
'''simple docstring'''




import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowercase			:				List[str]    		=			logging.get_logger(__name__)

lowercase			:				List[Any]    		=			[
    ['attention', 'attn'],
    ['encoder_attention', 'encoder_attn'],
    ['q_lin', 'q_proj'],
    ['k_lin', 'k_proj'],
    ['v_lin', 'v_proj'],
    ['out_lin', 'out_proj'],
    ['norm_embeddings', 'layernorm_embedding'],
    ['position_embeddings', 'embed_positions'],
    ['embeddings', 'embed_tokens'],
    ['ffn.lin', 'fc'],
]







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    if k == "embeddings.weight":
        return "shared.weight"

    for parlai_name, hf_name in PATTERNS:
        A       :   Union[str, Any]					=						k.replace(snake_case__		,				snake_case__						)

    if k.startswith('''encoder'''						):
        A       :   List[Any]					=						k.replace('''.attn'''		,				'''.self_attn'''						)
        A       :   Any					=						k.replace('''norm1'''		,				'''self_attn_layer_norm'''						)
        A       :   str					=						k.replace('''norm2'''		,				'''final_layer_norm'''						)
    elif k.startswith('''decoder'''						):
        A       :   str					=						k.replace('''norm1'''		,				'''self_attn_layer_norm'''						)
        A       :   Tuple					=						k.replace('''norm2'''		,				'''encoder_attn_layer_norm'''						)
        A       :   Any					=						k.replace('''norm3'''		,				'''final_layer_norm'''						)
    return k







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   Optional[int]					=						[
        '''model.encoder.layernorm_embedding.weight''',
        '''model.encoder.layernorm_embedding.bias''',
        '''model.decoder.layernorm_embedding.weight''',
        '''model.decoder.layernorm_embedding.bias''',
    ]
    for k in keys:
        A       :   Optional[Any]					=						sd.pop(snake_case__						)
        A       :   Tuple					=						k.replace('''layernorm_embedding'''		,				'''layer_norm'''						)
        assert new_k not in sd
        A       :   List[str]					=						v


lowercase			:				Any    		=			['START']







@torch.no_grad()
def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Union[str, Any]					=						torch.load(snake_case__		,				map_location='''cpu'''						)
    A       :   Any					=						model['''model''']
    A       :   Dict					=						BlenderbotConfig.from_json_file(snake_case__						)
    A       :   List[Any]					=						BlenderbotForConditionalGeneration(snake_case__						)
    A       :   Optional[int]					=						m.model.state_dict().keys()
    A       :   Optional[Any]					=						[]
    A       :   List[Any]					=						{}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

        A       :   List[Any]					=						rename_state_dict_key(snake_case__						)
        if new_k not in valid_keys:
            failures.append([k, new_k]						)
        else:
            A       :   List[str]					=						v
    if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
        rename_layernorm_keys(snake_case__						)
    m.model.load_state_dict(snake_case__		,				strict=snake_case__						)
    m.half()
    m.save_pretrained(snake_case__						)


if __name__ == "__main__":
      lowercase			:				Optional[int]    		=			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument('--src_path', type=str, help='like blenderbot-model.bin')
      parser.add_argument('--save_dir', default='hf_blenderbot', type=str, help='Where to save converted model.')
      parser.add_argument(
          '--hf_config_json', default='blenderbot-3b-config.json', type=str, help='Path to config to use'
      )
      lowercase			:				int    		=			parser.parse_args()
      convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






'''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))




