
'''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()
snake_case_ :  Dict     =					logging.get_logger(__name__)

snake_case_ :  Any     =					{
    '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  __snake_case     (    _UpperCAmelCase					:       Optional[Any],      _UpperCAmelCase					:       List[str],      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Optional[Any],      _UpperCAmelCase					:       List[Any]):
   for attribute in key.split('''.'''):
      UpperCamelCase       =  getattr(_UpperCAmelCase,      _UpperCAmelCase)

   if weight_type is not None:
      UpperCamelCase       =  getattr(_UpperCAmelCase,      _UpperCAmelCase).shape
   else:
      UpperCamelCase       =  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":
      UpperCamelCase       =  value
   elif weight_type == "weight_g":
      UpperCamelCase       =  value
   elif weight_type == "weight_v":
      UpperCamelCase       =  value
   elif weight_type == "bias":
      UpperCamelCase       =  value
   else:
      UpperCamelCase       =  value

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



def  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       str):
   UpperCamelCase       =  []
   UpperCamelCase       =  fairseq_model.state_dict()

   UpperCamelCase       =  hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      UpperCamelCase       =  False
      if "conv_layers" in name:
         load_conv_layer(
             _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      hf_model.config.feat_extract_norm == '''group''',      )
         UpperCamelCase       =  True
      else:
         for key, mapped_key in MAPPING.items():
            UpperCamelCase       =  '''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]:
               UpperCamelCase       =  True
               if "*" in mapped_key:
                  UpperCamelCase       =  name.split(_UpperCAmelCase)[0].split('''.''')[-2]
                  UpperCamelCase       =  mapped_key.replace('''*''',      _UpperCAmelCase)
               if "weight_g" in name:
                  UpperCamelCase       =  '''weight_g'''
               elif "weight_v" in name:
                  UpperCamelCase       =  '''weight_v'''
               elif "weight" in name:
                  UpperCamelCase       =  '''weight'''
               elif "bias" in name:
                  UpperCamelCase       =  '''bias'''
               else:
                  UpperCamelCase       =  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  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       List[str]):
   UpperCamelCase       =  full_name.split('''conv_layers.''')[-1]
   UpperCamelCase       =  name.split('''.''')
   UpperCamelCase       =  int(items[0])
   UpperCamelCase       =  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.'
         )
         UpperCamelCase       =  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.'
         )
         UpperCamelCase       =  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."
         )
         UpperCamelCase       =  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.'
         )
         UpperCamelCase       =  value
         logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.')
   else:
      unused_weights.append(_UpperCAmelCase)



def  __snake_case     (    _UpperCAmelCase					:       List[str],      _UpperCAmelCase					:       Optional[int]):
   UpperCamelCase       =  SEWConfig()
   if is_finetuned:
      UpperCamelCase       =  model.wav_encoder.wav_model.cfg
   else:
      UpperCamelCase       =  model.cfg

   UpperCamelCase       =  fs_config.conv_bias
   UpperCamelCase       =  eval(fs_config.conv_feature_layers)
   UpperCamelCase       =  [x[0] for x in conv_layers]
   UpperCamelCase       =  [x[1] for x in conv_layers]
   UpperCamelCase       =  [x[2] for x in conv_layers]
   UpperCamelCase       =  '''gelu'''
   UpperCamelCase       =  '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
   UpperCamelCase       =  0.0
   UpperCamelCase       =  fs_config.activation_fn.name
   UpperCamelCase       =  fs_config.encoder_embed_dim
   UpperCamelCase       =  0.0_2
   UpperCamelCase       =  fs_config.encoder_ffn_embed_dim
   UpperCamelCase       =  1E-5
   UpperCamelCase       =  fs_config.encoder_layerdrop
   UpperCamelCase       =  fs_config.encoder_attention_heads
   UpperCamelCase       =  fs_config.conv_pos_groups
   UpperCamelCase       =  fs_config.conv_pos
   UpperCamelCase       =  len(_UpperCAmelCase)
   UpperCamelCase       =  fs_config.encoder_layers
   UpperCamelCase       =  fs_config.squeeze_factor

   # take care of any params that are overridden by the Wav2VecCtc model
   if is_finetuned:
      UpperCamelCase       =  model.cfg
      UpperCamelCase       =  fs_config.final_dropout
      UpperCamelCase       =  fs_config.layerdrop
   UpperCamelCase       =  fs_config.activation_dropout
   UpperCamelCase       =  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
   UpperCamelCase       =  fs_config.attention_dropout
   UpperCamelCase       =  fs_config.dropout_input
   UpperCamelCase       =  fs_config.dropout
   UpperCamelCase       =  fs_config.mask_channel_length
   UpperCamelCase       =  fs_config.mask_channel_prob
   UpperCamelCase       =  fs_config.mask_length
   UpperCamelCase       =  fs_config.mask_prob

   UpperCamelCase       =  '''Wav2Vec2FeatureExtractor'''
   UpperCamelCase       =  '''Wav2Vec2CTCTokenizer'''

   return config



@torch.no_grad()
def  __snake_case     (    _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       Union[str, Any]=None,      _UpperCAmelCase					:       Dict=None,      _UpperCAmelCase					:       Dict=True):

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

   if config_path is not None:
      UpperCamelCase       =  SEWConfig.from_pretrained(_UpperCAmelCase)
   else:
      UpperCamelCase       =  convert_config(model[0],      _UpperCAmelCase)
   UpperCamelCase       =  model[0].eval()

   UpperCamelCase       =  True if config.feat_extract_norm == '''layer''' else False
   UpperCamelCase       =  WavaVecaFeatureExtractor(
       feature_size=1,      sampling_rate=1_6000,      padding_value=0,      do_normalize=_UpperCAmelCase,      return_attention_mask=_UpperCAmelCase,      )

   if is_finetuned:
      if dict_path:
         UpperCamelCase       =  Dictionary.load(_UpperCAmelCase)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         UpperCamelCase       =  target_dict.pad_index
         UpperCamelCase       =  target_dict.bos_index
         UpperCamelCase       =  target_dict.pad_index
         UpperCamelCase       =  target_dict.bos_index
         UpperCamelCase       =  target_dict.eos_index
         UpperCamelCase       =  len(target_dict.symbols)
         UpperCamelCase       =  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)
         UpperCamelCase       =  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,      )
         UpperCamelCase       =  WavaVecaProcessor(feature_extractor=_UpperCAmelCase,      tokenizer=_UpperCAmelCase)
         processor.save_pretrained(_UpperCAmelCase)

      UpperCamelCase       =  SEWForCTC(_UpperCAmelCase)
   else:
      UpperCamelCase       =  SEWModel(_UpperCAmelCase)
      feature_extractor.save_pretrained(_UpperCAmelCase)

   recursively_load_weights(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)

   hf_model.save_pretrained(_UpperCAmelCase)


if __name__ == "__main__":
      snake_case_ :  List[Any]     =					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'
      )
      snake_case_ :  List[str]     =					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'''


def  __snake_case     (    _UpperCAmelCase					:       list[list[float]]):
   UpperCamelCase       =  []
   for data in source_data:
      for i, el in enumerate(_UpperCAmelCase):
         if len(_UpperCAmelCase) < i + 1:
            data_lists.append([])
         data_lists[i].append(float(_UpperCAmelCase))
   return data_lists



def  __snake_case     (    _UpperCAmelCase					:       list[list[float]],      _UpperCAmelCase					:       list[int]):
   UpperCamelCase       =  []
   for dlist, weight in zip(_UpperCAmelCase,      _UpperCAmelCase):
      UpperCamelCase       =  min(_UpperCAmelCase)
      UpperCamelCase       =  max(_UpperCAmelCase)

      UpperCamelCase       =  []
      # for weight 0 score is 1 - actual score
      if weight == 0:
         for item in dlist:
            try:
               score.append(1 - ((item - mind) / (maxd - mind)))
            except ZeroDivisionError:
               score.append(1)

      elif weight == 1:
         for item in dlist:
            try:
               score.append((item - mind) / (maxd - mind))
            except ZeroDivisionError:
               score.append(0)

        # weight not 0 or 1
      else:
         UpperCamelCase       =  f'Invalid weight of {weight:f} provided'
         raise ValueError(_UpperCAmelCase)

      score_lists.append(_UpperCAmelCase)

   return score_lists



def  __snake_case     (    _UpperCAmelCase					:       list[list[float]]):
   UpperCamelCase       =  [0 for i in range(len(score_lists[0]))]

   for slist in score_lists:
      for j, ele in enumerate(_UpperCAmelCase):
         UpperCamelCase       =  final_scores[j] + ele

   return final_scores



def  __snake_case     (    _UpperCAmelCase					:       list[list[float]],      _UpperCAmelCase					:       list[int]):
   UpperCamelCase       =  get_data(_UpperCAmelCase)
   UpperCamelCase       =  calculate_each_score(_UpperCAmelCase,      _UpperCAmelCase)
   UpperCamelCase       =  generate_final_scores(_UpperCAmelCase)

   # append scores to source data
   for i, ele in enumerate(_UpperCAmelCase):
      source_data[i].append(_UpperCAmelCase)

   return source_data





