



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   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  cuda
				a__					=  AutoConfig.from_pretrained(lowerCamelCase       )
				a__					=  AutoModelForCTC.from_pretrained(lowerCamelCase       )
				self.model.eval()
				if self.cuda:
							self.model.to(device="""cuda"""       )
				a__					=  AutoProcessor.from_pretrained(lowerCamelCase       )
				a__					=  torchaudio.transforms.Resample(lowerCamelCase   ,       1_6000       )
				a__					=  0
				a__					=  list(self.processor.tokenizer.get_vocab().keys()       )
				for i in range(len(lowerCamelCase       )       ):
							if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
										a__					=  i
				print("""Blank Token id [PAD]/<pad>"""   ,       lowerCamelCase       )
				a__					=  blank_id

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__				,       a__					=  torchaudio.load(lowerCamelCase       )
				a__					=  self.resampler(lowerCamelCase       ).squeeze().numpy()
				return speech

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  self.blank_id
				a__					=  """|""".join(item["""sent"""].split(""" """       )       )
				if not os.path.isfile(item["""wav_path"""]       ):
							print(item["""wav_path"""]   ,       """not found in wavs directory"""       )

				a__					=  self.speech_file_to_array_fn(item["""wav_path"""]       )
				a__					=  self.processor(lowerCamelCase   ,       sampling_rate=1_6000   ,       return_tensors="""pt"""   ,       padding=lowerCamelCase       )
				if self.cuda:
							a__					=  inputs.to(device="""cuda"""       )

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

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

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

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

				def get_trellis(lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase=0       ):
							a__					=  emission.size(0       )
							a__					=  len(lowerCamelCase       )

							# 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.
							a__					=  torch.full((num_frames + 1, num_tokens + 1)   ,       -float("""inf"""       )       )
							a__					=  0
							for t in range(lowerCamelCase       ):
										a__					=  torch.maximum(
										    # Score for staying at the same token
										    trellis[t, 1:] + emission[t, blank_id]   ,       trellis[t, :-1] + emission[t, tokens]   ,       )
							return trellis

				a__					=  get_trellis(lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase       )
				@dataclass
				class lowerCAmelCase_       :







					'''simple docstring'''




					A_       :     int
					A_       :     int
					A_       :     float

				def backtrack(lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase=0       ):
							a__					=  trellis.size(1       ) - 1
							a__					=  torch.argmax(trellis[:, j]       ).item()

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

										# 2. Store the path with frame-wise probability.
										a__					=  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   ,       lowerCamelCase       )       )

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

				a__					=  backtrack(lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase       )
				@dataclass
				class lowerCAmelCase_       :







					'''simple docstring'''




					A_       :     str
					A_       :     int
					A_       :     int
					A_       :     float

					def __repr__(	self       ):



								'''simple docstring'''


								return f'{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'







					@property
					def 				_A			(	self       ):



								'''simple docstring'''


								return self.end - self.start

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

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







	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase       ):



				'''simple docstring'''


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

				# load text file
				a__					=  open(lowerCamelCase   ,       encoding="""utf8"""       ).readlines()

				a__					=  []
				for line in lines:
							if len(line.strip().split("""\t"""       )       ) != 2:
										print("""Script must be in format: 00001  this is my sentence"""       )
										exit()

							a__				,       a__					=  line.strip().split("""\t"""       )
							a__					=  os.path.join(lowerCamelCase   ,       wav_name + """.wav"""       )
							a__					=  os.path.join(lowerCamelCase   ,       wav_name + """.txt"""       )

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

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




def    UpperCAmelCase					(  ):



			'''simple docstring'''





			a__					=  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"""	)

			a__					=  parser.parse_args()

			a__					=  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()






import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

_lowercase				:					Any     					=logging.get_logger(__name__)

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

_lowercase				:					int     					={
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

_lowercase				:					Tuple     					={
    """microsoft/deberta-base""": 5_12,
    """microsoft/deberta-large""": 5_12,
    """microsoft/deberta-xlarge""": 5_12,
    """microsoft/deberta-base-mnli""": 5_12,
    """microsoft/deberta-large-mnli""": 5_12,
    """microsoft/deberta-xlarge-mnli""": 5_12,
}

_lowercase				:					str     					={
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}




def    UpperCAmelCase					(  ):



			'''simple docstring'''





			a__					=  (
			    list(range(ord("""!"""	)				,					ord("""~"""	) + 1	)	) + list(range(ord("""¡"""	)				,					ord("""¬"""	) + 1	)	) + list(range(ord("""®"""	)				,					ord("""ÿ"""	) + 1	)	)
			)
			a__					=  bs[:]
			a__					=  0
			for b in range(2**8	):
						if b not in bs:
									bs.append(lowercase__	)
									cs.append(2**8 + n	)
									n += 1
			a__					=  [chr(lowercase__	) for n in cs]
			return dict(zip(lowercase__				,					lowercase__	)	)




def    UpperCAmelCase					(  lowercase__						:					Optional[Any]	):



			'''simple docstring'''





			a__					=  set()
			a__					=  word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)	)
						a__					=  char
			return pairs





class lowerCAmelCase_       (   A_			):







	'''simple docstring'''




	A_       :     List[str]									=		VOCAB_FILES_NAMES
	A_       :     str									=		PRETRAINED_VOCAB_FILES_MAP
	A_       :     List[str]									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A_       :     Optional[int]									=		['input_ids', 'attention_mask', 'token_type_ids']

	def __init__(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase="replace"   ,       lowerCamelCase="[CLS]"   ,       lowerCamelCase="[SEP]"   ,       lowerCamelCase="[SEP]"   ,       lowerCamelCase="[CLS]"   ,       lowerCamelCase="[UNK]"   ,       lowerCamelCase="[PAD]"   ,       lowerCamelCase="[MASK]"   ,       lowerCamelCase=False   ,       lowerCamelCase=False   ,       **lowerCamelCase   ,       ):



				'''simple docstring'''


				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else bos_token
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else eos_token
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else sep_token
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else cls_token
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else unk_token
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else pad_token

				# Mask token behave like a normal word, i.e. include the space before it
				a__					=  AddedToken(lowerCamelCase   ,       lstrip=lowerCamelCase   ,       rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase   ,       lowerCamelCase       ) else mask_token

				super().__init__(
				    errors=lowerCamelCase   ,       bos_token=lowerCamelCase   ,       eos_token=lowerCamelCase   ,       unk_token=lowerCamelCase   ,       sep_token=lowerCamelCase   ,       cls_token=lowerCamelCase   ,       pad_token=lowerCamelCase   ,       mask_token=lowerCamelCase   ,       add_prefix_space=lowerCamelCase   ,       add_bos_token=lowerCamelCase   ,       **lowerCamelCase   ,       )
				a__					=  add_bos_token

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

				# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
				a__					=  re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""       )

	@property
	# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
	def 				_A			(	self       ):



				'''simple docstring'''


				return len(self.encoder       )

	def 				_A			(	self       ):



				'''simple docstring'''


				return dict(self.encoder   ,       **self.added_tokens_encoder       )

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				if token in self.cache:
							return self.cache[token]
				a__					=  tuple(lowerCamelCase       )
				a__					=  get_pairs(lowerCamelCase       )

				if not pairs:
							return token

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

										if word[i] == first and i < len(lowerCamelCase       ) - 1 and word[i + 1] == second:
													new_word.append(first + second       )
													i += 2
										else:
													new_word.append(word[i]       )
													i += 1
							a__					=  tuple(lowerCamelCase       )
							a__					=  new_word
							if len(lowerCamelCase       ) == 1:
										break
							else:
										a__					=  get_pairs(lowerCamelCase       )
				a__					=  """ """.join(lowerCamelCase       )
				a__					=  word
				return word

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase = None       ):



				'''simple docstring'''


				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				a__					=  [self.cls_token_id]
				a__					=  [self.sep_token_id]
				return cls + token_ids_a + sep + token_ids_a + sep

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase = None   ,       lowerCamelCase = False       ):



				'''simple docstring'''


				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowerCamelCase   ,       token_ids_a=lowerCamelCase   ,       already_has_special_tokens=lowerCamelCase       )

				if token_ids_a is None:
							return [1] + ([0] * len(lowerCamelCase       )) + [1]
				return [1] + ([0] * len(lowerCamelCase       )) + [1] + ([0] * len(lowerCamelCase       )) + [1]

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase = None       ):



				'''simple docstring'''


				a__					=  [self.sep_token_id]
				a__					=  [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       ) * [0] + len(token_ids_a + sep       ) * [1]

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  []
				for token in re.findall(self.pat   ,       lowerCamelCase       ):
							a__					=  """""".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(lowerCamelCase       ).split(""" """       )       )
				return bpe_tokens

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				return self.encoder.get(lowerCamelCase   ,       self.encoder.get(self.unk_token       )       )

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				return self.decoder.get(lowerCamelCase       )

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  """""".join(lowerCamelCase       )
				a__					=  bytearray([self.byte_decoder[c] for c in text]       ).decode("""utf-8"""   ,       errors=self.errors       )
				return text

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase = None       ):



				'''simple docstring'''


				if not os.path.isdir(lowerCamelCase       ):
							logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
							return
				a__					=  os.path.join(
				    lowerCamelCase   ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
				a__					=  os.path.join(
				    lowerCamelCase   ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]       )

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

				a__					=  0
				with open(lowerCamelCase   ,       """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 lowerCamelCase       : 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!"""       )
													a__					=  token_index
										writer.write(""" """.join(lowerCamelCase       ) + """\n"""       )
										index += 1

				return vocab_file, merge_file

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase=False   ,       **lowerCamelCase       ):



				'''simple docstring'''


				a__					=  kwargs.pop("""add_prefix_space"""   ,       self.add_prefix_space       )
				if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase       ) > 0 and not text[0].isspace()):
							a__					=  """ """ + text
				return (text, kwargs)







	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  []
				for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(lowerCamelCase   ,       add_special_tokens=lowerCamelCase       ) + [self.eos_token_id]       )
				if len(lowerCamelCase       ) > self.model_max_length:
							a__					=  input_ids[-self.model_max_length :]
				return input_ids

