



"""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    A_    :



						"""simple docstring"""






						def __init__(      self				:List[str]     ,						lowercase_				:Dict     ,						lowercase_				:List[Any]     ,						lowercase_				:Dict      )       ->    int:
												UpperCAmelCase    =				cuda
												UpperCAmelCase    =				AutoConfig.from_pretrained(lowercase_      )
												UpperCAmelCase    =				AutoModelForCTC.from_pretrained(lowercase_      )
												self.model.eval()
												if self.cuda:
																		self.model.to(device='cuda'      )
												UpperCAmelCase    =				AutoProcessor.from_pretrained(lowercase_      )
												UpperCAmelCase    =				torchaudio.transforms.Resample(lowercase_     ,						1_60_00      )
												UpperCAmelCase    =				0
												UpperCAmelCase    =				list(self.processor.tokenizer.get_vocab().keys()      )
												for i in range(len(lowercase_      )      ):
																		if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																								UpperCAmelCase    =				i
												print('Blank Token id [PAD]/<pad>'     ,						lowercase_      )
												UpperCAmelCase    =				blank_id






						def 			UpperCAmelCase__  (      self				:Any     ,						lowercase_				:int      )       ->    int:
												UpperCAmelCase   ,					UpperCAmelCase    =				torchaudio.load(lowercase_      )
												UpperCAmelCase    =				self.resampler(lowercase_      ).squeeze().numpy()
												return speech






						def 			UpperCAmelCase__  (      self				:int     ,						lowercase_				:Union[str, Any]      )       ->    List[Any]:
												UpperCAmelCase    =				self.blank_id
												UpperCAmelCase    =				'|'.join(item['sent'].split(' '      )      )
												if not os.path.isfile(item['wav_path']      ):
																		print(item['wav_path']     ,						'not found in wavs directory'      )

												UpperCAmelCase    =				self.speech_file_to_array_fn(item['wav_path']      )
												UpperCAmelCase    =				self.processor(lowercase_     ,						sampling_rate=1_60_00     ,						return_tensors='pt'     ,						padding=lowercase_      )
												if self.cuda:
																		UpperCAmelCase    =				inputs.to(device='cuda'      )

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

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

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

												UpperCAmelCase    =				{c: i for i, c in enumerate(lowercase_      )}
												UpperCAmelCase    =				[]
												for c in transcript:
																		if c in dictionary:
																								tokens.append(dictionary[c]      )

												def get_trellis(lowercase_				:List[str]     ,						lowercase_				:Optional[int]     ,						lowercase_				:Optional[int]=0      ):
																		UpperCAmelCase    =				emission.size(0      )
																		UpperCAmelCase    =				len(lowercase_      )

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

												UpperCAmelCase    =				get_trellis(lowercase_     ,						lowercase_     ,						lowercase_      )







												@dataclass
												class    A_    :



																		"""simple docstring"""
																		__UpperCamelCase	       =							42
																		__UpperCamelCase	       =							42
																		__UpperCamelCase	       =							42

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

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

																								# 2. Store the path with frame-wise probability.
																								UpperCAmelCase    =				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     ,						lowercase_      )      )

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

												UpperCAmelCase    =				backtrack(lowercase_     ,						lowercase_     ,						lowercase_     ,						lowercase_      )







												@dataclass
												class    A_    :



																		"""simple docstring"""
																		__UpperCamelCase	       =							42
																		__UpperCamelCase	       =							42
																		__UpperCamelCase	       =							42
																		__UpperCamelCase	       =							42






																		def __repr__(      self				:int      )       ->    Optional[Any]:
																								return f"""{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"""






																		@property
																		def 			UpperCAmelCase__  (      self				:Tuple      )       ->    Any:
																								return self.end - self.start

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

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






						def 			UpperCAmelCase__  (      self				:Optional[int]     ,						lowercase_				:Any     ,						lowercase_				:int     ,						lowercase_				:Tuple      )       ->    Union[str, Any]:
												if not os.path.exists(lowercase_      ):
																		os.makedirs(lowercase_      )

												# load text file
												UpperCAmelCase    =				open(lowercase_     ,						encoding='utf8'      ).readlines()

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

																		UpperCAmelCase   ,					UpperCAmelCase    =				line.strip().split('\t'      )
																		UpperCAmelCase    =				os.path.join(lowercase_     ,						wav_name + '.wav'      )
																		UpperCAmelCase    =				os.path.join(lowercase_     ,						wav_name + '.txt'      )

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

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


def 				_lowerCAmelCase   (     ):
						UpperCAmelCase    =				argparse.ArgumentParser()

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

						UpperCAmelCase    =				parser.parse_args()

						UpperCAmelCase    =				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 Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


snake_case_         = logging.get_logger(__name__)

snake_case_         = {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

snake_case_         = {
    """vocab_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/vocab.json"""},
    """merges_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/merges.txt"""},
}

snake_case_         = {
    """openai-gpt""": 512,
}

def 				_lowerCAmelCase   (     lowercase_		):
						UpperCAmelCase    =				text.strip()
						if not text:
												return []
						UpperCAmelCase    =				text.split()
						return tokens







class    A_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""






						def __init__(      self				:Dict     ,						lowercase_				:int=True     ,						lowercase_				:Union[str, Any]=None     ,						lowercase_				:List[Any]=True     ,						lowercase_				:List[str]=None     ,						lowercase_				:Optional[int]=True     ,						)       ->    Any:
												if never_split is None:
																		UpperCAmelCase    =				[]
												UpperCAmelCase    =				do_lower_case
												UpperCAmelCase    =				set(lowercase_      )
												UpperCAmelCase    =				tokenize_chinese_chars
												UpperCAmelCase    =				strip_accents
												UpperCAmelCase    =				do_split_on_punc






						def 			UpperCAmelCase__  (      self				:List[str]     ,						lowercase_				:Optional[int]     ,						lowercase_				:int=None      )       ->    List[Any]:
												UpperCAmelCase    =				self.never_split.union(set(lowercase_      )      ) if never_split else self.never_split
												UpperCAmelCase    =				self._clean_text(lowercase_      )

												# This was added on November 1st, 2018 for the multilingual and Chinese
												# models. This is also applied to the English models now, but it doesn't
												# matter since the English models were not trained on any Chinese data
												# and generally don't have any Chinese data in them (there are Chinese
												# characters in the vocabulary because Wikipedia does have some Chinese
												# words in the English Wikipedia.).
												if self.tokenize_chinese_chars:
																		UpperCAmelCase    =				self._tokenize_chinese_chars(lowercase_      )
												# prevents treating the same character with different unicode codepoints as different characters
												UpperCAmelCase    =				unicodedata.normalize('NFC'     ,						lowercase_      )
												UpperCAmelCase    =				whitespace_tokenize(lowercase_      )
												UpperCAmelCase    =				[]
												for token in orig_tokens:
																		if token not in never_split:
																								if self.do_lower_case:
																														UpperCAmelCase    =				token.lower()
																														if self.strip_accents is not False:
																																				UpperCAmelCase    =				self._run_strip_accents(lowercase_      )
																								elif self.strip_accents:
																														UpperCAmelCase    =				self._run_strip_accents(lowercase_      )
																		split_tokens.extend(self._run_split_on_punc(lowercase_     ,						lowercase_      )      )

												UpperCAmelCase    =				whitespace_tokenize(' '.join(lowercase_      )      )
												return output_tokens






						def 			UpperCAmelCase__  (      self				:Tuple     ,						lowercase_				:int      )       ->    Tuple:
												UpperCAmelCase    =				unicodedata.normalize('NFD'     ,						lowercase_      )
												UpperCAmelCase    =				[]
												for char in text:
																		UpperCAmelCase    =				unicodedata.category(lowercase_      )
																		if cat == "Mn":
																								continue
																		output.append(lowercase_      )
												return "".join(lowercase_      )






						def 			UpperCAmelCase__  (      self				:Optional[Any]     ,						lowercase_				:Tuple     ,						lowercase_				:Optional[Any]=None      )       ->    Optional[int]:
												if not self.do_split_on_punc or (never_split is not None and text in never_split):
																		return [text]
												UpperCAmelCase    =				list(lowercase_      )
												UpperCAmelCase    =				0
												UpperCAmelCase    =				True
												UpperCAmelCase    =				[]
												while i < len(lowercase_      ):
																		UpperCAmelCase    =				chars[i]
																		if _is_punctuation(lowercase_      ):
																								output.append([char]      )
																								UpperCAmelCase    =				True
																		else:
																								if start_new_word:
																														output.append([]      )
																								UpperCAmelCase    =				False
																								output[-1].append(lowercase_      )
																		i += 1

												return ["".join(lowercase_      ) for x in output]






						def 			UpperCAmelCase__  (      self				:Optional[int]     ,						lowercase_				:str      )       ->    int:
												UpperCAmelCase    =				[]
												for char in text:
																		UpperCAmelCase    =				ord(lowercase_      )
																		if self._is_chinese_char(lowercase_      ):
																								output.append(' '      )
																								output.append(lowercase_      )
																								output.append(' '      )
																		else:
																								output.append(lowercase_      )
												return "".join(lowercase_      )






						def 			UpperCAmelCase__  (      self				:List[Any]     ,						lowercase_				:int      )       ->    Optional[Any]:
												# This defines a "chinese character" as anything in the CJK Unicode block:
												#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
												#
												# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
												# despite its name. The modern Korean Hangul alphabet is a different block,
												# as is Japanese Hiragana and Katakana. Those alphabets are used to write
												# space-separated words, so they are not treated specially and handled
												# like the all of the other languages.
												if (
												    (cp >= 0x4E00 and cp <= 0x9FFF)
												    or (cp >= 0x3400 and cp <= 0x4DBF)  #
												    or (cp >= 0x20000 and cp <= 0x2A6DF)  #
												    or (cp >= 0x2A700 and cp <= 0x2B73F)  #
												    or (cp >= 0x2B740 and cp <= 0x2B81F)  #
												    or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
												    or (cp >= 0xF900 and cp <= 0xFAFF)
												    or (cp >= 0x2F800 and cp <= 0x2FA1F)  #
												):  #
																		return True

												return False






						def 			UpperCAmelCase__  (      self				:Optional[Any]     ,						lowercase_				:int      )       ->    List[Any]:
												UpperCAmelCase    =				[]
												for char in text:
																		UpperCAmelCase    =				ord(lowercase_      )
																		if cp == 0 or cp == 0xFFFD or _is_control(lowercase_      ):
																								continue
																		if _is_whitespace(lowercase_      ):
																								output.append(' '      )
																		else:
																								output.append(lowercase_      )
												return "".join(lowercase_      )


def 				_lowerCAmelCase   (     lowercase_		):
						UpperCAmelCase    =				set()
						UpperCAmelCase    =				word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)		)
												UpperCAmelCase    =				char
						return pairs

def 				_lowerCAmelCase   (     lowercase_		):
						UpperCAmelCase    =				text.replace('—'	,			'-'		)
						UpperCAmelCase    =				text.replace('–'	,			'-'		)
						UpperCAmelCase    =				text.replace('―'	,			'-'		)
						UpperCAmelCase    =				text.replace('…'	,			'...'		)
						UpperCAmelCase    =				text.replace('´'	,			'\''		)
						UpperCAmelCase    =				re.sub(R'(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'	,			R' \1 '	,			lowercase_		)
						UpperCAmelCase    =				re.sub(R'\s*\n\s*'	,			' \n '	,			lowercase_		)
						UpperCAmelCase    =				re.sub(R'[^\S\n]+'	,			' '	,			lowercase_		)
						return text.strip()







class    A_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""
						__UpperCamelCase	       =							VOCAB_FILES_NAMES
						__UpperCamelCase	       =							PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase	       =							["""input_ids""", """attention_mask"""]






						def __init__(      self				:Dict     ,						lowercase_				:Optional[int]     ,						lowercase_				:Dict     ,						lowercase_				:List[str]="<unk>"     ,						**lowercase_				:Tuple      )       ->    Dict:
												super().__init__(unk_token=lowercase_     ,						**lowercase_      )

												try:
																		import ftfy
																		from spacy.lang.en import English

																		UpperCAmelCase    =				English()
																		UpperCAmelCase    =				_nlp.tokenizer
																		UpperCAmelCase    =				ftfy.fix_text
												except ImportError:
																		logger.warning('ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'      )
																		UpperCAmelCase    =				BasicTokenizer(do_lower_case=lowercase_      )
																		UpperCAmelCase    =				None

												with open(lowercase_     ,						encoding='utf-8'      ) as vocab_handle:
																		UpperCAmelCase    =				json.load(lowercase_      )
												UpperCAmelCase    =				{v: k for k, v in self.encoder.items()}
												with open(lowercase_     ,						encoding='utf-8'      ) as merges_handle:
																		UpperCAmelCase    =				merges_handle.read().split('\n'      )[1:-1]
												UpperCAmelCase    =				[tuple(merge.split()      ) for merge in merges]
												UpperCAmelCase    =				dict(zip(lowercase_     ,						range(len(lowercase_      )      )      )      )
												UpperCAmelCase    =				{}






						@property
						def 			UpperCAmelCase__  (      self				:Union[str, Any]      )       ->    List[Any]:
												return True






						@property
						def 			UpperCAmelCase__  (      self				:str      )       ->    str:
												return len(self.encoder      )






						def 			UpperCAmelCase__  (      self				:Optional[int]      )       ->    Optional[Any]:
												return dict(self.encoder     ,						**self.added_tokens_encoder      )






						def 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:List[Any]      )       ->    int:
												UpperCAmelCase    =				tuple(token[:-1]      ) + (token[-1] + '</w>',)
												if token in self.cache:
																		return self.cache[token]
												UpperCAmelCase    =				get_pairs(lowercase_      )

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

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

																								if word[i] == first and i < len(lowercase_      ) - 1 and word[i + 1] == second:
																														new_word.append(first + second      )
																														i += 2
																								else:
																														new_word.append(word[i]      )
																														i += 1
																		UpperCAmelCase    =				tuple(lowercase_      )
																		UpperCAmelCase    =				new_word
																		if len(lowercase_      ) == 1:
																								break
																		else:
																								UpperCAmelCase    =				get_pairs(lowercase_      )
												UpperCAmelCase    =				' '.join(lowercase_      )
												if word == "\n  </w>":
																		UpperCAmelCase    =				'\n</w>'
												UpperCAmelCase    =				word
												return word






						def 			UpperCAmelCase__  (      self				:List[str]     ,						lowercase_				:Union[str, Any]      )       ->    str:
												UpperCAmelCase    =				[]
												if self.fix_text is None:
																		# Using BERT's BasicTokenizer
																		UpperCAmelCase    =				self.nlp.tokenize(lowercase_      )
																		for token in text:
																								split_tokens.extend(list(self.bpe(lowercase_      ).split(' '      )      )      )
												else:
																		# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
																		UpperCAmelCase    =				self.nlp(text_standardize(self.fix_text(lowercase_      )      )      )
																		for token in text:
																								split_tokens.extend(list(self.bpe(token.text.lower()      ).split(' '      )      )      )
												return split_tokens






						def 			UpperCAmelCase__  (      self				:Tuple     ,						lowercase_				:Optional[Any]      )       ->    Tuple:
												return self.encoder.get(lowercase_     ,						self.encoder.get(self.unk_token      )      )






						def 			UpperCAmelCase__  (      self				:List[Any]     ,						lowercase_				:str      )       ->    Union[str, Any]:
												return self.decoder.get(lowercase_     ,						self.unk_token      )






						def 			UpperCAmelCase__  (      self				:Tuple     ,						lowercase_				:Optional[Any]      )       ->    Tuple:
												UpperCAmelCase    =				''.join(lowercase_      ).replace('</w>'     ,						' '      ).strip()
												return out_string






						def 			UpperCAmelCase__  (      self				:List[str]     ,						lowercase_				:str     ,						lowercase_				:Optional[str] = None      )       ->    Tuple[str]:
												if not os.path.isdir(lowercase_      ):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
																		return
												UpperCAmelCase    =				os.path.join(
												    lowercase_     ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )
												UpperCAmelCase    =				os.path.join(
												    lowercase_     ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

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

												UpperCAmelCase    =				0
												with open(lowercase_     ,						'w'     ,						encoding='utf-8'      ) as writer:
																		writer.write('#version: 0.2\n'      )
																		for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,						key=lambda lowercase_      : kv[1]      ):
																								if index != token_index:
																														logger.warning(
																														    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																														    ' Please check that the tokenizer is not corrupted!'      )
																														UpperCAmelCase    =				token_index
																								writer.write(' '.join(lowercase_      ) + '\n'      )
																								index += 1

												return vocab_file, merge_file





