



'''simple docstring'''





import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase      						=	logging.get_logger(__name__)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  str							      =					torch.load(UpperCamelCase							,    map_location="""cpu"""			)
    if "model" in sd.keys():
        lowerCAmelCase__ :  str							      =					torch.load(UpperCamelCase							,    map_location="""cpu"""			)["""model"""]

    # pop unnecessary weights
    lowerCAmelCase__ :  Dict							      =					[
        """decoder.version""",
        """decoder.output_projection.weight""",
    ]
    for key in keys_to_delete:
        if key in sd:
            sd.pop(UpperCamelCase			)

    lowerCAmelCase__ :  List[str]							      =					{
        """decoder.project_in_dim.weight""": """decoder.project_in.weight""",
        """decoder.project_out_dim.weight""": """decoder.project_out.weight""",
        """decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
        """decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
    }
    for old_key, new_key in keys_to_rename.items():
        if old_key in sd:
            lowerCAmelCase__ :  Any							      =					sd.pop(UpperCamelCase			)

    lowerCAmelCase__ :  Optional[int]							      =					list(sd.keys()			)
    for key in keys:
        if ".qkv_proj." in key:
            lowerCAmelCase__ :  Optional[int]							      =					sd[key]
            # We split QKV in separate Q,K,V

            lowerCAmelCase__ :  Optional[int]							      =					key.replace(""".qkv_proj."""							,    """.q_proj."""			)
            lowerCAmelCase__ :  List[str]							      =					key.replace(""".qkv_proj."""							,    """.k_proj."""			)
            lowerCAmelCase__ :  Optional[Any]							      =					key.replace(""".qkv_proj."""							,    """.v_proj."""			)

            lowerCAmelCase__ :  Union[str, Any]							      =					value.shape[0]
            assert depth % 3 == 0
            # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
            # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
            lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  int							      =					torch.split(UpperCamelCase							,    depth // 3							,    dim=0			)

            lowerCAmelCase__ :  Dict							      =					q
            lowerCAmelCase__ :  Dict							      =					k
            lowerCAmelCase__ :  Any							      =					v
            del sd[key]

    return sd
@torch.no_grad()
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=None			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[Any]							      =					load_checkpoint(UpperCamelCase			)

    if config is not None:
        lowerCAmelCase__ :  Tuple							      =					OPTConfig.from_pretrained(UpperCamelCase			)
    else:
        lowerCAmelCase__ :  List[str]							      =					OPTConfig()

    lowerCAmelCase__ :  Optional[int]							      =					OPTModel(UpperCamelCase			).half().eval()
    model.load_state_dict(UpperCamelCase			)

    # Check results
    Path(UpperCamelCase			).mkdir(exist_ok=UpperCamelCase			)
    model.save_pretrained(UpperCamelCase			)


if __name__ == "__main__":
 _lowerCAmelCase      						=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--fairseq_path''',
     type=str,
     help=(
         '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
         ''' https://huggingface.co/models?other=opt_metasq'''
     ),
 )
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
 _lowerCAmelCase      						=	parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



"""simple docstring"""






import requests
from bsa import BeautifulSoup



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							BeautifulSoup(requests.get(lowerCamelCase					,			params=lowerCamelCase       ).content					,			'html.parser'       )
				UpperCAmelCase__             =							soup.find('div'					,			attrs={'class': 'gs_ri'}       )
				UpperCAmelCase__             =							div.find('div'					,			attrs={'class': 'gs_fl'}       ).find_all('a'       )
				return anchors[2].get_text()


if __name__ == "__main__":
						lowerCAmelCase__  :  Optional[int]						     =	{
						    'title': (
						        'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
						        'capacitance, volumetric capacitance, and energy density'
						    ),
						    'journal': 'Chem. Mater.',
						    'volume': 30,
						    'pages': '3979-3990',
						    'year': 2_018,
						    'hl': 'en',
						}
						print(get_citation('https://scholar.google.com/scholar_lookup', params=params))



