





"""simple docstring"""




import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp


def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase = 40_96				)     ->			Dict:






      '''simple docstring'''


      lowercase  :      Any					=   key.shape[-3:]
      lowercase  :      List[Any]					=   value.shape[-1]
      lowercase  :      Dict					=   min(_UpperCAmelCase	,      _UpperCAmelCase				)
      lowercase  :      int					=   query / jnp.sqrt(_UpperCAmelCase				)

      @functools.partial(jax.checkpoint	,      prevent_cse=_UpperCAmelCase				)
      def summarize_chunk(_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				):
            lowercase  :      List[str]					=   jnp.einsum('...qhd,...khd->...qhk'	,      _UpperCAmelCase	,      _UpperCAmelCase	,      precision=_UpperCAmelCase				)

            lowercase  :      Union[str, Any]					=   jnp.max(_UpperCAmelCase	,      axis=-1	,      keepdims=_UpperCAmelCase				)
            lowercase  :      Dict					=   jax.lax.stop_gradient(_UpperCAmelCase				)
            lowercase  :      Optional[int]					=   jnp.exp(attn_weights - max_score				)

            lowercase  :      Tuple					=   jnp.einsum('...vhf,...qhv->...qhf'	,      _UpperCAmelCase	,      _UpperCAmelCase	,      precision=_UpperCAmelCase				)
            lowercase  :      List[Any]					=   jnp.einsum('...qhk->...qh'	,      _UpperCAmelCase				)

            return (exp_values, exp_weights.sum(axis=-1				), max_score)

      def chunk_scanner(_UpperCAmelCase				):
            # julienne key array
            lowercase  :      Tuple					=   jax.lax.dynamic_slice(
                operand=_UpperCAmelCase	,      start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]	,      slice_sizes=list(key.shape[:-3]				) + [key_chunk_size, num_heads, k_features]	,      )

            # julienne value array
            lowercase  :      List[str]					=   jax.lax.dynamic_slice(
                operand=_UpperCAmelCase	,      start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]	,      slice_sizes=list(value.shape[:-3]				) + [key_chunk_size, num_heads, v_features]	,      )

            return summarize_chunk(_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)

      lowercase  :      List[str]					=   jax.lax.map(f=_UpperCAmelCase	,      xs=jnp.arange(0	,      _UpperCAmelCase	,      _UpperCAmelCase				)				)

      lowercase  :      Tuple					=   jnp.max(_UpperCAmelCase	,      axis=0	,      keepdims=_UpperCAmelCase				)
      lowercase  :      Tuple					=   jnp.exp(chunk_max - global_max				)

      chunk_values *= jnp.expand_dims(_UpperCAmelCase	,      axis=-1				)
      chunk_weights *= max_diffs

      lowercase  :      Dict					=   chunk_values.sum(axis=0				)
      lowercase  :      Union[str, Any]					=   jnp.expand_dims(_UpperCAmelCase	,      -1				).sum(axis=0				)

      return all_values / all_weights


def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase=jax.lax.Precision.HIGHEST	,      _UpperCAmelCase = 10_24	,      _UpperCAmelCase = 40_96				)     ->			int:






      '''simple docstring'''


      lowercase  :      str					=   query.shape[-3:]

      def chunk_scanner(_UpperCAmelCase	,      _UpperCAmelCase				):
            # julienne query array
            lowercase  :      List[Any]					=   jax.lax.dynamic_slice(
                operand=_UpperCAmelCase	,      start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]	,      slice_sizes=list(query.shape[:-3]				) + [min(_UpperCAmelCase	,      _UpperCAmelCase				), num_heads, q_features]	,      )

            return (
                chunk_idx + query_chunk_size,  # unused ignore it
                _query_chunk_attention(
                    query=_UpperCAmelCase	,      key=_UpperCAmelCase	,      value=_UpperCAmelCase	,      precision=_UpperCAmelCase	,      key_chunk_size=_UpperCAmelCase				),
            )

      lowercase  :      Optional[Any]					=   jax.lax.scan(
          f=_UpperCAmelCase	,      init=0	,      xs=_UpperCAmelCase	,      length=math.ceil(num_q / query_chunk_size				)				)

      return jnp.concatenate(_UpperCAmelCase	,      axis=-3				)  # fuse the chunked result back


class   a__		(						nn.Module     ):
   _lowerCamelCase												=				42
   _lowerCamelCase												=				8
   _lowerCamelCase												=				64
   _lowerCamelCase												=				0.0
   _lowerCamelCase												=				False
   _lowerCamelCase												=				jnp.floataa


   def        lowercase				(							self      :							Any			)    ->       List[str]:
         lowercase  :      int					=   self.dim_head * self.heads
         lowercase  :      List[str]					=   self.dim_head**-0.5

         # Weights were exported with old names {to_q, to_k, to_v, to_out}
         lowercase  :      Optional[Any]					=   nn.Dense(__A,     use_bias=__A,     dtype=self.dtype,     name='to_q'			)
         lowercase  :      Optional[Any]					=   nn.Dense(__A,     use_bias=__A,     dtype=self.dtype,     name='to_k'			)
         lowercase  :      List[str]					=   nn.Dense(__A,     use_bias=__A,     dtype=self.dtype,     name='to_v'			)

         lowercase  :      List[Any]					=   nn.Dense(self.query_dim,     dtype=self.dtype,     name='to_out_0'			)
         lowercase  :      Dict					=   nn.Dropout(rate=self.dropout			)


   def        lowercase				(							self      :							Union[str, Any],     lowerCAmelCase      :							List[Any]			)    ->       Optional[Any]:
         lowercase  :      Optional[int]					=   tensor.shape
         lowercase  :      List[str]					=   self.heads
         lowercase  :      List[Any]					=   tensor.reshape(__A,     __A,     __A,     dim // head_size			)
         lowercase  :      str					=   jnp.transpose(__A,     (0, 2, 1, 3)			)
         lowercase  :      Optional[int]					=   tensor.reshape(batch_size * head_size,     __A,     dim // head_size			)
         return tensor


   def        lowercase				(							self      :							Tuple,     lowerCAmelCase      :							List[str]			)    ->       str:
         lowercase  :      int					=   tensor.shape
         lowercase  :      Dict					=   self.heads
         lowercase  :      List[str]					=   tensor.reshape(batch_size // head_size,     __A,     __A,     __A			)
         lowercase  :      int					=   jnp.transpose(__A,     (0, 2, 1, 3)			)
         lowercase  :      List[Any]					=   tensor.reshape(batch_size // head_size,     __A,     dim * head_size			)
         return tensor






   def __call__(							self      :							Any,     lowerCAmelCase      :							Any,     lowerCAmelCase      :							List[str]=None,     lowerCAmelCase      :							Optional[int]=True			)    ->       List[str]:
         lowercase  :      List[str]					=   hidden_states if context is None else context

         lowercase  :      Optional[int]					=   self.query(__A			)
         lowercase  :      Dict					=   self.key(__A			)
         lowercase  :      Optional[int]					=   self.value(__A			)

         lowercase  :      str					=   self.reshape_heads_to_batch_dim(__A			)
         lowercase  :      List[Any]					=   self.reshape_heads_to_batch_dim(__A			)
         lowercase  :      Tuple					=   self.reshape_heads_to_batch_dim(__A			)

         if self.use_memory_efficient_attention:
               lowercase  :      str					=   query_states.transpose(1,     0,     2			)
               lowercase  :      Dict					=   key_states.transpose(1,     0,     2			)
               lowercase  :      Dict					=   value_states.transpose(1,     0,     2			)

               # this if statement create a chunk size for each layer of the unet
               # the chunk size is equal to the query_length dimension of the deepest layer of the unet

               lowercase  :      Optional[Any]					=   query_states.shape[-3]
               if flatten_latent_dim % 64 == 0:
                     lowercase  :      Optional[int]					=   int(flatten_latent_dim / 64			)
               elif flatten_latent_dim % 16 == 0:
                     lowercase  :      Tuple					=   int(flatten_latent_dim / 16			)
               elif flatten_latent_dim % 4 == 0:
                     lowercase  :      int					=   int(flatten_latent_dim / 4			)
               else:
                     lowercase  :      int					=   int(__A			)

               lowercase  :      Union[str, Any]					=   jax_memory_efficient_attention(
                   __A,     __A,     __A,     query_chunk_size=__A,     key_chunk_size=4096 * 4			)

               lowercase  :      Dict					=   hidden_states.transpose(1,     0,     2			)
         else:
               # compute attentions
               lowercase  :      Union[str, Any]					=   jnp.einsum('b i d, b j d->b i j',     __A,     __A			)
               lowercase  :      Tuple					=   attention_scores * self.scale
               lowercase  :      Optional[int]					=   nn.softmax(__A,     axis=2			)

               # attend to values
               lowercase  :      List[str]					=   jnp.einsum('b i j, b j d -> b i d',     __A,     __A			)

         lowercase  :      str					=   self.reshape_batch_dim_to_heads(__A			)
         lowercase  :      str					=   self.proj_attn(__A			)
         return self.dropout_layer(__A,     deterministic=__A			)




class   a__		(						nn.Module     ):
   _lowerCamelCase												=				42
   _lowerCamelCase												=				42
   _lowerCamelCase												=				42
   _lowerCamelCase												=				0.0
   _lowerCamelCase												=				False
   _lowerCamelCase												=				jnp.floataa
   _lowerCamelCase												=				False


   def        lowercase				(							self      :							Any			)    ->       Union[str, Any]:
         lowercase  :      str					=   FlaxAttention(
             self.dim,     self.n_heads,     self.d_head,     self.dropout,     self.use_memory_efficient_attention,     dtype=self.dtype			)
         # cross attention
         lowercase  :      str					=   FlaxAttention(
             self.dim,     self.n_heads,     self.d_head,     self.dropout,     self.use_memory_efficient_attention,     dtype=self.dtype			)
         lowercase  :      Union[str, Any]					=   FlaxFeedForward(dim=self.dim,     dropout=self.dropout,     dtype=self.dtype			)
         lowercase  :      List[Any]					=   nn.LayerNorm(epsilon=1e-5,     dtype=self.dtype			)
         lowercase  :      Tuple					=   nn.LayerNorm(epsilon=1e-5,     dtype=self.dtype			)
         lowercase  :      Tuple					=   nn.LayerNorm(epsilon=1e-5,     dtype=self.dtype			)
         lowercase  :      int					=   nn.Dropout(rate=self.dropout			)






   def __call__(							self      :							Dict,     lowerCAmelCase      :							int,     lowerCAmelCase      :							List[Any],     lowerCAmelCase      :							Tuple=True			)    ->       int:
         lowercase  :      Dict					=   hidden_states
         if self.only_cross_attention:
               lowercase  :      Tuple					=   self.attna(self.norma(__A			),     __A,     deterministic=__A			)
         else:
               lowercase  :      Optional[Any]					=   self.attna(self.norma(__A			),     deterministic=__A			)
         lowercase  :      List[Any]					=   hidden_states + residual

         # cross attention
         lowercase  :      Tuple					=   hidden_states
         lowercase  :      Optional[int]					=   self.attna(self.norma(__A			),     __A,     deterministic=__A			)
         lowercase  :      str					=   hidden_states + residual

         # feed forward
         lowercase  :      Optional[int]					=   hidden_states
         lowercase  :      List[str]					=   self.ff(self.norma(__A			),     deterministic=__A			)
         lowercase  :      Tuple					=   hidden_states + residual

         return self.dropout_layer(__A,     deterministic=__A			)




class   a__		(						nn.Module     ):
   _lowerCamelCase												=				42
   _lowerCamelCase												=				42
   _lowerCamelCase												=				42
   _lowerCamelCase												=				1
   _lowerCamelCase												=				0.0
   _lowerCamelCase												=				False
   _lowerCamelCase												=				False
   _lowerCamelCase												=				jnp.floataa
   _lowerCamelCase												=				False


   def        lowercase				(							self      :							Optional[int]			)    ->       str:
         lowercase  :      List[Any]					=   nn.GroupNorm(num_groups=32,     epsilon=1e-5			)

         lowercase  :      List[str]					=   self.n_heads * self.d_head
         if self.use_linear_projection:
               lowercase  :      Union[str, Any]					=   nn.Dense(__A,     dtype=self.dtype			)
         else:
               lowercase  :      Optional[Any]					=   nn.Conv(
                   __A,     kernel_size=(1, 1),     strides=(1, 1),     padding='VALID',     dtype=self.dtype,     )

         lowercase  :      Tuple					=   [
             FlaxBasicTransformerBlock(
                 __A,     self.n_heads,     self.d_head,     dropout=self.dropout,     only_cross_attention=self.only_cross_attention,     dtype=self.dtype,     use_memory_efficient_attention=self.use_memory_efficient_attention,     )
             for _ in range(self.depth			)
         ]

         if self.use_linear_projection:
               lowercase  :      Any					=   nn.Dense(__A,     dtype=self.dtype			)
         else:
               lowercase  :      Optional[int]					=   nn.Conv(
                   __A,     kernel_size=(1, 1),     strides=(1, 1),     padding='VALID',     dtype=self.dtype,     )

         lowercase  :      Dict					=   nn.Dropout(rate=self.dropout			)






   def __call__(							self      :							Union[str, Any],     lowerCAmelCase      :							Union[str, Any],     lowerCAmelCase      :							Optional[Any],     lowerCAmelCase      :							int=True			)    ->       Optional[Any]:
         lowercase  :      Optional[Any]					=   hidden_states.shape
         lowercase  :      Dict					=   hidden_states
         lowercase  :      int					=   self.norm(__A			)
         if self.use_linear_projection:
               lowercase  :      Tuple					=   hidden_states.reshape(__A,     height * width,     __A			)
               lowercase  :      int					=   self.proj_in(__A			)
         else:
               lowercase  :      Dict					=   self.proj_in(__A			)
               lowercase  :      Optional[int]					=   hidden_states.reshape(__A,     height * width,     __A			)

         for transformer_block in self.transformer_blocks:
               lowercase  :      Dict					=   transformer_block(__A,     __A,     deterministic=__A			)

         if self.use_linear_projection:
               lowercase  :      List[Any]					=   self.proj_out(__A			)
               lowercase  :      Any					=   hidden_states.reshape(__A,     __A,     __A,     __A			)
         else:
               lowercase  :      Dict					=   hidden_states.reshape(__A,     __A,     __A,     __A			)
               lowercase  :      List[str]					=   self.proj_out(__A			)

         lowercase  :      Any					=   hidden_states + residual
         return self.dropout_layer(__A,     deterministic=__A			)




class   a__		(						nn.Module     ):
   _lowerCamelCase												=				42
   _lowerCamelCase												=				0.0
   _lowerCamelCase												=				jnp.floataa


   def        lowercase				(							self      :							Any			)    ->       int:
         lowercase  :      Union[str, Any]					=   FlaxGEGLU(self.dim,     self.dropout,     self.dtype			)
         lowercase  :      Any					=   nn.Dense(self.dim,     dtype=self.dtype			)






   def __call__(							self      :							Dict,     lowerCAmelCase      :							Tuple,     lowerCAmelCase      :							List[Any]=True			)    ->       Tuple:
         lowercase  :      str					=   self.net_a(__A,     deterministic=__A			)
         lowercase  :      Union[str, Any]					=   self.net_a(__A			)
         return hidden_states




class   a__		(						nn.Module     ):
   _lowerCamelCase												=				42
   _lowerCamelCase												=				0.0
   _lowerCamelCase												=				jnp.floataa


   def        lowercase				(							self      :							str			)    ->       List[Any]:
         lowercase  :      List[str]					=   self.dim * 4
         lowercase  :      Dict					=   nn.Dense(inner_dim * 2,     dtype=self.dtype			)
         lowercase  :      List[str]					=   nn.Dropout(rate=self.dropout			)






   def __call__(							self      :							int,     lowerCAmelCase      :							Optional[Any],     lowerCAmelCase      :							Any=True			)    ->       Optional[int]:
         lowercase  :      Any					=   self.proj(__A			)
         lowercase  :      List[str]					=   jnp.split(__A,     2,     axis=2			)
         return self.dropout_layer(hidden_linear * nn.gelu(__A			),     deterministic=__A			)





"""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__		:


   def __init__(							self      :							Any,     lowerCAmelCase      :							List[Any],     lowerCAmelCase      :							Dict,     lowerCAmelCase      :							Optional[Any]			)    ->       Optional[Any]:
         lowercase  :      Optional[Any]					=   cuda
         lowercase  :      Optional[Any]					=   AutoConfig.from_pretrained(lowerCAmelCase			)
         lowercase  :      str					=   AutoModelForCTC.from_pretrained(lowerCAmelCase			)
         self.model.eval()
         if self.cuda:
               self.model.to(device='cuda'			)
         lowercase  :      Tuple					=   AutoProcessor.from_pretrained(lowerCAmelCase			)
         lowercase  :      Tuple					=   torchaudio.transforms.Resample(lowerCAmelCase,     16000			)
         lowercase  :      Any					=   0
         lowercase  :      Tuple					=   list(self.processor.tokenizer.get_vocab().keys()			)
         for i in range(len(lowerCAmelCase			)			):
               if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                     lowercase  :      Dict					=   i
         print('Blank Token id [PAD]/<pad>',     lowerCAmelCase			)
         lowercase  :      List[str]					=   blank_id


   def        lowercase				(							self      :							List[Any],     lowerCAmelCase      :							Optional[int]			)    ->       Optional[Any]:
         lowercase	,    lowercase  :      Any					=   torchaudio.load(lowerCAmelCase			)
         lowercase  :      List[Any]					=   self.resampler(lowerCAmelCase			).squeeze().numpy()
         return speech


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

         lowercase  :      Dict					=   self.speech_file_to_array_fn(item['wav_path']			)
         lowercase  :      List[str]					=   self.processor(lowerCAmelCase,     sampling_rate=16000,     return_tensors='pt',     padding=lowerCAmelCase			)
         if self.cuda:
               lowercase  :      Union[str, Any]					=   inputs.to(device='cuda'			)

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

         # get the emission probability at frame level
         lowercase  :      Dict					=   torch.log_softmax(lowerCAmelCase,     dim=-1			)
         lowercase  :      Optional[int]					=   emissions[0].cpu().detach()

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

         lowercase  :      Optional[int]					=   {c: i for i, c in enumerate(lowerCAmelCase			)}
         lowercase  :      Union[str, Any]					=   []
         for c in transcript:
               if c in dictionary:
                     tokens.append(dictionary[c]			)

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

         lowercase  :      Any					=   get_trellis(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)


         @dataclass
         class   a__		:
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42

         def backtrack(lowerCAmelCase      :							Any,     lowerCAmelCase      :							Tuple,     lowerCAmelCase      :							str,     lowerCAmelCase      :							Any=0			):
               lowercase  :      Tuple					=   trellis.size(1			) - 1
               lowercase  :      Optional[int]					=   torch.argmax(trellis[:, j]			).item()

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

                     # 2. Store the path with frame-wise probability.
                     lowercase  :      str					=   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  :      int					=   backtrack(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)


         @dataclass
         class   a__		:
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42


            def __repr__(							self      :							Union[str, Any]			)    ->       Optional[Any]:
                  return f'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''






            @property
            def        lowercase				(							self      :							Tuple			)    ->       int:
                  return self.end - self.start

         def merge_repeats(lowerCAmelCase      :							Optional[Any]			):
               lowercase	,    lowercase  :      Tuple					=   0, 0
               lowercase  :      Union[str, Any]					=   []
               while ia < len(lowerCAmelCase			):
                     while ia < len(lowerCAmelCase			) and path[ia].token_index == path[ia].token_index:
                           ia += 1
                     lowercase  :      List[str]					=   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  :      List[Any]					=   ia
               return segments

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






   def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Tuple,     lowerCAmelCase      :							int			)    ->       Optional[Any]:
         if not os.path.exists(lowerCAmelCase			):
               os.makedirs(lowerCAmelCase			)

         # load text file
         lowercase  :      Dict					=   open(lowerCAmelCase,     encoding='utf8'			).readlines()

         lowercase  :      Optional[Any]					=   []
         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  :      int					=   line.strip().split('\t'			)
               lowercase  :      List[Any]					=   os.path.join(lowerCAmelCase,     wav_name + '.wav'			)
               lowercase  :      List[Any]					=   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   lowercase__     (			)     ->			Dict:






      '''simple docstring'''


      lowercase  :      Union[str, Any]					=   argparse.ArgumentParser()

      parser.add_argument(
          '--model_name'	,      type=_UpperCAmelCase	,      default='arijitx/wav2vec2-xls-r-300m-bengali'	,      help='wav2vec model name'				)
      parser.add_argument('--wav_dir'	,      type=_UpperCAmelCase	,      default='./wavs'	,      help='directory containing wavs'				)
      parser.add_argument('--text_file'	,      type=_UpperCAmelCase	,      default='script.txt'	,      help='file containing text'				)
      parser.add_argument('--input_wavs_sr'	,      type=_UpperCAmelCase	,      default=1_60_00	,      help='sampling rate of input audios'				)
      parser.add_argument(
          '--output_dir'	,      type=_UpperCAmelCase	,      default='./out_alignment'	,      help='output directory containing the alignment files'				)
      parser.add_argument('--cuda'	,      action='store_true'				)

      lowercase  :      List[Any]					=   parser.parse_args()

      lowercase  :      Dict					=   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()
