code: 
  
  
  
  """simple docstring"""
  import json
  import os
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
  
  
  _lowerCAmelCase					:List[Any]  =      logging.get_logger(__name__)
  
  _lowerCAmelCase					:int  =      {
      'vocab_file': 'vocab.json',
      'merges_file': 'merges.txt',
  }
  
  _lowerCAmelCase					:Dict  =      {
      'vocab_file': {
          'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
      },
      'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
  }
  
  _lowerCAmelCase					:List[Any]  =      {
      'microsoft/biogpt': 1_024,
  }
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Any							):
      _UpperCAmelCase							:						str       							=					set()
      _UpperCAmelCase							:						List[Any]       							=					word[0]
      for char in word[1:]:
          pairs.add((prev_char, char)							)
          _UpperCAmelCase							:						Dict       							=					char
      return pairs
  
  
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =VOCAB_FILES_NAMES
       a__             =PRETRAINED_VOCAB_FILES_MAP
       a__             =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a__             =['''input_ids''', '''attention_mask''']
  
  
  
  
  
       def __init__(      self ,			A ,			A ,			A="<unk>" ,			A="<s>" ,			A="</s>" ,			A="</s>" ,			A="<pad>" ,			**A ,			)							->      Optional[int]:
           super().__init__(
               bos_token=A ,			eos_token=A ,			sep_token=A ,			unk_token=A ,			pad_token=A ,			**A ,			)
  
           try:
               import sacremoses
           except ImportError:
               raise ImportError(
                   '''You need to install sacremoses to use BioGptTokenizer. '''
                   '''See https://pypi.org/project/sacremoses/ for installation.'''	)
  
           _UpperCAmelCase							:						str       							=					'''en'''
           _UpperCAmelCase							:						str       							=					sacremoses
           # cache of sm.MosesTokenizer instance
           _UpperCAmelCase							:						Optional[int]       							=					{}
           _UpperCAmelCase							:						Tuple       							=					{}
           with open(A ,			encoding='''utf-8'''	) as vocab_handle:
               _UpperCAmelCase							:						Union[str, Any]       							=					json.load(A	)
           _UpperCAmelCase							:						Optional[int]       							=					{v: k for k, v in self.encoder.items()}
           with open(A ,			encoding='''utf-8'''	) as merges_handle:
               _UpperCAmelCase							:						Any       							=					merges_handle.read().split('''\n'''	)[:-1]
           _UpperCAmelCase							:						Union[str, Any]       							=					[tuple(merge.split()[:2]	) for merge in merges]
           _UpperCAmelCase							:						Dict       							=					dict(zip(A ,			range(len(A	)	)	)	)
           _UpperCAmelCase							:						Optional[Any]       							=					{}
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      List[str]:
           return len(self.encoder	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Dict:
           return dict(self.encoder ,			**self.added_tokens_encoder	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A	)							->      Any:
           if lang not in self.cache_moses_tokenizer:
               _UpperCAmelCase							:						Dict       							=					self.sm.MosesTokenizer(lang=A	)
               _UpperCAmelCase							:						Dict       							=					moses_tokenizer
           return self.cache_moses_tokenizer[lang].tokenize(
               A ,			aggressive_dash_splits=A ,			return_str=A ,			escape=A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A	)							->      Any:
           if lang not in self.cache_moses_detokenizer:
               _UpperCAmelCase							:						List[Any]       							=					self.sm.MosesDetokenizer(lang=A	)
               _UpperCAmelCase							:						str       							=					moses_detokenizer
           return self.cache_moses_detokenizer[lang].detokenize(A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Any:
           _UpperCAmelCase							:						int       							=					tuple(token[:-1]	) + (token[-1] + '''</w>''',)
           if token in self.cache:
               return self.cache[token]
           _UpperCAmelCase							:						Dict       							=					get_pairs(A	)
  
           if not pairs:
               return token + "</w>"
  
           while True:
               _UpperCAmelCase							:						Dict       							=					min(A ,			key=lambda A	: self.bpe_ranks.get(A ,			float('''inf'''	)	)	)
               if bigram not in self.bpe_ranks:
                   break
               _UpperCAmelCase							,       _UpperCAmelCase							:						str       							=					bigram
               _UpperCAmelCase							:						str       							=					[]
               _UpperCAmelCase							:						int       							=					0
               while i < len(A	):
                   try:
                       _UpperCAmelCase							:						str       							=					word.index(A ,			A	)
                   except ValueError:
                       new_word.extend(word[i:]	)
                       break
                   else:
                       new_word.extend(word[i:j]	)
                       _UpperCAmelCase							:						Any       							=					j
  
                   if word[i] == first and i < len(A	) - 1 and word[i + 1] == second:
                       new_word.append(first + second	)
                       i += 2
                   else:
                       new_word.append(word[i]	)
                       i += 1
               _UpperCAmelCase							:						Tuple       							=					tuple(A	)
               _UpperCAmelCase							:						Any       							=					new_word
               if len(A	) == 1:
                   break
               else:
                   _UpperCAmelCase							:						Union[str, Any]       							=					get_pairs(A	)
           _UpperCAmelCase							:						Optional[int]       							=					''' '''.join(A	)
           if word == "\n  </w>":
               _UpperCAmelCase							:						Tuple       							=					'''\n</w>'''
           _UpperCAmelCase							:						Dict       							=					word
           return word
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A=False	)							->      Any:
           if bypass_tokenizer:
               _UpperCAmelCase							:						str       							=					text.split()
           else:
               _UpperCAmelCase							:						Union[str, Any]       							=					self.moses_tokenize(A ,			self.lang	)
  
           _UpperCAmelCase							:						str       							=					[]
           for token in text:
               if token:
                   split_tokens.extend(list(self.bpe(A	).split(''' '''	)	)	)
  
           return split_tokens
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      List[Any]:
           return self.encoder.get(A ,			self.encoder.get(self.unk_token	)	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      List[Any]:
           return self.decoder.get(A ,			self.unk_token	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Dict:
           _UpperCAmelCase							:						Any       							=					[t.replace(''' ''' ,			''''''	).replace('''</w>''' ,			''' '''	) for t in tokens]
           _UpperCAmelCase							:						Any       							=					''''''.join(A	).split()
           # detokenize
           _UpperCAmelCase							:						Optional[Any]       							=					self.moses_detokenize(A ,			self.lang	)
           return text
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      List[int]:
           if token_ids_a is None:
               return [self.sep_token_id] + token_ids_a
           _UpperCAmelCase							:						Union[str, Any]       							=					[self.sep_token_id]
           return sep + token_ids_a + sep + token_ids_a
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = None ,			A = False	)							->      List[int]:
           if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=A ,			token_ids_a=A ,			already_has_special_tokens=A	)
           # no bos used in fairseq
           if token_ids_a is not None:
               return [1] + ([0] * len(A	)) + [1] + ([0] * len(A	))
           return [1] + ([0] * len(A	))
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      List[int]:
           _UpperCAmelCase							:						int       							=					[self.sep_token_id]
  
           # no bos used in fairseq
           if token_ids_a is None:
               return len(token_ids_a + sep	) * [0]
           return len(token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      Tuple[str]:
           if not os.path.isdir(A	):
               logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
               return
           _UpperCAmelCase							:						Optional[Any]       							=					os.path.join(
               A ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)
           _UpperCAmelCase							:						List[str]       							=					os.path.join(
               A ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']	)
  
           with open(A ,			'''w''' ,			encoding='''utf-8'''	) as f:
               f.write(json.dumps(self.encoder ,			indent=2 ,			sort_keys=A ,			ensure_ascii=A	) + '''\n'''	)
  
           _UpperCAmelCase							:						Tuple       							=					0
           with open(A ,			'''w''' ,			encoding='''utf-8'''	) as writer:
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,			key=lambda A	: kv[1]	):
                   if index != token_index:
                       logger.warning(
                           f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                           ''' Please check that the tokenizer is not corrupted!'''	)
                       _UpperCAmelCase							:						Any       							=					token_index
                   writer.write(''' '''.join(A	) + '''\n'''	)
                   index += 1
  
           return vocab_file, merge_file
  
  
  
  
  
       def __getstate__(      self	)							->      Tuple:
           _UpperCAmelCase							:						Optional[Any]       							=					self.__dict__.copy()
           _UpperCAmelCase							:						List[str]       							=					None
           return state
  
  
  
  
  
       def __setstate__(      self ,			A	)							->      List[Any]:
           _UpperCAmelCase							:						Union[str, Any]       							=					d
  
           try:
               import sacremoses
           except ImportError:
               raise ImportError(
                   '''You need to install sacremoses to use XLMTokenizer. '''
                   '''See https://pypi.org/project/sacremoses/ for installation.'''	)
  
           _UpperCAmelCase							:						Optional[int]       							=					sacremoses
  
  
  
style_context: 
  
  
  
  """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 (
      SEWDConfig,
      SEWDForCTC,
      SEWDModel,
      WavaVecaCTCTokenizer,
      WavaVecaFeatureExtractor,
      WavaVecaProcessor,
      logging,
  )
  
  
  logging.set_verbosity_info()
  _lowerCAmelCase					:int  =      logging.get_logger(__name__)
  
  _lowerCAmelCase					:Dict  =      {
      'post_extract_proj': 'feature_projection',
      'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
      'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
      'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
      'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
      'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
      'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
      'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
      'output.dense': 'encoder.encoder.layer.*.output.dense',
      'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
      'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
      'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
      '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_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	Union[str, Any]  , UpperCamelCase__	:	Optional[int]  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	int							):
      for attribute in key.split('''.'''							):
          _UpperCAmelCase							:						Optional[Any]       							=					getattr(UpperCamelCase__  , UpperCamelCase__							)
  
      if weight_type is not None:
          _UpperCAmelCase							:						Union[str, Any]       							=					getattr(UpperCamelCase__  , UpperCamelCase__							).shape
      else:
          _UpperCAmelCase							:						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":
          _UpperCAmelCase							:						Dict       							=					value
      elif weight_type == "weight_g":
          _UpperCAmelCase							:						Optional[int]       							=					value
      elif weight_type == "weight_v":
          _UpperCAmelCase							:						List[Any]       							=					value
      elif weight_type == "bias":
          _UpperCAmelCase							:						Union[str, Any]       							=					value
      else:
          _UpperCAmelCase							:						Optional[int]       							=					value
  
      logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	int							):
      _UpperCAmelCase							:						Dict       							=					[]
      _UpperCAmelCase							:						Optional[int]       							=					fairseq_model.state_dict()
  
      _UpperCAmelCase							:						Any       							=					hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor
  
      for name, value in fairseq_dict.items():
          _UpperCAmelCase							:						str       							=					False
          if "conv_layers" in name:
              load_conv_layer(
                  UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , hf_model.config.feat_extract_norm == '''group'''  , )
              _UpperCAmelCase							:						Any       							=					True
          else:
              for key, mapped_key in MAPPING.items():
                  _UpperCAmelCase							:						str       							=					'''sew_d.''' + 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							:						Dict       							=					True
                      if "*" in mapped_key:
                          _UpperCAmelCase							:						List[str]       							=					name.split(UpperCamelCase__							)[0].split('''.'''							)[-2]
                          if not layer_index.isnumeric():
                              continue
                          _UpperCAmelCase							:						str       							=					mapped_key.replace('''*'''  , UpperCamelCase__							)
                      if "weight_g" in name:
                          _UpperCAmelCase							:						int       							=					'''weight_g'''
                      elif "weight_v" in name:
                          _UpperCAmelCase							:						Optional[int]       							=					'''weight_v'''
                      elif "weight" in name:
                          _UpperCAmelCase							:						int       							=					'''weight'''
                      elif "bias" in name:
                          _UpperCAmelCase							:						int       							=					'''bias'''
                      else:
                          _UpperCAmelCase							:						Dict       							=					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 				lowerCamelCase_		(UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	str							):
      _UpperCAmelCase							:						List[Any]       							=					full_name.split('''conv_layers.'''							)[-1]
      _UpperCAmelCase							:						Any       							=					name.split('''.'''							)
      _UpperCAmelCase							:						Union[str, Any]       							=					int(items[0]							)
      _UpperCAmelCase							:						int       							=					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							:						Tuple       							=					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							:						Union[str, Any]       							=					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							:						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.'
              )
              _UpperCAmelCase							:						int       							=					value
              logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
      else:
          unused_weights.append(UpperCamelCase__							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	Any							):
      _UpperCAmelCase							:						Any       							=					SEWDConfig()
      if is_finetuned:
          _UpperCAmelCase							:						str       							=					model.wav_encoder.wav_model.cfg
      else:
          _UpperCAmelCase							:						Optional[Any]       							=					model.cfg
  
      _UpperCAmelCase							:						Optional[Any]       							=					fs_config.conv_bias
      _UpperCAmelCase							:						Optional[int]       							=					eval(fs_config.conv_feature_layers							)
      _UpperCAmelCase							:						Dict       							=					[x[0] for x in conv_layers]
      _UpperCAmelCase							:						int       							=					[x[1] for x in conv_layers]
      _UpperCAmelCase							:						Optional[int]       							=					[x[2] for x in conv_layers]
      _UpperCAmelCase							:						int       							=					'''gelu'''
      _UpperCAmelCase							:						int       							=					'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
      _UpperCAmelCase							:						Tuple       							=					0.0
      _UpperCAmelCase							:						List[Any]       							=					fs_config.activation_fn.name
      _UpperCAmelCase							:						Optional[Any]       							=					fs_config.encoder_embed_dim
      _UpperCAmelCase							:						Optional[int]       							=					0.02
      _UpperCAmelCase							:						List[Any]       							=					fs_config.encoder_ffn_embed_dim
      _UpperCAmelCase							:						Dict       							=					1E-5
      _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.encoder_layerdrop
      _UpperCAmelCase							:						Tuple       							=					fs_config.encoder_attention_heads
      _UpperCAmelCase							:						Optional[Any]       							=					fs_config.conv_pos_groups
      _UpperCAmelCase							:						Any       							=					fs_config.conv_pos
      _UpperCAmelCase							:						Optional[Any]       							=					len(UpperCamelCase__							)
      _UpperCAmelCase							:						Any       							=					fs_config.encoder_layers
      _UpperCAmelCase							:						Dict       							=					fs_config.squeeze_factor
      # DeBERTa-specific parameters:
      _UpperCAmelCase							:						int       							=					fs_config.max_position_embeddings
      _UpperCAmelCase							:						Any       							=					fs_config.position_buckets
      _UpperCAmelCase							:						Optional[int]       							=					fs_config.share_att_key
      _UpperCAmelCase							:						Dict       							=					fs_config.relative_attention
      _UpperCAmelCase							:						List[Any]       							=					fs_config.position_biased_input
      _UpperCAmelCase							:						str       							=					tuple(fs_config.pos_att_type.split('''|'''							)							)
      _UpperCAmelCase							:						List[Any]       							=					fs_config.norm_rel_ebd
  
      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
          _UpperCAmelCase							:						Any       							=					model.cfg
          _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.final_dropout
          _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.layerdrop
      _UpperCAmelCase							:						str       							=					fs_config.activation_dropout
      _UpperCAmelCase							:						List[Any]       							=					fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      _UpperCAmelCase							:						List[Any]       							=					fs_config.attention_dropout
      _UpperCAmelCase							:						List[Any]       							=					fs_config.dropout_input
      _UpperCAmelCase							:						str       							=					fs_config.dropout
      _UpperCAmelCase							:						List[Any]       							=					fs_config.mask_channel_length
      _UpperCAmelCase							:						Tuple       							=					fs_config.mask_channel_prob
      _UpperCAmelCase							:						List[str]       							=					fs_config.mask_length
      _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.mask_prob
  
      _UpperCAmelCase							:						int       							=					'''Wav2Vec2FeatureExtractor'''
      _UpperCAmelCase							:						str       							=					'''Wav2Vec2CTCTokenizer'''
  
      return config
  
  
  
  
  
  @torch.no_grad()
  def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	int  , UpperCamelCase__	:	Tuple=None  , UpperCamelCase__	:	Any=None  , UpperCamelCase__	:	Union[str, Any]=True							):
  
      if is_finetuned:
          _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						int       							=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]  , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
      else:
          _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						Tuple       							=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)
  
      if config_path is not None:
          _UpperCAmelCase							:						List[str]       							=					SEWDConfig.from_pretrained(UpperCamelCase__							)
      else:
          _UpperCAmelCase							:						Optional[Any]       							=					convert_config(model[0]  , UpperCamelCase__							)
      _UpperCAmelCase							:						Dict       							=					model[0].eval()
  
      _UpperCAmelCase							:						List[str]       							=					True if config.feat_extract_norm == '''layer''' else False
      _UpperCAmelCase							:						Optional[int]       							=					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							:						List[str]       							=					Dictionary.load(UpperCamelCase__							)
  
              # important change bos & pad token id since CTC symbol is <pad> and
              # not <s> as in fairseq
              _UpperCAmelCase							:						Union[str, Any]       							=					target_dict.pad_index
              _UpperCAmelCase							:						Union[str, Any]       							=					target_dict.bos_index
              _UpperCAmelCase							:						int       							=					target_dict.pad_index
              _UpperCAmelCase							:						str       							=					target_dict.bos_index
              _UpperCAmelCase							:						Tuple       							=					target_dict.eos_index
              _UpperCAmelCase							:						List[str]       							=					len(target_dict.symbols							)
              _UpperCAmelCase							:						Optional[Any]       							=					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							:						Any       							=					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							:						Dict       							=					WavaVecaProcessor(feature_extractor=UpperCamelCase__  , tokenizer=UpperCamelCase__							)
              processor.save_pretrained(UpperCamelCase__							)
  
          _UpperCAmelCase							:						Tuple       							=					SEWDForCTC(UpperCamelCase__							)
      else:
          _UpperCAmelCase							:						Any       							=					SEWDModel(UpperCamelCase__							)
          feature_extractor.save_pretrained(UpperCamelCase__							)
  
      recursively_load_weights(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
  
      hf_model.save_pretrained(UpperCamelCase__							)
  
  
  if __name__ == "__main__":
    _lowerCAmelCase					:Union[str, 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'
    )
    _lowerCAmelCase					:int  =      parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )
  
  
  