





"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowerCamelCase_     :						str						=					{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  for attribute in key.split('.'					):
    A_  :       Union[str, Any]										=  getattr(_UpperCAmelCase ,					_UpperCAmelCase					)

  if weight_type is not None:
    A_  :       Optional[int]										=  getattr(_UpperCAmelCase ,					_UpperCAmelCase					).shape
  else:
    A_  :       Union[str, Any]										=  hf_pointer.shape

  assert hf_shape == value.shape, (
      f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
      f""" {value.shape} for {full_name}"""
  )

  if weight_type == "weight":
    A_  :       int										=  value
  elif weight_type == "weight_g":
    A_  :       Union[str, Any]										=  value
  elif weight_type == "weight_v":
    A_  :       Union[str, Any]										=  value
  elif weight_type == "bias":
    A_  :       Optional[int]										=  value
  else:
    A_  :       str										=  value

  logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""					)





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Dict										=  []
  A_  :       Dict										=  fairseq_model.state_dict()

  A_  :       Dict										=  hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    A_  :       Union[str, Any]										=  False
    if "conv_layers" in name:
      load_conv_layer(
          _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					hf_model.config.feat_extract_norm == 'group' ,					)
      A_  :       str										=  True
    else:
      for key, mapped_key in MAPPING.items():
        A_  :       List[str]										=  'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

        if key in name or key.split('w2v_model.'					)[-1] == name.split('.'					)[0]:
          A_  :       Any										=  True
          if "*" in mapped_key:
            A_  :       Dict										=  name.split(_UpperCAmelCase					)[0].split('.'					)[-2]
            A_  :       str										=  mapped_key.replace('*' ,					_UpperCAmelCase					)
          if "weight_g" in name:
            A_  :       Optional[Any]										=  'weight_g'
          elif "weight_v" in name:
            A_  :       int										=  'weight_v'
          elif "weight" in name:
            A_  :       List[Any]										=  'weight'
          elif "bias" in name:
            A_  :       Any										=  'bias'
          else:
            A_  :       Optional[Any]										=  None
          set_recursively(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)
        continue
    if not is_used:
      unused_weights.append(_UpperCAmelCase					)

  logger.warning(f"""Unused weights: {unused_weights}"""					)





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       int										=  full_name.split('conv_layers.'					)[-1]
  A_  :       Union[str, Any]										=  name.split('.'					)
  A_  :       List[str]										=  int(items[0]					)
  A_  :       Any										=  int(items[1]					)

  if type_id == 0:
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
          f"""{full_name} has size {value.shape}, but"""
          f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
      )
      A_  :       List[str]										=  value
      logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
          f"""{full_name} has size {value.shape}, but"""
          f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
      )
      A_  :       str										=  value
      logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
          f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
          " found."
      )
      A_  :       Union[str, Any]										=  value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
          f"""{full_name} has size {value.shape}, but"""
          f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
      )
      A_  :       List[Any]										=  value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
  else:
    unused_weights.append(_UpperCAmelCase					)





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[str]										=  SEWConfig()
  if is_finetuned:
    A_  :       Tuple										=  model.wav_encoder.wav_model.cfg
  else:
    A_  :       List[Any]										=  model.cfg

  A_  :       Tuple										=  fs_config.conv_bias
  A_  :       Optional[int]										=  eval(fs_config.conv_feature_layers					)
  A_  :       Union[str, Any]										=  [x[0] for x in conv_layers]
  A_  :       Tuple										=  [x[1] for x in conv_layers]
  A_  :       int										=  [x[2] for x in conv_layers]
  A_  :       Tuple										=  'gelu'
  A_  :       Dict										=  'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
  A_  :       Dict										=  0.0
  A_  :       List[str]										=  fs_config.activation_fn.name
  A_  :       Dict										=  fs_config.encoder_embed_dim
  A_  :       List[str]										=  0.02
  A_  :       Tuple										=  fs_config.encoder_ffn_embed_dim
  A_  :       List[Any]										=  1E-5
  A_  :       Union[str, Any]										=  fs_config.encoder_layerdrop
  A_  :       int										=  fs_config.encoder_attention_heads
  A_  :       Dict										=  fs_config.conv_pos_groups
  A_  :       List[Any]										=  fs_config.conv_pos
  A_  :       int										=  len(_UpperCAmelCase					)
  A_  :       Dict										=  fs_config.encoder_layers
  A_  :       Union[str, Any]										=  fs_config.squeeze_factor

  # take care of any params that are overridden by the Wav2VecCtc model
  if is_finetuned:
    A_  :       str										=  model.cfg
    A_  :       Optional[int]										=  fs_config.final_dropout
    A_  :       Optional[Any]										=  fs_config.layerdrop
  A_  :       List[str]										=  fs_config.activation_dropout
  A_  :       Union[str, Any]										=  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
  A_  :       int										=  fs_config.attention_dropout
  A_  :       Tuple										=  fs_config.dropout_input
  A_  :       str										=  fs_config.dropout
  A_  :       str										=  fs_config.mask_channel_length
  A_  :       Optional[int]										=  fs_config.mask_channel_prob
  A_  :       Union[str, Any]										=  fs_config.mask_length
  A_  :       Any										=  fs_config.mask_prob

  A_  :       List[str]										=  'Wav2Vec2FeatureExtractor'
  A_  :       int										=  'Wav2Vec2CTCTokenizer'

  return config





@torch.no_grad()
def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=None ,					_UpperCAmelCase=None ,					_UpperCAmelCase=True					):






  """simple docstring"""





  if is_finetuned:
    A_						,       A_						,       A_  :       Dict										=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path] ,					arg_overrides={'data': '/'.join(dict_path.split('/'					)[:-1]					)}					)
  else:
    A_						,       A_						,       A_  :       Tuple										=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

  if config_path is not None:
    A_  :       Tuple										=  SEWConfig.from_pretrained(_UpperCAmelCase					)
  else:
    A_  :       Optional[Any]										=  convert_config(model[0] ,					_UpperCAmelCase					)
  A_  :       Optional[int]										=  model[0].eval()

  A_  :       Dict										=  True if config.feat_extract_norm == 'layer' else False
  A_  :       Tuple										=  WavaVecaFeatureExtractor(
      feature_size=1 ,					sampling_rate=16000 ,					padding_value=0 ,					do_normalize=_UpperCAmelCase ,					return_attention_mask=_UpperCAmelCase ,					)

  if is_finetuned:
    if dict_path:
      A_  :       Any										=  Dictionary.load(_UpperCAmelCase					)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      A_  :       Dict										=  target_dict.pad_index
      A_  :       str										=  target_dict.bos_index
      A_  :       Union[str, Any]										=  target_dict.pad_index
      A_  :       List[Any]										=  target_dict.bos_index
      A_  :       int										=  target_dict.eos_index
      A_  :       Union[str, Any]										=  len(target_dict.symbols					)
      A_  :       List[str]										=  os.path.join(_UpperCAmelCase ,					'vocab.json'					)
      if not os.path.isdir(_UpperCAmelCase					):
        logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_UpperCAmelCase					)					)
        return
      os.makedirs(_UpperCAmelCase ,					exist_ok=_UpperCAmelCase					)
      with open(_UpperCAmelCase ,					'w' ,					encoding='utf-8'					) as vocab_handle:
        json.dump(target_dict.indices ,					_UpperCAmelCase					)
      A_  :       Optional[int]										=  WavaVecaCTCTokenizer(
          _UpperCAmelCase ,					unk_token=target_dict.unk_word ,					pad_token=target_dict.pad_word ,					bos_token=target_dict.bos_word ,					eos_token=target_dict.eos_word ,					word_delimiter_token='|' ,					do_lower_case=_UpperCAmelCase ,					)
      A_  :       Union[str, Any]										=  WavaVecaProcessor(feature_extractor=_UpperCAmelCase ,					tokenizer=_UpperCAmelCase					)
      processor.save_pretrained(_UpperCAmelCase					)

    A_  :       List[Any]										=  SEWForCTC(_UpperCAmelCase					)
  else:
    A_  :       Dict										=  SEWModel(_UpperCAmelCase					)
    feature_extractor.save_pretrained(_UpperCAmelCase					)

  recursively_load_weights(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)

  hf_model.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
   lowerCamelCase_     :						Dict						=					argparse.ArgumentParser()
   parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
   parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
   parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
   parser.add_argument(
       '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
   )
   lowerCamelCase_     :						Dict						=					parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )






"""simple docstring"""




from heapq import heappop, heappush

import numpy as np





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					):






  """simple docstring"""





  A_						,       A_  :       List[str]										=  grid.shape
  A_  :       Optional[int]										=  [-1, 1, 0, 0]
  A_  :       str										=  [0, 0, -1, 1]
  if allow_diagonal:
    dx += [-1, -1, 1, 1]
    dy += [-1, 1, -1, 1]

  A_						,       A_  :       List[Any]										=  [(0, source)], set()
  A_  :       Optional[Any]										=  np.full((rows, cols) ,					np.inf					)
  A_  :       int										=  0
  A_  :       Optional[int]										=  np.empty((rows, cols) ,					dtype=_UpperCAmelCase					)
  A_  :       Optional[int]										=  None

  while queue:
    ((A_)						,       (A_))  :       str										=  heappop(_UpperCAmelCase					)
    if (x, y) in visited:
      continue
    visited.add((x, y)					)

    if (x, y) == destination:
      A_  :       int										=  []
      while (x, y) != source:
        path.append((x, y)					)
        A_						,       A_  :       List[Any]										=  predecessors[x, y]
      path.append(_UpperCAmelCase					)  # add the source manually
      path.reverse()
      return matrix[destination], path

    for i in range(len(_UpperCAmelCase					)					):
      A_						,       A_  :       Tuple										=  x + dx[i], y + dy[i]
      if 0 <= nx < rows and 0 <= ny < cols:
        A_  :       Union[str, Any]										=  grid[nx][ny]
        if next_node == 1 and matrix[nx, ny] > dist + 1:
          heappush(_UpperCAmelCase ,					(dist + 1, (nx, ny))					)
          A_  :       Optional[Any]										=  dist + 1
          A_  :       Optional[Any]										=  (x, y)

  return np.inf, []


if __name__ == "__main__":
   import doctest

   doctest.testmod()