






"""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()
_SCREAMING_SNAKE_CASE					:				Optional[int]					    =						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:				Tuple					    =						{
    '''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      lowerCamelCase__       (							_lowerCamelCase       :		str		,				_lowerCamelCase       :		List[Any]		,				_lowerCamelCase       :		Dict		,				_lowerCamelCase       :		Optional[int]		,				_lowerCamelCase       :		Optional[Any] )      ->				List[Any]:
  for attribute in key.split('.' ):
    lowerCamelCase_         =						getattr(_lowerCamelCase		,				_lowerCamelCase )

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

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







def      lowerCamelCase__       (							_lowerCamelCase       :		Any		,				_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		List[Any] )      ->				Union[str, Any]:
  lowerCamelCase_         =						[]
  lowerCamelCase_         =						fairseq_model.state_dict()

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

  for name, value in fairseq_dict.items():
    lowerCamelCase_         =						False
    if "conv_layers" in name:
      load_conv_layer(
          _lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				hf_model.config.feat_extract_norm == 'group'		,				)
      lowerCamelCase_         =						True
    else:
      for key, mapped_key in MAPPING.items():
        lowerCamelCase_         =						'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]:
          lowerCamelCase_         =						True
          if "*" in mapped_key:
            lowerCamelCase_         =						name.split(_lowerCamelCase )[0].split('.' )[-2]
            lowerCamelCase_         =						mapped_key.replace('*'		,				_lowerCamelCase )
          if "weight_g" in name:
            lowerCamelCase_         =						'weight_g'
          elif "weight_v" in name:
            lowerCamelCase_         =						'weight_v'
          elif "weight" in name:
            lowerCamelCase_         =						'weight'
          elif "bias" in name:
            lowerCamelCase_         =						'bias'
          else:
            lowerCamelCase_         =						None
          set_recursively(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
        continue
    if not is_used:
      unused_weights.append(_lowerCamelCase )

  logger.warning(F'''Unused weights: {unused_weights}''' )







def      lowerCamelCase__       (							_lowerCamelCase       :		int		,				_lowerCamelCase       :		List[Any]		,				_lowerCamelCase       :		int		,				_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		Optional[int] )      ->				int:
  lowerCamelCase_         =						full_name.split('conv_layers.' )[-1]
  lowerCamelCase_         =						name.split('.' )
  lowerCamelCase_         =						int(items[0] )
  lowerCamelCase_         =						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.'''
      )
      lowerCamelCase_         =						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.'''
      )
      lowerCamelCase_         =						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."
      )
      lowerCamelCase_         =						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.'''
      )
      lowerCamelCase_         =						value
      logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
  else:
    unused_weights.append(_lowerCamelCase )







def      lowerCamelCase__       (							_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		List[str] )      ->				Optional[Any]:
  lowerCamelCase_         =						SEWConfig()
  if is_finetuned:
    lowerCamelCase_         =						model.wav_encoder.wav_model.cfg
  else:
    lowerCamelCase_         =						model.cfg

  lowerCamelCase_         =						fs_config.conv_bias
  lowerCamelCase_         =						eval(fs_config.conv_feature_layers )
  lowerCamelCase_         =						[x[0] for x in conv_layers]
  lowerCamelCase_         =						[x[1] for x in conv_layers]
  lowerCamelCase_         =						[x[2] for x in conv_layers]
  lowerCamelCase_         =						'gelu'
  lowerCamelCase_         =						'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
  lowerCamelCase_         =						0.0
  lowerCamelCase_         =						fs_config.activation_fn.name
  lowerCamelCase_         =						fs_config.encoder_embed_dim
  lowerCamelCase_         =						0.02
  lowerCamelCase_         =						fs_config.encoder_ffn_embed_dim
  lowerCamelCase_         =						1e-5
  lowerCamelCase_         =						fs_config.encoder_layerdrop
  lowerCamelCase_         =						fs_config.encoder_attention_heads
  lowerCamelCase_         =						fs_config.conv_pos_groups
  lowerCamelCase_         =						fs_config.conv_pos
  lowerCamelCase_         =						len(_lowerCamelCase )
  lowerCamelCase_         =						fs_config.encoder_layers
  lowerCamelCase_         =						fs_config.squeeze_factor

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

  lowerCamelCase_         =						'Wav2Vec2FeatureExtractor'
  lowerCamelCase_         =						'Wav2Vec2CTCTokenizer'

  return config







@torch.no_grad()
def      lowerCamelCase__       (							_lowerCamelCase       :		Any		,				_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		Any=None		,				_lowerCamelCase       :		Union[str, Any]=None		,				_lowerCamelCase       :		List[str]=True )      ->				Optional[Any]:

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

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

  lowerCamelCase_         =						True if config.feat_extract_norm == 'layer' else False
  lowerCamelCase_         =						WavaVecaFeatureExtractor(
      feature_size=1		,				sampling_rate=16000		,				padding_value=0		,				do_normalize=_lowerCamelCase		,				return_attention_mask=_lowerCamelCase		,				)

  if is_finetuned:
    if dict_path:
      lowerCamelCase_         =						Dictionary.load(_lowerCamelCase )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      lowerCamelCase_         =						target_dict.pad_index
      lowerCamelCase_         =						target_dict.bos_index
      lowerCamelCase_         =						target_dict.pad_index
      lowerCamelCase_         =						target_dict.bos_index
      lowerCamelCase_         =						target_dict.eos_index
      lowerCamelCase_         =						len(target_dict.symbols )
      lowerCamelCase_         =						os.path.join(_lowerCamelCase		,				'vocab.json' )
      if not os.path.isdir(_lowerCamelCase ):
        logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_lowerCamelCase ) )
        return
      os.makedirs(_lowerCamelCase		,				exist_ok=_lowerCamelCase )
      with open(_lowerCamelCase		,				'w'		,				encoding='utf-8' ) as vocab_handle:
        json.dump(target_dict.indices		,				_lowerCamelCase )
      lowerCamelCase_         =						WavaVecaCTCTokenizer(
          _lowerCamelCase		,				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=_lowerCamelCase		,				)
      lowerCamelCase_         =						WavaVecaProcessor(feature_extractor=_lowerCamelCase		,				tokenizer=_lowerCamelCase )
      processor.save_pretrained(_lowerCamelCase )

    lowerCamelCase_         =						SEWForCTC(_lowerCamelCase )
  else:
    lowerCamelCase_         =						SEWModel(_lowerCamelCase )
    feature_extractor.save_pretrained(_lowerCamelCase )

  recursively_load_weights(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )

  hf_model.save_pretrained(_lowerCamelCase )


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





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


_SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						{
    '''vocab_file''': {
        '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''unc-nlp/lxmert-base-uncased''': (
            '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

_SCREAMING_SNAKE_CASE					:				Any					    =						{
    '''unc-nlp/lxmert-base-uncased''': 512,
}

_SCREAMING_SNAKE_CASE					:				int					    =						{
    '''unc-nlp/lxmert-base-uncased''': {'''do_lower_case''': True},
}






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     int						= VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE						:     Any						= PRETRAINED_INIT_CONFIGURATION
    SCREAMING_SNAKE_CASE						:     Any						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= LxmertTokenizer






    def __init__(       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=None       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=None       ,						__SCREAMING_SNAKE_CASE     :							str=True       ,						__SCREAMING_SNAKE_CASE     :							List[Any]="[UNK]"       ,						__SCREAMING_SNAKE_CASE     :							int="[SEP]"       ,						__SCREAMING_SNAKE_CASE     :							int="[PAD]"       ,						__SCREAMING_SNAKE_CASE     :							List[Any]="[CLS]"       ,						__SCREAMING_SNAKE_CASE     :							Any="[MASK]"       ,						__SCREAMING_SNAKE_CASE     :							List[str]=True       ,						__SCREAMING_SNAKE_CASE     :							List[str]=None       ,						**__SCREAMING_SNAKE_CASE     :							Any       ,						)     ->		Any:
      super().__init__(
          __SCREAMING_SNAKE_CASE       ,						tokenizer_file=__SCREAMING_SNAKE_CASE       ,						do_lower_case=__SCREAMING_SNAKE_CASE       ,						unk_token=__SCREAMING_SNAKE_CASE       ,						sep_token=__SCREAMING_SNAKE_CASE       ,						pad_token=__SCREAMING_SNAKE_CASE       ,						cls_token=__SCREAMING_SNAKE_CASE       ,						mask_token=__SCREAMING_SNAKE_CASE       ,						tokenize_chinese_chars=__SCREAMING_SNAKE_CASE       ,						strip_accents=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE       ,						)

      lowerCamelCase_         =						json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
      if (
          normalizer_state.get('lowercase'       ,						__SCREAMING_SNAKE_CASE					) != do_lower_case
          or normalizer_state.get('strip_accents'       ,						__SCREAMING_SNAKE_CASE					) != strip_accents
          or normalizer_state.get('handle_chinese_chars'       ,						__SCREAMING_SNAKE_CASE					) != tokenize_chinese_chars
      ):
        lowerCamelCase_         =						getattr(__SCREAMING_SNAKE_CASE       ,						normalizer_state.pop('type'					)					)
        lowerCamelCase_         =						do_lower_case
        lowerCamelCase_         =						strip_accents
        lowerCamelCase_         =						tokenize_chinese_chars
        lowerCamelCase_         =						normalizer_class(**__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						do_lower_case






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							Tuple=None					)     ->		Dict:
      lowerCamelCase_         =						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

      return output






    def    UpperCamelCase       (       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							List[int]       ,						__SCREAMING_SNAKE_CASE     :							Optional[List[int]] = None					)     ->		List[int]:
      lowerCamelCase_         =						[self.sep_token_id]
      lowerCamelCase_         =						[self.cls_token_id]
      if token_ids_a is None:
        return len(cls + token_ids_a + sep					) * [0]
      return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = None					)     ->		Tuple[str]:
      lowerCamelCase_         =						self._tokenizer.model.save(__SCREAMING_SNAKE_CASE       ,						name=__SCREAMING_SNAKE_CASE					)
      return tuple(__SCREAMING_SNAKE_CASE					)




