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()





from __future__ import annotations

import sys
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =letter
		_lowercase   =freq
		_lowercase   ={}


	def __repr__(   self       ):
		return F'''{self.letter}:{self.freq}'''

class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =freq
		_lowercase   =left
		_lowercase   =right

def 	__lowerCamelCase							(				__a    : str		)      ->       list[Letter]:
	_lowercase   ={}
	with open(__a		) as f:
		while True:
			_lowercase   =f.read(1		)
			if not c:
				break
			_lowercase   =chars[c] + 1 if c in chars else 1
	return sorted((Letter(__a      ,							__a		) for c, f in chars.items())      ,							key=lambda __a		: x.freq		)

def 	__lowerCamelCase							(				__a    : list[Letter]		)      ->       Letter | TreeNode:
	_lowercase   =letters  # type: ignore
	while len(__a		) > 1:
		_lowercase   =response.pop(0		)
		_lowercase   =response.pop(0		)
		_lowercase   =left.freq + right.freq
		_lowercase   =TreeNode(__a      ,							__a      ,							__a		)
		response.append(__a		)
		response.sort(key=lambda __a		: x.freq		)
	return response[0]

def 	__lowerCamelCase							(				__a    : Letter | TreeNode      ,							__a    : str		)      ->       list[Letter]:
	if isinstance(__a      ,							__a		):
		_lowercase   =bitstring
		return [root]
	_lowercase   =root  # type: ignore
	_lowercase   =[]
	letters += traverse_tree(treenode.left      ,							bitstring + "0"		)
	letters += traverse_tree(treenode.right      ,							bitstring + "1"		)
	return letters

def 	__lowerCamelCase							(				__a    : str		)      ->       None:
	_lowercase   =parse_file(__a		)
	_lowercase   =build_tree(__a		)
	_lowercase   ={
	    k: v for letter in traverse_tree(__a      ,							""		) for k, v in letter.bitstring.items()
	}
	print(f'''Huffman Coding  of {file_path}: '''		)
	with open(__a		) as f:
		while True:
			_lowercase   =f.read(1		)
			if not c:
				break
			print(letters[c]      ,							end=" "		)
	print()


if __name__ == "__main__":
						# pass the file path to the huffman function
						huffman(sys.argv[1])




