




import argparse

import torch

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


logging.set_verbosity_info()
lowerCAmelCase     :   Any						     =  logging.get_logger(__name__)

lowerCAmelCase     :   List[str]						     =  [
    ['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    A_  (							a  ):

      """simple docstring"""



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

      for parlai_name, hf_name in PATTERNS:
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     k.replace(lowerCAmelCase__					,   lowerCAmelCase__  )

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

def    A_  (							a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  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:
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     sd.pop(lowerCAmelCase__  )
            SCREAMING_SNAKE_CASE_				:  Tuple           =     k.replace('layernorm_embedding'					,   'layer_norm'  )
            assert new_k not in sd
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     v


lowerCAmelCase     :   Any						     =  ['START']

@torch.no_grad()
def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



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

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


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









from math import pi

def    A_  (							a					,   a  ):

      """simple docstring"""



      return 2 * pi * radius * (angle / 3_6_0)


if __name__ == "__main__":
    print(arc_length(90, 10))






