






import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
a__       =     logging.get_logger(__name__)

a__       =     {
    '''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.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
    '''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
    '''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
    '''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''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
a__       =     [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def _UpperCAmelCase   (	a						:	Union[str, Any]  ,					a						:	List[Any]  ,					a						:	Optional[Any]  ,					a						:	str  ,					a						:	Union[str, Any] ):
       for attribute in key.split(""".""" ):
              snake_case__	  =       getattr(a  ,					a )

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

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

def _UpperCAmelCase   (	a						:	Tuple  ,					a						:	List[Any] ):
       snake_case__	  =       []
       snake_case__	  =       fairseq_model.state_dict()

       snake_case__	  =       hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              snake_case__	  =       False
              if "conv_layers" in name:
                     load_conv_layer(
                         a  ,					a  ,					a  ,					a  ,					hf_model.config.feat_extract_norm == """group"""  ,					)
                     snake_case__	  =       True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
                                   snake_case__	  =       True
                                   if "*" in mapped_key:
                                          snake_case__	  =       name.split(a )[0].split(""".""" )[-2]
                                          snake_case__	  =       mapped_key.replace("""*"""  ,					a )
                                   if "weight_g" in name:
                                          snake_case__	  =       'weight_g'
                                   elif "weight_v" in name:
                                          snake_case__	  =       'weight_v'
                                   elif "bias" in name and "relative_attention_bias" not in name:
                                          snake_case__	  =       'bias'
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          snake_case__	  =       'weight'
                                   else:
                                          snake_case__	  =       None

                                   set_recursively(a  ,					a  ,					a  ,					a  ,					a )
                            continue
              if not is_used:
                     unused_weights.append(a )

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

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

@torch.no_grad()
def _UpperCAmelCase   (	a						:	Optional[Any]  ,					a						:	Dict  ,					a						:	Union[str, Any]=None ):
       # load the pre-trained checkpoints
       snake_case__	  =       torch.load(a )
       snake_case__	  =       WavLMConfigOrig(checkpoint["""cfg"""] )
       snake_case__	  =       WavLMOrig(a )
       model.load_state_dict(checkpoint["""model"""] )
       model.eval()

       if config_path is not None:
              snake_case__	  =       WavLMConfig.from_pretrained(a )
       else:
              snake_case__	  =       WavLMConfig()

       snake_case__	  =       WavLMModel(a )

       recursively_load_weights(a  ,					a )

       hf_wavlm.save_pretrained(a )


if __name__ == "__main__":
 a__       =     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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
 a__       =     parser.parse_args()
 convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)








import requests
from bsa import BeautifulSoup

def _UpperCAmelCase   (	a						:	str  ,					a						:	dict ):
							snake_case__	  =       BeautifulSoup(requests.get(a  ,					params=a ).content  ,					"""html.parser""" )
							snake_case__	  =       soup.find("""div"""  ,					attrs={"""class""": """gs_ri"""} )
							snake_case__	  =       div.find("""div"""  ,					attrs={"""class""": """gs_fl"""} ).find_all("""a""" )
							return anchors[2].get_text()


if __name__ == "__main__":
	a__       =     {
	    """title""": (
	        """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
	        """capacitance, volumetric capacitance, and energy density"""
	    ),
	    """journal""": """Chem. Mater.""",
	    """volume""": 3_0,
	    """pages""": """3979-3990""",
	    """year""": 2_0_1_8,
	    """hl""": """en""",
	}
	print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))
