"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_a      :		str           =   logging.get_logger(__name__)

_a      :		Optional[Any]           =   {
    'post_extract_proj': 'feature_projection.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.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Any			,_lowerCamelCase						:       int	)       ->      List[Any]:



  for attribute in key.split("""."""	):
    _lowerCAmelCase						:       Dict  			=     getattr(snake_case__			,snake_case__	)

  if weight_type is not None:
    _lowerCAmelCase						:       Dict  			=     getattr(snake_case__			,snake_case__	).shape
  else:
    _lowerCAmelCase						:       str  			=     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						:       List[str]  			=     value
  elif weight_type == "weight_g":
    _lowerCAmelCase						:       str  			=     value
  elif weight_type == "weight_v":
    _lowerCAmelCase						:       Optional[Any]  			=     value
  elif weight_type == "bias":
    _lowerCAmelCase						:       List[str]  			=     value
  else:
    _lowerCAmelCase						:       Tuple  			=     value

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





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       int			,_lowerCamelCase						:       Dict	)       ->      Optional[int]:
  _lowerCAmelCase						:       Union[str, Any]  			=     []
  _lowerCAmelCase						:       Tuple  			=     fairseq_model.state_dict()

  _lowerCAmelCase						:       Union[str, Any]  			=     hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    _lowerCAmelCase						:       Any  			=     False
    if "conv_layers" in name:
      load_conv_layer(
          snake_case__			,snake_case__			,snake_case__			,snake_case__			,hf_model.config.feat_extract_norm == """group"""			,)
      _lowerCAmelCase						:       Optional[Any]  			=     True
    else:
      for key, mapped_key in MAPPING.items():
        _lowerCAmelCase						:       List[str]  			=     '''hubert.''' + 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] and not is_finetuned):
          _lowerCAmelCase						:       str  			=     True
          if "*" in mapped_key:
            _lowerCAmelCase						:       str  			=     name.split(snake_case__	)[0].split("""."""	)[-2]
            _lowerCAmelCase						:       int  			=     mapped_key.replace("""*"""			,snake_case__	)
          if "weight_g" in name:
            _lowerCAmelCase						:       List[str]  			=     '''weight_g'''
          elif "weight_v" in name:
            _lowerCAmelCase						:       str  			=     '''weight_v'''
          elif "weight" in name:
            _lowerCAmelCase						:       List[str]  			=     '''weight'''
          elif "bias" in name:
            _lowerCAmelCase						:       Optional[Any]  			=     '''bias'''
          else:
            _lowerCAmelCase						:       Optional[Any]  			=     None
          set_recursively(snake_case__			,snake_case__			,snake_case__			,snake_case__			,snake_case__	)
        continue
    if not is_used:
      unused_weights.append(snake_case__	)

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





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       int			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Optional[int]			,_lowerCamelCase						:       List[Any]	)       ->      Any:
  _lowerCAmelCase						:       Optional[int]  			=     full_name.split("""conv_layers."""	)[-1]
  _lowerCAmelCase						:       Dict  			=     name.split("""."""	)
  _lowerCAmelCase						:       Union[str, Any]  			=     int(items[0]	)
  _lowerCAmelCase						:       str  			=     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						:       int  			=     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						:       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."
      )
      _lowerCAmelCase						:       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."
      )
      _lowerCAmelCase						:       Tuple  			=     value
      logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."	)
  else:
    unused_weights.append(snake_case__	)





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



  if config_path is not None:
    _lowerCAmelCase						:       Tuple  			=     HubertConfig.from_pretrained(snake_case__	)
  else:
    _lowerCAmelCase						:       Dict  			=     HubertConfig()

  if is_finetuned:
    if dict_path:
      _lowerCAmelCase						:       Any  			=     Dictionary.load(snake_case__	)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _lowerCAmelCase						:       str  			=     target_dict.pad_index
      _lowerCAmelCase						:       Optional[int]  			=     target_dict.bos_index
      _lowerCAmelCase						:       Optional[int]  			=     target_dict.eos_index
      _lowerCAmelCase						:       Optional[int]  			=     len(target_dict.symbols	)
      _lowerCAmelCase						:       Union[str, Any]  			=     os.path.join(snake_case__			,"""vocab.json"""	)
      if not os.path.isdir(snake_case__	):
        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(snake_case__	)	)
        return
      os.makedirs(snake_case__			,exist_ok=snake_case__	)
      with open(snake_case__			,"""w"""			,encoding="""utf-8"""	) as vocab_handle:
        json.dump(target_dict.indices			,snake_case__	)
      _lowerCAmelCase						:       Optional[int]  			=     WavaVecaCTCTokenizer(
          snake_case__			,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=snake_case__			,)
      _lowerCAmelCase						:       Optional[Any]  			=     True if config.feat_extract_norm == '''layer''' else False
      _lowerCAmelCase						:       List[str]  			=     WavaVecaFeatureExtractor(
          feature_size=1			,sampling_rate=16000			,padding_value=0			,do_normalize=snake_case__			,return_attention_mask=snake_case__			,)
      _lowerCAmelCase						:       Dict  			=     WavaVecaProcessor(feature_extractor=snake_case__			,tokenizer=snake_case__	)
      processor.save_pretrained(snake_case__	)

    _lowerCAmelCase						:       Union[str, Any]  			=     HubertForCTC(snake_case__	)
  else:
    _lowerCAmelCase						:       List[str]  			=     HubertModel(snake_case__	)

  if is_finetuned:
    _lowerCAmelCase						:       int  			=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]			,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""	)[:-1]	)}	)
  else:
    _lowerCAmelCase						:       Optional[Any]  			=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

  _lowerCAmelCase						:       int  			=     model[0].eval()

  recursively_load_weights(snake_case__			,snake_case__			,snake_case__	)

  hf_wavavec.save_pretrained(snake_case__	)


if __name__ == "__main__":
     _a      :		Optional[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(
         '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
     )
     _a      :		Dict           =   parser.parse_args()
     convert_hubert_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )






'''simple docstring'''




import requests
from bsa import BeautifulSoup







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   str					=						BeautifulSoup(requests.get(snake_case__		,				params=snake_case__						).content		,				'''html.parser'''						)
    A       :   Dict					=						soup.find('''div'''		,				attrs={'''class''': '''gs_ri'''}						)
    A       :   Optional[int]					=						div.find('''div'''		,				attrs={'''class''': '''gs_fl'''}						).find_all('''a'''						)
    return anchors[2].get_text()


if __name__ == "__main__":
      lowercase			:				str    		=			{
          'title': (
              'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
              'capacitance, volumetric capacitance, and energy density'
          ),
          'journal': 'Chem. Mater.',
          'volume': 30,
          'pages': '3979-3990',
          'year': 20_18,
          'hl': 'en',
      }
      print(get_citation('https://scholar.google.com/scholar_lookup', params=params))




