





import argparse

import torch

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


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

UpperCAmelCase_     							= [
    ['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_    (  __UpperCAmelCase:			Any      )      ->	Optional[Any]:
    if k == "embeddings.weight":
        return "shared.weight"

    for parlai_name, hf_name in PATTERNS:
        UpperCamelCase__							:				str          =      k.replace(__UpperCAmelCase		,   __UpperCAmelCase      )

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int      )      ->	List[Any]:
    UpperCamelCase__							:				Union[str, Any]          =      [
        '''model.encoder.layernorm_embedding.weight''',
        '''model.encoder.layernorm_embedding.bias''',
        '''model.decoder.layernorm_embedding.weight''',
        '''model.decoder.layernorm_embedding.bias''',
    ]
    for k in keys:
        UpperCamelCase__							:				Any          =      sd.pop(__UpperCAmelCase      )
        UpperCamelCase__							:				Dict          =      k.replace('''layernorm_embedding'''		,   '''layer_norm'''      )
        assert new_k not in sd
        UpperCamelCase__							:				Optional[int]          =      v


UpperCAmelCase_     							= ['START']




@torch.no_grad()
def 		lowerCAmelCase_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			List[str]		,   __UpperCAmelCase:			Any      )      ->	Optional[int]:
    UpperCamelCase__							:				Tuple          =      torch.load(__UpperCAmelCase		,   map_location='''cpu'''      )
    UpperCamelCase__							:				Optional[int]          =      model['''model''']
    UpperCamelCase__							:				Any          =      BlenderbotConfig.from_json_file(__UpperCAmelCase      )
    UpperCamelCase__							:				int          =      BlenderbotForConditionalGeneration(__UpperCAmelCase      )
    UpperCamelCase__							:				Optional[int]          =      m.model.state_dict().keys()
    UpperCamelCase__							:				Optional[int]          =      []
    UpperCamelCase__							:				Tuple          =      {}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

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


if __name__ == "__main__":
  UpperCAmelCase_     							= 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'
  )
  UpperCAmelCase_     							= parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)








def 		lowerCAmelCase_    (  __UpperCAmelCase:			float		,   __UpperCAmelCase:			int      )      ->	float:
    if digit_amount > 0:
        return round(number - int(__UpperCAmelCase      )		,   __UpperCAmelCase      )
    return number - int(__UpperCAmelCase      )


if __name__ == "__main__":
  print(decimal_isolate(1.53, 0))
  print(decimal_isolate(35.345, 1))
  print(decimal_isolate(35.345, 2))
  print(decimal_isolate(35.345, 3))
  print(decimal_isolate(-14.789, 3))
  print(decimal_isolate(0, 2))
  print(decimal_isolate(-14.123, 1))
  print(decimal_isolate(-14.123, 2))
  print(decimal_isolate(-14.123, 3))

