


'''simple docstring'''





from math import factorial, radians


def        a__     (					UpperCamelCase_						:      float,   UpperCamelCase_						:      int = 18,   UpperCamelCase_						:      int = 10						):
   UpperCAmelCase__	:str      =      angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

   # Converting from degrees to radians
   UpperCAmelCase__	:List[str]      =      radians(_UpperCamelCase						)

   UpperCAmelCase__	:Optional[int]      =      angle_in_radians
   UpperCAmelCase__	:Optional[int]      =      3
   UpperCAmelCase__	:Tuple      =      -1

   for _ in range(_UpperCamelCase						):
      result += (b * (angle_in_radians**a)) / factorial(_UpperCamelCase						)

      UpperCAmelCase__	:str      =      -b  # One positive term and the next will be negative and so on...
      a += 2  # Increased by 2 for every term.

   return round(_UpperCamelCase,   _UpperCamelCase						)


if __name__ == "__main__":
    __import__('''doctest''').testmod()









'''simple docstring'''





import argparse

import torch

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


logging.set_verbosity_info()
UpperCamelCase_		:     int          =		logging.get_logger(__name__)

UpperCamelCase_		:     Tuple          =		[
    ['''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      (  _UpperCamelCase:       List[Any]       )		->					Union[str, Any]:

     """simple docstring"""




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

     for parlai_name, hf_name in PATTERNS:
          _snake_case				= k.replace(_UpperCamelCase   ,		_UpperCamelCase       )

     if k.startswith("encoder"       ):
          _snake_case				= k.replace(".attn"   ,		".self_attn"       )
          _snake_case				= k.replace("norm1"   ,		"self_attn_layer_norm"       )
          _snake_case				= k.replace("norm2"   ,		"final_layer_norm"       )
     elif k.startswith("decoder"       ):
          _snake_case				= k.replace("norm1"   ,		"self_attn_layer_norm"       )
          _snake_case				= k.replace("norm2"   ,		"encoder_attn_layer_norm"       )
          _snake_case				= k.replace("norm3"   ,		"final_layer_norm"       )
     return k





def       __a      (  _UpperCamelCase:       Dict       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= [
         "model.encoder.layernorm_embedding.weight",
         "model.encoder.layernorm_embedding.bias",
         "model.decoder.layernorm_embedding.weight",
         "model.decoder.layernorm_embedding.bias",
     ]
     for k in keys:
          _snake_case				= sd.pop(_UpperCamelCase       )
          _snake_case				= k.replace("layernorm_embedding"   ,		"layer_norm"       )
          assert new_k not in sd
          _snake_case				= v


UpperCamelCase_		:     Union[str, Any]          =		['''START''']





@torch.no_grad()
def       __a      (  _UpperCamelCase:       Any   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Union[str, Any]       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= torch.load(_UpperCamelCase   ,		map_location="cpu"       )
     _snake_case				= model["model"]
     _snake_case				= BlenderbotConfig.from_json_file(_UpperCamelCase       )
     _snake_case				= BlenderbotForConditionalGeneration(_UpperCamelCase       )
     _snake_case				= m.model.state_dict().keys()
     _snake_case				= []
     _snake_case				= {}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

          _snake_case				= rename_state_dict_key(_UpperCamelCase       )
          if new_k not in valid_keys:
               failures.append([k, new_k]       )
          else:
               _snake_case				= 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_		:     Any          =		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_		:     Optional[int]          =		parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


