





"""simple docstring"""
import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
       raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
__UpperCamelCase    :	List[str]						=     logging.get_logger(__name__)

__UpperCamelCase    :	Any						=     '''Hello world! cécé herlolip'''

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						str     ,    _UpperCAmelCase				:						str     ,    _UpperCAmelCase				:						bool ):
 lowerCAmelCase							,lowerCAmelCase													=   os.path.split(_UpperCAmelCase )
 lowerCAmelCase													=   DataaVecTextModel.from_pretrained(
     _UpperCAmelCase     ,    checkpoint_file=_UpperCAmelCase )
 dataavec.eval()  # disable dropout
 lowerCAmelCase													=   dataavec.models[0]
 lowerCAmelCase													=   dataavec_model.encoder.sentence_encoder
 lowerCAmelCase													=   DataaVecTextConfig(
     vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings     ,    hidden_size=dataavec_model.args.encoder_embed_dim     ,    num_hidden_layers=dataavec_model.args.encoder_layers     ,    num_attention_heads=dataavec_model.args.encoder_attention_heads     ,    intermediate_size=dataavec_model.args.encoder_ffn_embed_dim     ,    max_position_embeddings=514     ,    type_vocab_size=1     ,    layer_norm_eps=1e-5     ,    )
 if classification_head:
  lowerCAmelCase													=   dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
 print('Our BERT config:'     ,    _UpperCAmelCase )

 lowerCAmelCase													=   DataaVecTextForSequenceClassification(_UpperCAmelCase ) if classification_head else DataaVecTextForMaskedLM(_UpperCAmelCase )
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 lowerCAmelCase													=   dataavec_sent_encoder.embed_tokens.weight
 lowerCAmelCase													=   dataavec_sent_encoder.embed_positions.weight
 lowerCAmelCase													=   torch.zeros_like(
     model.dataavec_text.embeddings.token_type_embeddings.weight )  # just zero them out b/c data2vec doesn't use them.
 lowerCAmelCase													=   dataavec_sent_encoder.layernorm_embedding.weight
 lowerCAmelCase													=   dataavec_sent_encoder.layernorm_embedding.bias

 for i in range(config.num_hidden_layers ):
  # Encoder: start of layer
  lowerCAmelCase													=   model.dataavec_text.encoder.layer[i]
  lowerCAmelCase													=   dataavec_sent_encoder.layers[i]

  # self attention
  lowerCAmelCase													=   layer.attention.self
  assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
      (config.hidden_size, config.hidden_size) ), (
      "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
      F' {torch.Size((config.hidden_size, config.hidden_size) )}'
  )
  assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
      (config.hidden_size, config.hidden_size) ), (
      "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
      F' {torch.Size((config.hidden_size, config.hidden_size) )}'
  )
  assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
      (config.hidden_size, config.hidden_size) ), (
      "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
      F' {torch.Size((config.hidden_size, config.hidden_size) )}'
  )

  lowerCAmelCase													=   dataavec_layer.self_attn.q_proj.weight
  lowerCAmelCase													=   dataavec_layer.self_attn.q_proj.bias
  lowerCAmelCase													=   dataavec_layer.self_attn.k_proj.weight
  lowerCAmelCase													=   dataavec_layer.self_attn.k_proj.bias
  lowerCAmelCase													=   dataavec_layer.self_attn.v_proj.weight
  lowerCAmelCase													=   dataavec_layer.self_attn.v_proj.bias

  # self-attention output
  lowerCAmelCase													=   layer.attention.output
  assert (
      self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
  ), F'Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'
  lowerCAmelCase													=   dataavec_layer.self_attn.out_proj.weight
  lowerCAmelCase													=   dataavec_layer.self_attn.out_proj.bias
  lowerCAmelCase													=   dataavec_layer.self_attn_layer_norm.weight
  lowerCAmelCase													=   dataavec_layer.self_attn_layer_norm.bias

  # intermediate
  lowerCAmelCase													=   layer.intermediate
  assert (
      intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
  ), F'Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'
  lowerCAmelCase													=   dataavec_layer.fca.weight
  lowerCAmelCase													=   dataavec_layer.fca.bias

  # output
  lowerCAmelCase													=   layer.output
  assert (
      bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
  ), F'Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'
  lowerCAmelCase													=   dataavec_layer.fca.weight
  lowerCAmelCase													=   dataavec_layer.fca.bias
  lowerCAmelCase													=   dataavec_layer.final_layer_norm.weight
  lowerCAmelCase													=   dataavec_layer.final_layer_norm.bias
  # end of layer

 if classification_head:
  lowerCAmelCase													=   dataavec.model.classification_heads['mnli'].dense.weight
  lowerCAmelCase													=   dataavec.model.classification_heads['mnli'].dense.bias
  lowerCAmelCase													=   dataavec.model.classification_heads['mnli'].out_proj.weight
  lowerCAmelCase													=   dataavec.model.classification_heads['mnli'].out_proj.bias
 else:
  # LM Head
  lowerCAmelCase													=   dataavec_model.encoder.lm_head.dense.weight
  lowerCAmelCase													=   dataavec_model.encoder.lm_head.dense.bias
  lowerCAmelCase													=   dataavec_model.encoder.lm_head.layer_norm.weight
  lowerCAmelCase													=   dataavec_model.encoder.lm_head.layer_norm.bias
  lowerCAmelCase													=   dataavec_model.encoder.lm_head.weight
  lowerCAmelCase													=   dataavec_model.encoder.lm_head.bias

 # Let's check that we get the same results.
 lowerCAmelCase													=   dataavec.encode(_UpperCAmelCase ).unsqueeze(0 )  # batch of size 1

 lowerCAmelCase													=   model(_UpperCAmelCase )[0]
 if classification_head:
  lowerCAmelCase													=   dataavec.model.classification_heads['mnli'](dataavec.extract_features(_UpperCAmelCase ) )
 else:
  lowerCAmelCase													=   dataavec_model(_UpperCAmelCase )[0]
 print(our_output.shape     ,    their_output.shape )
 lowerCAmelCase													=   torch.max(torch.abs(our_output - their_output ) ).item()
 print(F'max_absolute_diff = {max_absolute_diff}' )  # ~ 1e-7
 lowerCAmelCase													=   torch.allclose(_UpperCAmelCase     ,    _UpperCAmelCase     ,    atol=1e-3 )
 print('Do both models output the same tensors?'     ,    '🔥' if success else '💩' )
 if not success:
  raise Exception('Something went wRoNg' )

 pathlib.Path(_UpperCAmelCase ).mkdir(parents=_UpperCAmelCase     ,    exist_ok=_UpperCAmelCase )
 print(F'Saving model to {pytorch_dump_folder_path}' )
 model.save_pretrained(_UpperCAmelCase )


if __name__ == "__main__":
       __UpperCamelCase    :	Optional[int]						=     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       parser.add_argument(
           '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
       )
       __UpperCamelCase    :	Any						=     parser.parse_args()
       convert_dataavec_checkpoint_to_pytorch(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )












"""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							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   cuda
  lowerCAmelCase													=   AutoConfig.from_pretrained(_snake_case    )
  lowerCAmelCase													=   AutoModelForCTC.from_pretrained(_snake_case    )
  self.model.eval()
  if self.cuda:
   self.model.to(device='cuda'    )
  lowerCAmelCase													=   AutoProcessor.from_pretrained(_snake_case    )
  lowerCAmelCase													=   torchaudio.transforms.Resample(_snake_case							,					1_60_00    )
  lowerCAmelCase													=   0
  lowerCAmelCase													=   list(self.processor.tokenizer.get_vocab().keys()    )
  for i in range(len(_snake_case    )    ):
   if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
    lowerCAmelCase													=   i
  print('Blank Token id [PAD]/<pad>'							,					_snake_case    )
  lowerCAmelCase													=   blank_id



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase							,lowerCAmelCase													=   torchaudio.load(_snake_case    )
  lowerCAmelCase													=   self.resampler(_snake_case    ).squeeze().numpy()
  return speech



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






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

  lowerCAmelCase													=   self.speech_file_to_array_fn(item['wav_path']    )
  lowerCAmelCase													=   self.processor(_snake_case							,					sampling_rate=1_60_00							,					return_tensors='pt'							,					padding=_snake_case    )
  if self.cuda:
   lowerCAmelCase													=   inputs.to(device='cuda'    )

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

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

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

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

  def get_trellis(_snake_case							,					_snake_case							,					_snake_case=0    ):
   lowerCAmelCase													=   emission.size(0    )
   lowerCAmelCase													=   len(_snake_case    )

   # 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.
   lowerCAmelCase													=   torch.full((num_frames + 1, num_tokens + 1)							,					-float('inf'    )    )
   lowerCAmelCase													=   0
   for t in range(_snake_case    ):
    lowerCAmelCase													=   torch.maximum(
        # Score for staying at the same token
        trellis[t, 1:] + emission[t, blank_id]							,					trellis[t, :-1] + emission[t, tokens]							,					)
   return trellis

  lowerCAmelCase													=   get_trellis(_snake_case							,					_snake_case							,					_snake_case    )







  @dataclass
  class 			a       :
   snake_case__								=					42
   snake_case__								=					42
   snake_case__								=					42

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

   lowerCAmelCase													=   []
   for t in range(_snake_case							,					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.
    lowerCAmelCase													=   trellis[t - 1, j] + emission[t - 1, blank_id]
    # Score for token changing from C-1 at T-1 to J at T.
    lowerCAmelCase													=   trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

    # 2. Store the path with frame-wise probability.
    lowerCAmelCase													=   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							,					_snake_case    )    )

    # 3. Update the token
    if changed > stayed:
     j -= 1
     if j == 0:
      break
   else:
    raise ValueError('Failed to align'    )
   return path[::-1]

  lowerCAmelCase													=   backtrack(_snake_case							,					_snake_case							,					_snake_case							,					_snake_case    )







  @dataclass
  class 			a       :
   snake_case__								=					42
   snake_case__								=					42
   snake_case__								=					42
   snake_case__								=					42



   def __repr__(    self    ):







    """simple docstring"""






    return F'{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'



   @property
   def       UpperCamelCase__     (    self    ):







    """simple docstring"""






    return self.end - self.start

  def merge_repeats(_snake_case    ):
   lowerCAmelCase							,lowerCAmelCase													=   0, 0
   lowerCAmelCase													=   []
   while ia < len(_snake_case    ):
    while ia < len(_snake_case    ) and path[ia].token_index == path[ia].token_index:
     ia += 1
    lowerCAmelCase													=   sum(path[k].score for k in range(_snake_case							,					_snake_case    )    ) / (ia - ia)
    segments.append(
        Segment(
            transcript[path[ia].token_index]							,					path[ia].time_index							,					path[ia - 1].time_index + 1							,					_snake_case							,					)    )
    lowerCAmelCase													=   ia
   return segments

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



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






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

  # load text file
  lowerCAmelCase													=   open(_snake_case							,					encoding='utf8'    ).readlines()

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

   lowerCAmelCase							,lowerCAmelCase													=   line.strip().split('\t'    )
   lowerCAmelCase													=   os.path.join(_snake_case							,					wav_name + '.wav'    )
   lowerCAmelCase													=   os.path.join(_snake_case							,					wav_name + '.txt'    )

   items.append({'sent': sentence, 'wav_path': wav_path, 'out_path': out_path}    )
  print('Number of samples found in script file'							,					len(_snake_case    )    )

  for item in tqdm(_snake_case    ):
   self.align_single_sample(_snake_case    )


def 		_SCREAMING_SNAKE_CASE ():
 lowerCAmelCase													=   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_6000     ,    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' )

 lowerCAmelCase													=   parser.parse_args()

 lowerCAmelCase													=   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()





