




'''simple docstring'''






import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
				if is_torch_available():
								import torch
				if is_tf_available():
								import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


_lowerCAmelCase       =   logging.get_logger(__name__)

_lowerCAmelCase       =   {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

_lowerCAmelCase       =   {
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
}

_lowerCAmelCase       =   {
    '''Salesforce/codegen-350M-mono''': 2048,
}

@lru_cache()
def      __lowerCAmelCase   (       ):
			__UpperCamelCase :     Optional[Any]										=  (
			    list(range(ord("!"		)   , ord("~"		) + 1		)		) + list(range(ord("¡"		)   , ord("¬"		) + 1		)		) + list(range(ord("®"		)   , ord("ÿ"		) + 1		)		)
			)
			__UpperCamelCase :     List[str]										=  bs[:]
			__UpperCamelCase :     Union[str, Any]										=  0
			for b in range(2**8		):
						if b not in bs:
									bs.append(snake_case__		)
									cs.append(2**8 + n		)
									n += 1
			__UpperCamelCase :     Optional[Any]										=  [chr(snake_case__		) for n in cs]
			return dict(zip(snake_case__   , snake_case__		)		)

def      __lowerCAmelCase   (       snake_case__		):
			__UpperCamelCase :     Union[str, Any]										=  set()
			__UpperCamelCase :     str										=  word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)		)
						__UpperCamelCase :     List[str]										=  char
			return pairs




class    A						(					SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				A       =							VOCAB_FILES_NAMES
				A       =							PRETRAINED_VOCAB_FILES_MAP
				A       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A       =							["input_ids", "attention_mask"]
				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase="replace"					,    _UpperCAmelCase="<|endoftext|>"					,    _UpperCAmelCase="<|endoftext|>"					,    _UpperCAmelCase="<|endoftext|>"					,    _UpperCAmelCase=None					,    _UpperCAmelCase=False					,    _UpperCAmelCase=False					,    **_UpperCAmelCase					,    )      ->			Optional[int]:
							__UpperCamelCase :     List[Any]										=  AddedToken(_UpperCAmelCase					,    lstrip=_UpperCAmelCase					,    rstrip=_UpperCAmelCase     ) if isinstance(_UpperCAmelCase					,    _UpperCAmelCase     ) else bos_token
							__UpperCamelCase :     Optional[Any]										=  AddedToken(_UpperCAmelCase					,    lstrip=_UpperCAmelCase					,    rstrip=_UpperCAmelCase     ) if isinstance(_UpperCAmelCase					,    _UpperCAmelCase     ) else eos_token
							__UpperCamelCase :     int										=  AddedToken(_UpperCAmelCase					,    lstrip=_UpperCAmelCase					,    rstrip=_UpperCAmelCase     ) if isinstance(_UpperCAmelCase					,    _UpperCAmelCase     ) else unk_token
							__UpperCamelCase :     Any										=  AddedToken(_UpperCAmelCase					,    lstrip=_UpperCAmelCase					,    rstrip=_UpperCAmelCase     ) if isinstance(_UpperCAmelCase					,    _UpperCAmelCase     ) else pad_token
							super().__init__(
							    errors=_UpperCAmelCase					,    unk_token=_UpperCAmelCase					,    bos_token=_UpperCAmelCase					,    eos_token=_UpperCAmelCase					,    pad_token=_UpperCAmelCase					,    add_prefix_space=_UpperCAmelCase					,    add_bos_token=_UpperCAmelCase					,    **_UpperCAmelCase					,    )
							__UpperCamelCase :     Tuple										=  add_bos_token

							with open(_UpperCAmelCase					,    encoding="utf-8"     ) as vocab_handle:
										__UpperCamelCase :     Optional[int]										=  json.load(_UpperCAmelCase     )
							__UpperCamelCase :     Optional[int]										=  {v: k for k, v in self.encoder.items()}
							__UpperCamelCase :     List[Any]										=  errors  # how to handle errors in decoding
							__UpperCamelCase :     int										=  bytes_to_unicode()
							__UpperCamelCase :     str										=  {v: k for k, v in self.byte_encoder.items()}
							with open(_UpperCAmelCase					,    encoding="utf-8"     ) as merges_handle:
										__UpperCamelCase :     Optional[Any]										=  merges_handle.read().split("\n"     )[1:-1]
							__UpperCamelCase :     Optional[int]										=  [tuple(merge.split()     ) for merge in bpe_merges]
							__UpperCamelCase :     Union[str, Any]										=  dict(zip(_UpperCAmelCase					,    range(len(_UpperCAmelCase     )     )     )     )
							__UpperCamelCase :     Dict										=  {}
							__UpperCamelCase :     int										=  add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							__UpperCamelCase :     List[str]										=  re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"     )
				@property
				def 			a_			(self     )      ->			Any:
							return len(self.encoder     )
				def 			a_			(self     )      ->			int:
							return dict(self.encoder					,    **self.added_tokens_encoder     )
				def 			a_			(self					,    _UpperCAmelCase     )      ->			Tuple:
							if token in self.cache:
										return self.cache[token]
							__UpperCamelCase :     Any										=  tuple(_UpperCAmelCase     )
							__UpperCamelCase :     Any										=  get_pairs(_UpperCAmelCase     )

							if not pairs:
										return token

							while True:
										__UpperCamelCase :     List[Any]										=  min(_UpperCAmelCase					,    key=lambda _UpperCAmelCase     : self.bpe_ranks.get(_UpperCAmelCase					,    float("inf"     )     )     )
										if bigram not in self.bpe_ranks:
													break
										__UpperCamelCase						,   __UpperCamelCase :     List[Any]										=  bigram
										__UpperCamelCase :     Union[str, Any]										=  []
										__UpperCamelCase :     List[Any]										=  0
										while i < len(_UpperCAmelCase     ):
													try:
																__UpperCamelCase :     Optional[Any]										=  word.index(_UpperCAmelCase					,    _UpperCAmelCase     )
													except ValueError:
																new_word.extend(word[i:]     )
																break
													else:
																new_word.extend(word[i:j]     )
																__UpperCamelCase :     Union[str, 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
										__UpperCamelCase :     Any										=  tuple(_UpperCAmelCase     )
										__UpperCamelCase :     Dict										=  new_word
										if len(_UpperCAmelCase     ) == 1:
													break
										else:
													__UpperCamelCase :     Union[str, Any]										=  get_pairs(_UpperCAmelCase     )
							__UpperCamelCase :     str										=  " ".join(_UpperCAmelCase     )
							__UpperCamelCase :     List[Any]										=  word
							return word
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase=None     )      ->			str:
							if self.add_bos_token:
										__UpperCamelCase :     str										=  [self.bos_token_id]
							else:
										__UpperCamelCase :     str										=  []

							__UpperCamelCase :     Tuple										=  bos_token_ids + token_ids_a

							if token_ids_a is None:
										return output

							return output + bos_token_ids + token_ids_a
				def 			a_			(self					,    _UpperCAmelCase     )      ->			str:
							__UpperCamelCase :     Optional[int]										=  []
							for token in re.findall(self.pat					,    _UpperCAmelCase     ):
										__UpperCamelCase :     int										=  "".join(
										    self.byte_encoder[b] for b in token.encode("utf-8"     )     )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
										bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_UpperCAmelCase     ).split(" "     )     )
							return bpe_tokens
				def 			a_			(self					,    _UpperCAmelCase     )      ->			Union[str, Any]:
							return self.encoder.get(_UpperCAmelCase					,    self.encoder.get(self.unk_token     )     )
				def 			a_			(self					,    _UpperCAmelCase     )      ->			Tuple:
							return self.decoder.get(_UpperCAmelCase     )
				def 			a_			(self					,    _UpperCAmelCase     )      ->			Dict:
							__UpperCamelCase :     Tuple										=  "".join(_UpperCAmelCase     )
							__UpperCamelCase :     Dict										=  bytearray([self.byte_decoder[c] for c in text]     ).decode("utf-8"					,    errors=self.errors     )
							return text
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase = None     )      ->			Tuple[str]:
							if not os.path.isdir(_UpperCAmelCase     ):
										logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
										return
							__UpperCamelCase :     List[Any]										=  os.path.join(
							    _UpperCAmelCase					,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )
							__UpperCamelCase :     List[Any]										=  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"     )

							__UpperCamelCase :     Optional[Any]										=  0
							with open(_UpperCAmelCase					,    "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 _UpperCAmelCase     : 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 :     Optional[Any]										=  token_index
													writer.write(" ".join(_UpperCAmelCase     ) + "\n"     )
													index += 1

							return vocab_file, merge_file
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase=False					,    **_UpperCAmelCase     )      ->			str:
							__UpperCamelCase :     Any										=  kwargs.pop("add_prefix_space"					,    self.add_prefix_space     )
							if is_split_into_words or add_prefix_space:
										__UpperCamelCase :     Dict										=  " " + text
							return (text, kwargs)
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase = False					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    **_UpperCAmelCase					,    )      ->			str:
							__UpperCamelCase :     Optional[Any]										=  super()._decode(
							    token_ids=_UpperCAmelCase					,    skip_special_tokens=_UpperCAmelCase					,    clean_up_tokenization_spaces=_UpperCAmelCase					,    **_UpperCAmelCase					,    )

							if truncate_before_pattern is not None and len(_UpperCAmelCase     ) > 0:
										__UpperCamelCase :     Tuple										=  self.truncate(_UpperCAmelCase					,    _UpperCAmelCase     )

							return decoded_text





				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			str:
							def find_re(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     ):
										__UpperCamelCase :     str										=  pattern.search(_UpperCAmelCase					,    _UpperCAmelCase     )
										return m.start() if m else -1

							__UpperCamelCase :     Optional[Any]										=  [re.compile(_UpperCAmelCase					,    re.MULTILINE     ) for pattern in truncate_before_pattern]

							__UpperCamelCase :     Union[str, Any]										=  list(re.finditer("^print"					,    _UpperCAmelCase					,    re.MULTILINE     )     )

							if len(_UpperCAmelCase     ) > 1:
										__UpperCamelCase :     Tuple										=  completion[: prints[1].start()]

							__UpperCamelCase :     Optional[int]										=  list(re.finditer("^def"					,    _UpperCAmelCase					,    re.MULTILINE     )     )

							if len(_UpperCAmelCase     ) > 1:
										__UpperCamelCase :     Union[str, Any]										=  completion[: defs[1].start()]

							__UpperCamelCase :     Dict										=  0

							__UpperCamelCase :     int										=  [
							    pos for pos in [find_re(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     ) for terminal in terminals] if pos != -1
							]

							if len(_UpperCAmelCase     ) > 0:
										return completion[: min(_UpperCAmelCase     )]
							else:
										return completion









'''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					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			str:
							__UpperCamelCase :     str										=  cuda
							__UpperCamelCase :     Union[str, Any]										=  AutoConfig.from_pretrained(_UpperCAmelCase     )
							__UpperCamelCase :     List[str]										=  AutoModelForCTC.from_pretrained(_UpperCAmelCase     )
							self.model.eval()
							if self.cuda:
										self.model.to(device="cuda"     )
							__UpperCamelCase :     Any										=  AutoProcessor.from_pretrained(_UpperCAmelCase     )
							__UpperCamelCase :     Any										=  torchaudio.transforms.Resample(_UpperCAmelCase					,    1_6_0_0_0     )
							__UpperCamelCase :     List[Any]										=  0
							__UpperCamelCase :     Dict										=  list(self.processor.tokenizer.get_vocab().keys()     )
							for i in range(len(_UpperCAmelCase     )     ):
										if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
													__UpperCamelCase :     Union[str, Any]										=  i
							print("Blank Token id [PAD]/<pad>"					,    _UpperCAmelCase     )
							__UpperCamelCase :     Tuple										=  blank_id
				def 			a_			(self					,    _UpperCAmelCase     )      ->			Union[str, Any]:
							__UpperCamelCase						,   __UpperCamelCase :     str										=  torchaudio.load(_UpperCAmelCase     )
							__UpperCamelCase :     Dict										=  self.resampler(_UpperCAmelCase     ).squeeze().numpy()
							return speech
				def 			a_			(self					,    _UpperCAmelCase     )      ->			int:
							__UpperCamelCase :     Union[str, Any]										=  self.blank_id
							__UpperCamelCase :     Any										=  "|".join(item["sent"].split(" "     )     )
							if not os.path.isfile(item["wav_path"]     ):
										print(item["wav_path"]					,    "not found in wavs directory"     )

							__UpperCamelCase :     Optional[Any]										=  self.speech_file_to_array_fn(item["wav_path"]     )
							__UpperCamelCase :     Tuple										=  self.processor(_UpperCAmelCase					,    sampling_rate=1_6_0_0_0					,    return_tensors="pt"					,    padding=_UpperCAmelCase     )
							if self.cuda:
										__UpperCamelCase :     str										=  inputs.to(device="cuda"     )

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

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

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

							__UpperCamelCase :     Tuple										=  {c: i for i, c in enumerate(_UpperCAmelCase     )}
							__UpperCamelCase :     List[str]										=  []
							for c in transcript:
										if c in dictionary:
													tokens.append(dictionary[c]     )

							def get_trellis(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase=0     ):
										__UpperCamelCase :     List[Any]										=  emission.size(0     )
										__UpperCamelCase :     Optional[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.
										__UpperCamelCase :     Optional[int]										=  torch.full((num_frames + 1, num_tokens + 1)					,    -float("inf"     )     )
										__UpperCamelCase :     Union[str, Any]										=  0
										for t in range(_UpperCAmelCase     ):
													__UpperCamelCase :     Optional[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

							__UpperCamelCase :     Optional[Any]										=  get_trellis(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )



							@dataclass
							class    A						:




											'''simple docstring'''





											A       =							42
											A       =							42
											A       =							42

							def backtrack(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase=0     ):
										__UpperCamelCase :     Dict										=  trellis.size(1     ) - 1
										__UpperCamelCase :     List[str]										=  torch.argmax(trellis[:, j]     ).item()

										__UpperCamelCase :     int										=  []
										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.
													__UpperCamelCase :     Dict										=  trellis[t - 1, j] + emission[t - 1, blank_id]
													# Score for token changing from C-1 at T-1 to J at T.
													__UpperCamelCase :     Optional[int]										=  trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

													# 2. Store the path with frame-wise probability.
													__UpperCamelCase :     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]

							__UpperCamelCase :     Optional[Any]										=  backtrack(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )



							@dataclass
							class    A						:




											'''simple docstring'''





											A       =							42
											A       =							42
											A       =							42
											A       =							42
											def __repr__(self     )      ->			Optional[int]:
														return f"{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}"





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

							def merge_repeats(_UpperCAmelCase     ):
										__UpperCamelCase						,   __UpperCamelCase :     int										=  0, 0
										__UpperCamelCase :     Dict										=  []
										while ia < len(_UpperCAmelCase     ):
													while ia < len(_UpperCAmelCase     ) and path[ia].token_index == path[ia].token_index:
																ia += 1
													__UpperCamelCase :     Union[str, Any]										=  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					,    )     )
													__UpperCamelCase :     Any										=  ia
										return segments

							__UpperCamelCase :     Optional[Any]										=  merge_repeats(_UpperCAmelCase     )
							with open(item["out_path"]					,    "w"     ) as out_align:
										for seg in segments:
													out_align.write(str(_UpperCAmelCase     ) + "\n"     )





				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			str:
							if not os.path.exists(_UpperCAmelCase     ):
										os.makedirs(_UpperCAmelCase     )

							# load text file
							__UpperCamelCase :     Optional[int]										=  open(_UpperCAmelCase					,    encoding="utf8"     ).readlines()

							__UpperCamelCase :     str										=  []
							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 :     List[Any]										=  line.strip().split("\t"     )
										__UpperCamelCase :     List[Any]										=  os.path.join(_UpperCAmelCase					,    wav_name + ".wav"     )
										__UpperCamelCase :     Tuple										=  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      __lowerCAmelCase   (       ):
			__UpperCamelCase :     Union[str, Any]										=  argparse.ArgumentParser()

			parser.add_argument(
			    "--model_name"   , type=snake_case__   , default="arijitx/wav2vec2-xls-r-300m-bengali"   , help="wav2vec model name"		)
			parser.add_argument("--wav_dir"   , type=snake_case__   , default="./wavs"   , help="directory containing wavs"		)
			parser.add_argument("--text_file"   , type=snake_case__   , default="script.txt"   , help="file containing text"		)
			parser.add_argument("--input_wavs_sr"   , type=snake_case__   , default=16_000   , help="sampling rate of input audios"		)
			parser.add_argument(
			    "--output_dir"   , type=snake_case__   , default="./out_alignment"   , help="output directory containing the alignment files"		)
			parser.add_argument("--cuda"   , action="store_true"		)

			__UpperCamelCase :     Any										=  parser.parse_args()

			__UpperCamelCase :     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()



