
import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor

class 		lowercase_							:

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


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

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_							,					UpperCamelCase_											=							torchaudio.load(__UpperCamelCase  )
        UpperCamelCase_											=							self.resampler(__UpperCamelCase  ).squeeze().numpy()
        return speech

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


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

        UpperCamelCase_											=							self.speech_file_to_array_fn(item["""wav_path"""]  )
        UpperCamelCase_											=							self.processor(__UpperCamelCase  ,						sampling_rate=1_6_0_0_0  ,						return_tensors="""pt"""  ,						padding=__UpperCamelCase  )
        if self.cuda:
               UpperCamelCase_											=							inputs.to(device="""cuda"""  )

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

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

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

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

        def get_trellis(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase=0  ):
               UpperCamelCase_											=							emission.size(0  )
               UpperCamelCase_											=							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_											=							torch.full((num_frames + 1, num_tokens + 1)  ,						-float("""inf"""  )  )
               UpperCamelCase_											=							0
               for t in range(__UpperCamelCase  ):
                      UpperCamelCase_											=							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_											=							get_trellis(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  )

        @dataclass
        class 		lowercase_							:
         A__    :					int
         A__    :					int
         A__    :					float

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

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

                      # 2. Store the path with frame-wise probability.
                      UpperCamelCase_											=							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_											=							backtrack(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  )

        @dataclass
        class 		lowercase_							:
         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*2_0:5d}\t{self.end*2_0:5d}'''

         @property
         def lowerCamelCase_					(   self  ):




                """simple docstring"""


                return self.end - self.start

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

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

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


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

        # load text file
        UpperCamelCase_											=							open(__UpperCamelCase  ,						encoding="""utf8"""  ).readlines()

        UpperCamelCase_											=							[]
        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_											=							line.strip().split("""\t"""  )
               UpperCamelCase_											=							os.path.join(__UpperCamelCase  ,						wav_name + """.wav"""  )
               UpperCamelCase_											=							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__					(    )		->						Any:
       UpperCamelCase_											=							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=1_6000						,   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"""  )

       UpperCamelCase_											=							parser.parse_args()

       UpperCamelCase_											=							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 typing import Any

import numpy as np

class 					__UpperCamelCase					:







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						if np.any(tableau[:, -1]	,			where=tableau[:, -1] < 0   ):
											raise ValueError('RHS must be > 0'   )

						__magic_name__				=     tableau
						__magic_name__,						__magic_name__				=     tableau.shape

						# Number of decision variables x1, x2, x3...
						__magic_name__				=     n_vars

						# Number of artificial variables to be minimised
						__magic_name__				=     len(np.where(tableau[self.n_vars : -1] == -1   )[0]   )

						# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
						__magic_name__				=     (self.n_art_vars > 0) + 1

						# Number of slack variables added to make inequalities into equalities
						__magic_name__				=     self.n_rows - self.n_stages

						# Objectives for each stage
						__magic_name__				=     ['max']

						# In two stage simplex, first minimise then maximise
						if self.n_art_vars:
											self.objectives.append('min'   )

						__magic_name__				=     ['']

						# Index of current pivot row and column
						__magic_name__				=     None
						__magic_name__				=     None

						# Does objective row only contain (non)-negative values?
						__magic_name__				=     False







	@staticmethod
	def 						_SCREAMING_SNAKE_CASE				( *__UpperCamelCase:			int   ):

						'''simple docstring'''





						if len(__UpperCamelCase   ) != 3:
											raise ValueError('Must provide n_vars, n_slack, and n_art_vars'   )

						if not all(x >= 0 and isinstance(__UpperCamelCase	,			__UpperCamelCase   ) for x in args   ):
											raise ValueError('All arguments must be non-negative integers'   )

						# decision | slack | artificial
						__magic_name__				=     ['x', 's', 'a']
						__magic_name__				=     []
						for i in range(3   ):
											for j in range(args[i]   ):
																titles.append(string_starts[i] + str(j + 1   )   )
						titles.append('RHS'   )
						return titles







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]	,			__UpperCamelCase:			np.ndarray   ):

						'''simple docstring'''





						__magic_name__				=     self.objectives[-1]

						# Find entries of highest magnitude in objective rows
						__magic_name__				=     (objective == 'min') - (objective == 'max')
						__magic_name__				=     np.argmax(sign * tableau[0, : self.n_vars]   )

						# Choice is only valid if below 0 for maximise, and above for minimise
						if sign * self.tableau[0, col_idx] <= 0:
											__magic_name__				=     True
											return 0, 0

						# Pivot row is chosen as having the lowest quotient when elements of
						# the pivot column divide the right-hand side

						# Slice excluding the objective rows
						__magic_name__				=     slice(self.n_stages	,			self.n_rows   )

						# RHS
						__magic_name__				=     tableau[s, -1]

						# Elements of pivot column within slice
						__magic_name__				=     tableau[s, col_idx]

						# Array filled with nans
						__magic_name__				=     np.full(self.n_rows - self.n_stages	,			np.nan   )

						# If element in pivot column is greater than zeron_stages, return
						# quotient or nan otherwise
						__magic_name__				=     np.divide(__UpperCamelCase	,			__UpperCamelCase	,			out=__UpperCamelCase	,			where=divisor > 0   )

						# Arg of minimum quotient excluding the nan values. n_stages is added
						# to compensate for earlier exclusion of objective columns
						__magic_name__				=     np.nanargmin(__UpperCamelCase   ) + self.n_stages
						return row_idx, col_idx







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			int	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						__magic_name__				=     tableau[row_idx].copy()

						__magic_name__				=     piv_row[col_idx]

						# Entry becomes 1
						piv_row *= 1 / piv_val

						# Variable in pivot column becomes basic, ie the only non-zero entry
						for idx, coeff in enumerate(tableau[:, col_idx]   ):
											tableau[idx] += -coeff * piv_row
						__magic_name__				=     piv_row
						return tableau







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			__UpperCamelCase:			np.ndarray   ):

						'''simple docstring'''





						self.objectives.pop()

						if not self.objectives:
											return tableau

						# Slice containing ids for artificial columns
						__magic_name__				=     slice(-self.n_art_vars - 1	,			-1   )

						# Delete the artificial variable columns
						__magic_name__				=     np.delete(__UpperCamelCase	,			__UpperCamelCase	,			axis=1   )

						# Delete the objective row of the first stage
						__magic_name__				=     np.delete(__UpperCamelCase	,			0	,			axis=0   )

						__magic_name__				=     1
						self.n_rows -= 1
						__magic_name__				=     0
						__magic_name__				=     False
						return tableau







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						for _ in range(1_00   ):
											# Completion of each stage removes an objective. If both stages
											# are complete, then no objectives are left
											if not self.objectives:
																__magic_name__				=     self.generate_col_titles(
																    self.n_vars	,			self.n_slack	,			self.n_art_vars   )

																# Find the values of each variable at optimal solution
																return self.interpret_tableau(self.tableau	,			self.col_titles   )

											__magic_name__,						__magic_name__				=     self.find_pivot(self.tableau   )

											# If there are no more negative values in objective row
											if self.stop_iter:
																# Delete artificial variable columns and rows. Update attributes
																__magic_name__				=     self.change_stage(self.tableau   )
											else:
																__magic_name__				=     self.pivot(self.tableau	,			__UpperCamelCase	,			__UpperCamelCase   )
						return {}







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			list[str]   ):

						'''simple docstring'''





						__magic_name__				=     {'P': abs(tableau[0, -1]   )}

						for i in range(self.n_vars   ):
											# Gives ids of nonzero entries in the ith column
											__magic_name__				=     np.nonzero(tableau[:, i]   )
											__magic_name__				=     len(nonzero[0]   )

											# First entry in the nonzero ids
											__magic_name__				=     nonzero[0][0]
											__magic_name__				=     tableau[nonzero_rowidx, i]

											# If there is only one nonzero value in column, which is one
											if n_nonzero == nonzero_val == 1:
																__magic_name__				=     tableau[nonzero_rowidx, -1]
																__magic_name__				=     rhs_val

        # Check for basic variables
						for title in col_titles:
											# Don't add RHS or slack variables to output dict
											if title[0] not in "R-s-a":
																output_dict.setdefault(__UpperCamelCase	,			0   )
						return output_dict


if __name__ == "__main__":
						import doctest

						doctest.testmod()




