
"""simple docstring"""





import numpy as np
import requests







def       __a	(						):
   UpperCAmelCase_   :   Union[str, Any]     	=       requests.get(
       "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
       "master/Week1/ADRvsRating.csv"    )
   UpperCAmelCase_   :   List[Any]     	=       response.text.splitlines()
   UpperCAmelCase_   :   Dict     	=       []
   for item in lines:
      UpperCAmelCase_   :   Tuple     	=       item.split(","    )
      data.append(__lowerCamelCase    )
   data.pop(0    )  # This is for removing the labels from the list
   UpperCAmelCase_   :   Optional[int]     	=       np.matrix(__lowerCamelCase    )
   return dataset







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Union[str, Any]     	=       len_data

   UpperCAmelCase_   :   Any     	=       np.dot(__lowerCamelCase,						data_x.transpose()    )
   prod -= data_y.transpose()
   UpperCAmelCase_   :   List[Any]     	=       np.dot(__lowerCamelCase,						__lowerCamelCase    )
   UpperCAmelCase_   :   Dict     	=       theta - (alpha / n) * sum_grad
   return theta







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Any     	=       np.dot(__lowerCamelCase,						data_x.transpose()    )
   prod -= data_y.transpose()
   UpperCAmelCase_   :   Optional[Any]     	=       np.sum(np.square(__lowerCamelCase    )    )
   UpperCAmelCase_   :   Optional[Any]     	=       sum_elem / (2 * len_data)
   return error







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   List[Any]     	=       10_0000
   UpperCAmelCase_   :   str     	=       0.000_1550

   UpperCAmelCase_   :   List[Any]     	=       data_x.shape[1]
   UpperCAmelCase_   :   str     	=       data_x.shape[0] - 1

   UpperCAmelCase_   :   str     	=       np.zeros((1, no_features)    )

   for i in range(0,						__lowerCamelCase    ):
      UpperCAmelCase_   :   Dict     	=       run_steep_gradient_descent(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
      UpperCAmelCase_   :   Optional[int]     	=       sum_of_square_error(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
      print(f"""At Iteration {i + 1} - Error is {error:.5f}"""    )

   return theta







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   List[str]     	=       sum(abs(y - predicted_y[i]    ) for i, y in enumerate(__lowerCamelCase    )    )
   return total / len(__lowerCamelCase    )







def       __a	(						):
   UpperCAmelCase_   :   int     	=       collect_dataset()

   UpperCAmelCase_   :   List[str]     	=       data.shape[0]
   UpperCAmelCase_   :   Tuple     	=       np.c_[np.ones(__lowerCamelCase    ), data[:, :-1]].astype(__lowerCamelCase    )
   UpperCAmelCase_   :   Optional[int]     	=       data[:, -1].astype(__lowerCamelCase    )

   UpperCAmelCase_   :   str     	=       run_linear_regression(__lowerCamelCase,						__lowerCamelCase    )
   UpperCAmelCase_   :   Union[str, Any]     	=       theta.shape[1]
   print("Resultant Feature vector : "    )
   for i in range(0,						__lowerCamelCase    ):
      print(f"""{theta[0, i]:.5f}"""    )


if __name__ == "__main__":
    main()








'''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()



