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       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =cuda
		_lowercase   =AutoConfig.from_pretrained(lowerCAmelCase_       )
		_lowercase   =AutoModelForCTC.from_pretrained(lowerCAmelCase_       )
		self.model.eval()
		if self.cuda:
			self.model.to(device="cuda"       )
		_lowercase   =AutoProcessor.from_pretrained(lowerCAmelCase_       )
		_lowercase   =torchaudio.transforms.Resample(lowerCAmelCase_       ,  16000       )
		_lowercase   =0
		_lowercase   =list(self.processor.tokenizer.get_vocab().keys()       )
		for i in range(len(lowerCAmelCase_       )       ):
			if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
				_lowercase   =i
		print("Blank Token id [PAD]/<pad>"       ,  lowerCAmelCase_       )
		_lowercase   =blank_id


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   ,  _lowercase   =torchaudio.load(lowerCAmelCase_       )
		_lowercase   =self.resampler(lowerCAmelCase_       ).squeeze().numpy()
		return speech


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

		_lowercase   =self.speech_file_to_array_fn(item["wav_path"]       )
		_lowercase   =self.processor(lowerCAmelCase_       ,  sampling_rate=16000       ,  return_tensors="pt"       ,  padding=lowerCAmelCase_       )
		if self.cuda:
			_lowercase   =inputs.to(device="cuda"       )

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

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

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

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

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

		_lowercase   =get_trellis(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
		@dataclass
		class 	_a  :



			"""simple docstring"""



			__SCREAMING_SNAKE_CASE              =					42
			__SCREAMING_SNAKE_CASE              =					42
			__SCREAMING_SNAKE_CASE              =					42

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

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

				# 2. Store the path with frame-wise probability.
				_lowercase   =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]

		_lowercase   =backtrack(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
		@dataclass
		class 	_a  :



			"""simple docstring"""



			__SCREAMING_SNAKE_CASE              =					42
			__SCREAMING_SNAKE_CASE              =					42
			__SCREAMING_SNAKE_CASE              =					42
			__SCREAMING_SNAKE_CASE              =					42


			def __repr__(   self       ):
				return F'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''


			@property
			def __lowerCAmelCase      (   self       ):
				return self.end - self.start

		def merge_repeats(lowerCAmelCase_       ):
			_lowercase   ,  _lowercase   =0, 0
			_lowercase   =[]
			while ia < len(lowerCAmelCase_       ):
				while ia < len(lowerCAmelCase_       ) and path[ia].token_index == path[ia].token_index:
					ia += 1
				_lowercase   =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_       ,  )       )
				_lowercase   =ia
			return segments

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


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if not os.path.exists(lowerCAmelCase_       ):
			os.makedirs(lowerCAmelCase_       )

		# load text file
		_lowercase   =open(lowerCAmelCase_       ,  encoding="utf8"       ).readlines()

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

			_lowercase   ,  _lowercase   =line.strip().split("\t"       )
			_lowercase   =os.path.join(lowerCAmelCase_       ,  wav_name + ".wav"       )
			_lowercase   =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 	__lowerCamelCase							(				)      ->       Union[str, Any]:
	_lowercase   =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=16_000      ,							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"		)

	_lowercase   =parser.parse_args()

	_lowercase   =WavaVecaAligner(args.model_name      ,							args.input_wavs_sr      ,							args.cuda		)
	aligner.align_data(args.wav_dir      ,							args.text_file      ,							args.output_dir		)


if __name__ == "__main__":
						main()









'''simple docstring'''







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


SCREAMING_SNAKE_CASE__     : List[Any]				=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     : List[str]				=   {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__     : Optional[int]				=   {
    """vocab_file""": {
        """microsoft/layoutlm-base-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt"""
        ),
        """microsoft/layoutlm-large-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """microsoft/layoutlm-base-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json"""
        ),
        """microsoft/layoutlm-large-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__     : Tuple				=   {
    """microsoft/layoutlm-base-uncased""": 5_12,
    """microsoft/layoutlm-large-uncased""": 5_12,
}

SCREAMING_SNAKE_CASE__     : List[Any]				=   {
    """microsoft/layoutlm-base-uncased""": {"""do_lower_case""": True},
    """microsoft/layoutlm-large-uncased""": {"""do_lower_case""": True},
}




class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		List[str]		=      VOCAB_FILES_NAMES
			__snake_case  :		Dict		=      PRETRAINED_VOCAB_FILES_MAP
			__snake_case  :		Optional[int]		=      PRETRAINED_INIT_CONFIGURATION
			__snake_case  :		Any		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case  :		str		=      LayoutLMTokenizer






			def __init__(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Dict=None      ,       SCREAMING_SNAKE_CASE  :							int=None      ,       SCREAMING_SNAKE_CASE  :							Tuple=True      ,       SCREAMING_SNAKE_CASE  :							Optional[int]="[UNK]"      ,       SCREAMING_SNAKE_CASE  :							Tuple="[SEP]"      ,       SCREAMING_SNAKE_CASE  :							int="[PAD]"      ,       SCREAMING_SNAKE_CASE  :							List[Any]="[CLS]"      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]="[MASK]"      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=True      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=None      ,       **SCREAMING_SNAKE_CASE  :							Optional[int]      ,       ):




					"""simple docstring"""







					super().__init__(
					    SCREAMING_SNAKE_CASE      ,       tokenizer_file=SCREAMING_SNAKE_CASE      ,       do_lower_case=SCREAMING_SNAKE_CASE      ,       unk_token=SCREAMING_SNAKE_CASE      ,       sep_token=SCREAMING_SNAKE_CASE      ,       pad_token=SCREAMING_SNAKE_CASE      ,       cls_token=SCREAMING_SNAKE_CASE      ,       mask_token=SCREAMING_SNAKE_CASE      ,       tokenize_chinese_chars=SCREAMING_SNAKE_CASE      ,       strip_accents=SCREAMING_SNAKE_CASE      ,       **SCREAMING_SNAKE_CASE      ,       )

					SCREAMING_SNAKE_CASE_						:str										=      json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
					if (
					    normalizer_state.get('lowercase'      ,       SCREAMING_SNAKE_CASE	) != do_lower_case
					    or normalizer_state.get('strip_accents'      ,       SCREAMING_SNAKE_CASE	) != strip_accents
					    or normalizer_state.get('handle_chinese_chars'      ,       SCREAMING_SNAKE_CASE	) != tokenize_chinese_chars
					):
							SCREAMING_SNAKE_CASE_						:List[str]										=      getattr(SCREAMING_SNAKE_CASE      ,       normalizer_state.pop('type'	)	)
							SCREAMING_SNAKE_CASE_						:Tuple										=      do_lower_case
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      strip_accents
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      tokenize_chinese_chars
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      normalizer_class(**SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      do_lower_case






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							str=None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

					if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

					return output






			def 				_lowercase	(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							List[int]      ,       SCREAMING_SNAKE_CASE  :							Optional[List[int]] = None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      [self.sep_token_id]
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      [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 				_lowercase	(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							Optional[str] = None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self._tokenizer.model.save(SCREAMING_SNAKE_CASE      ,       name=SCREAMING_SNAKE_CASE	)
					return tuple(SCREAMING_SNAKE_CASE	)
