )  ->					List[Any]:
											lowerCAmelCase      :Optional[Any]				  = self.src_lang

											if self.do_lower_case:
																	lowerCAmelCase      :Tuple				  = text.lower()

											if bypass_tokenizer:
																	lowerCAmelCase      :List[str]				  = text.split()
											else:
																	lowerCAmelCase      :Tuple				  = self.moses_pipeline(UpperCAmelCase    ,   lang=UpperCAmelCase      )
																	lowerCAmelCase      :Optional[int]				  = self.moses_tokenize(UpperCAmelCase    ,   lang=UpperCAmelCase      )

											lowerCAmelCase      :int				  = []
											for token in text:
																	if token:
																							split_tokens.extend(list(self.bpe(UpperCAmelCase      ).split(' '      )      )      )

											return split_tokens
					def 					UpperCAmelCase__   (       self       :  Dict    ,   UpperCAmelCase       :  Any      )  ->					List[Any]:
											return self.encoder.get(UpperCAmelCase    ,   self.encoder.get(self.unk_token      )      )
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]    ,   UpperCAmelCase       :  Any      )  ->					Dict:
											return self.decoder.get(UpperCAmelCase    ,   self.unk_token      )
					def 					UpperCAmelCase__   (       self       :  List[str]    ,   UpperCAmelCase       :  Optional[Any]      )  ->					int:
											lowerCAmelCase      :Optional[int]				  = [t.replace(' '    ,   ''      ).replace('</w>'    ,   ' '      ) for t in tokens]
											lowerCAmelCase      :Optional[int]				  = ''.join(UpperCAmelCase      ).split()
											# detokenize
											lowerCAmelCase      :Dict				  = self.moses_detokenize(UpperCAmelCase    ,   self.tgt_lang      )
											return text
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  List[int]    ,   UpperCAmelCase       :  Optional[List[int]] = None      )  ->					List[int]:
											lowerCAmelCase      :Union[str, Any]				  = [self.sep_token_id]

											# no bos used in fairseq
											if token_ids_a is None:
																	return token_ids_a + sep
											return token_ids_a + sep + token_ids_a + sep
					def 					UpperCAmelCase__   (       self       :  Dict    ,   UpperCAmelCase       :  List[int]    ,   UpperCAmelCase       :  Optional[List[int]] = None    ,   UpperCAmelCase       :  bool = False      )  ->					List[int]:

											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=UpperCAmelCase    ,   token_ids_a=UpperCAmelCase    ,   already_has_special_tokens=UpperCAmelCase      )
											# no bos used in fairseq
											if token_ids_a is not None:
																	return ([0] * len(UpperCAmelCase      )) + [1] + ([0] * len(UpperCAmelCase      )) + [1]
											return ([0] * len(UpperCAmelCase      )) + [1]
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  List[int]    ,   UpperCAmelCase       :  Optional[List[int]] = None      )  ->					List[int]:
											lowerCAmelCase      :Optional[Any]				  = [self.sep_token_id]

											# no bos used in fairseq
											if token_ids_a is None:
																	return len(token_ids_a + sep      ) * [0]
											return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]
					def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  Optional[str] = None      )  ->					Tuple[str]:
											if not os.path.isdir(UpperCAmelCase      ):
																	logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
																	return

											lowerCAmelCase      :Any				  = os.path.join(
											    UpperCAmelCase    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['src_vocab_file']      )
											lowerCAmelCase      :Tuple				  = os.path.join(
											    UpperCAmelCase    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['tgt_vocab_file']      )
											lowerCAmelCase      :Optional[int]				  = os.path.join(
											    UpperCAmelCase    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

											with open(UpperCAmelCase    ,   'w'    ,   encoding='utf-8'      ) as f:
																	f.write(json.dumps(self.encoder    ,   indent=2    ,   sort_keys=UpperCAmelCase    ,   ensure_ascii=UpperCAmelCase      ) + '\n'      )

											with open(UpperCAmelCase    ,   'w'    ,   encoding='utf-8'      ) as f:
																	lowerCAmelCase      :int				  = {v: k for k, v in self.decoder.items()}
																	f.write(json.dumps(UpperCAmelCase    ,   indent=2    ,   sort_keys=UpperCAmelCase    ,   ensure_ascii=UpperCAmelCase      ) + '\n'      )

											lowerCAmelCase      :Optional[Any]				  = 0
											with open(UpperCAmelCase    ,   'w'    ,   encoding='utf-8'      ) as writer:
																	for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,   key=lambda UpperCAmelCase      : kv[1]      ):
																							if index != token_index:
																													logger.warning(
																													    f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
																													    ' Please check that the tokenizer is not corrupted!'      )
																													lowerCAmelCase      :List[Any]				  = token_index
																							writer.write(' '.join(UpperCAmelCase      ) + '\n'      )
																							index += 1

											return src_vocab_file, tgt_vocab_file, merges_file
					def __getstate__(       self       :  Union[str, Any]      )  ->					int:
											lowerCAmelCase      :Any				  = self.__dict__.copy()
											lowerCAmelCase      :Tuple				  = None
											return state
					def __setstate__(       self       :  Any    ,   UpperCAmelCase       :  Union[str, Any]      )  ->					List[Any]:
											lowerCAmelCase      :Tuple				  = d

											try:
																	import sacremoses
											except ImportError:
																	raise ImportError(
																	    'You need to install sacremoses to use XLMTokenizer. '
																	    'See https://pypi.org/project/sacremoses/ for installation.'      )

											lowerCAmelCase      :str				  = sacremoses