


import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor







class      __lowercase  :





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= cuda
										SCREAMING_SNAKE_CASE_						:			int       						= AutoConfig.from_pretrained(UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			int       						= AutoModelForCTC.from_pretrained(UpperCamelCase__    )
										self.model.eval()
										if self.cuda:
													self.model.to(device='cuda'    )
										SCREAMING_SNAKE_CASE_						:			Tuple       						= AutoProcessor.from_pretrained(UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			str       						= torchaudio.transforms.Resample(UpperCamelCase__       ,		1_6_0_0_0    )
										SCREAMING_SNAKE_CASE_						:			Any       						= 0
										SCREAMING_SNAKE_CASE_						:			int       						= list(self.processor.tokenizer.get_vocab().keys()    )
										for i in range(len(UpperCamelCase__    )    ):
													if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= i
										print('Blank Token id [PAD]/<pad>'       ,		UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= blank_id






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Tuple       						= torchaudio.load(UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= self.resampler(UpperCamelCase__    ).squeeze().numpy()
										return speech






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.blank_id
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= '''|'''.join(item['sent'].split(' '    )    )
										if not os.path.isfile(item['wav_path']    ):
													print(item['wav_path']       ,		'not found in wavs directory'    )

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= self.speech_file_to_array_fn(item['wav_path']    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.processor(UpperCamelCase__       ,		sampling_rate=1_6_0_0_0       ,		return_tensors='pt'       ,		padding=UpperCamelCase__    )
										if self.cuda:
													SCREAMING_SNAKE_CASE_						:			str       						= inputs.to(device='cuda'    )

										with torch.no_grad():
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= self.model(inputs.input_values    ).logits

										# get the emission probability at frame level
										SCREAMING_SNAKE_CASE_						:			str       						= torch.log_softmax(UpperCamelCase__       ,		dim=-1    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= emissions[0].cpu().detach()

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

										SCREAMING_SNAKE_CASE_						:			List[str]       						= {c: i for i, c in enumerate(UpperCamelCase__    )}
										SCREAMING_SNAKE_CASE_						:			Any       						= []
										for c in transcript:
													if c in dictionary:
																tokens.append(dictionary[c]    )

										def get_trellis(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__=0    ):
													SCREAMING_SNAKE_CASE_						:			List[str]       						= emission.size(0    )
													SCREAMING_SNAKE_CASE_						:			List[str]       						= 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.
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= torch.full((num_frames + 1, num_tokens + 1)       ,		-float('inf'    )    )
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 0
													for t in range(UpperCamelCase__    ):
																SCREAMING_SNAKE_CASE_						:			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

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= get_trellis(UpperCamelCase__       ,		UpperCamelCase__       ,		UpperCamelCase__    )







										@dataclass
										class      __lowercase  :





																	"""simple docstring"""







																	_UpperCAmelCase     =		4_2
																	_UpperCAmelCase     =		4_2
																	_UpperCAmelCase     =		4_2

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

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

																# 2. Store the path with frame-wise probability.
																SCREAMING_SNAKE_CASE_						:			Optional[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]

										SCREAMING_SNAKE_CASE_						:			Any       						= backtrack(UpperCamelCase__       ,		UpperCamelCase__       ,		UpperCamelCase__       ,		UpperCamelCase__    )







										@dataclass
										class      __lowercase  :





																	"""simple docstring"""







																	_UpperCAmelCase     =		4_2
																	_UpperCAmelCase     =		4_2
																	_UpperCAmelCase     =		4_2
																	_UpperCAmelCase     =		4_2






																	def __repr__(	self    ):





																				"""simple docstring"""




																				return F'''{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}'''






																	@property
																	def  UpperCamelCase__			(	self    ):





																				"""simple docstring"""




																				return self.end - self.start

										def merge_repeats(lowerCAmelCase__    ):
													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= 0, 0
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= []
													while ia < len(UpperCamelCase__    ):
																while ia < len(UpperCamelCase__    ) and path[ia].token_index == path[ia].token_index:
																			ia += 1
																SCREAMING_SNAKE_CASE_						:			Dict       						= 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__       ,		)    )
																SCREAMING_SNAKE_CASE_						:			int       						= ia
													return segments

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






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




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

										# load text file
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= open(UpperCamelCase__       ,		encoding='utf8'    ).readlines()

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

													SCREAMING_SNAKE_CASE_						:			Dict       						= line.strip().split('\t'    )
													SCREAMING_SNAKE_CASE_						:			Tuple       						= os.path.join(UpperCamelCase__       ,		wav_name + '.wav'    )
													SCREAMING_SNAKE_CASE_						:			str       						= 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 		a__       (							):
			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= argparse.ArgumentParser()

			parser.add_argument(
			    '--model_name',	type=SCREAMING_SNAKE_CASE__,	default='arijitx/wav2vec2-xls-r-300m-bengali',	help='wav2vec model name'							)
			parser.add_argument('--wav_dir',	type=SCREAMING_SNAKE_CASE__,	default='./wavs',	help='directory containing wavs'							)
			parser.add_argument('--text_file',	type=SCREAMING_SNAKE_CASE__,	default='script.txt',	help='file containing text'							)
			parser.add_argument('--input_wavs_sr',	type=SCREAMING_SNAKE_CASE__,	default=1_6_0_0_0,	help='sampling rate of input audios'							)
			parser.add_argument(
			    '--output_dir',	type=SCREAMING_SNAKE_CASE__,	default='./out_alignment',	help='output directory containing the alignment files'							)
			parser.add_argument('--cuda',	action='store_true'							)

			SCREAMING_SNAKE_CASE_						:			Tuple       						= parser.parse_args()

			SCREAMING_SNAKE_CASE_						:			Tuple       						= 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 bsa
import requests




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				dict[str, str]:


		'''simple docstring'''


		return {
		    "name": soup.ha.a.text,
		    "genre": soup.find('''span'''      ,    class_='''genre'''       ).text.strip(),
		    "rating": soup.strong.text,
		    "page_link": F'https://www.imdb.com{soup.a.get("href"       )}',
		}




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__ = 5       )   ->				tuple:


		'''simple docstring'''


		snake_case   :    List[Any]			       =		int(float(SCREAMING_SNAKE_CASE__       )       )
		if num_movies < 1:
				return ()
		snake_case   :    int			       =		(
		    '''https://www.imdb.com/search/title?title_type='''
		    F'feature&sort=num_votes,desc&count={num_movies}'
		)
		snake_case   :    Optional[Any]			       =		bsa.BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__       ).content      ,    '''html.parser'''       )
		return tuple(
		    get_movie_data_from_soup(SCREAMING_SNAKE_CASE__       )
		    for movie in source.find_all('''div'''      ,    class_='''lister-item mode-advanced'''       )       )


if __name__ == "__main__":
						import json

						lowercase__		    =	int(input("How many movies would you like to see? "))
						print(
						    ", ".join(
						        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
						    )
						)


