



'''simple docstring'''


import argparse

import torch

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


logging.set_verbosity_info()
_lowerCamelCase	:       Tuple      	=	logging.get_logger(__name__)

_lowerCamelCase	:       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				(   A__    )       ->	List[str]:






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

   for parlai_name, hf_name in PATTERNS:
      UpperCamelCase					=  k.replace(A__    ,						A__    )

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







def 				__lowerCamelCase				(   A__    )       ->	Any:






   """simple docstring"""
   UpperCamelCase					=  [
       '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					=  sd.pop(A__    )
      UpperCamelCase					=  k.replace('layernorm_embedding'    ,						'layer_norm'    )
      assert new_k not in sd
      UpperCamelCase					=  v


_lowerCamelCase	:       Any      	=	["START"]







@torch.no_grad()
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	int:






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

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


if __name__ == "__main__":
   _lowerCamelCase	:       List[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	:       str      	=	parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


'''simple docstring'''




import math




def 							lowerCamelCase	(      UpperCAmelCase__       :					int )			->      int:

							if not isinstance(UpperCAmelCase__      ,      UpperCAmelCase__ ):
														lowercase_					:			List[Any]										=       F'''Input value of [number={number}] must be an integer'''
														raise TypeError(UpperCAmelCase__ )

							if number < 1:
														lowercase_					:			Tuple										=       F'''Input value of [number={number}] must be > 0'''
														raise ValueError(UpperCAmelCase__ )
							elif number == 1:
														return 3
							elif number == 2:
														return 5
							else:
														lowercase_					:			Union[str, Any]										=       int(math.log(number // 3      ,      2 ) ) + 2

														lowercase_					:			Any										=       [3, 5]
														lowercase_					:			List[Any]										=       2
														lowercase_					:			Optional[Any]										=       3
														for block in range(1      ,      UpperCAmelCase__ ):
																					for _ in range(UpperCAmelCase__ ):
																												proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
																												proth_index += 1
																					increment *= 2

							return proth_list[number - 1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						for number in range(11):
												_lowercase						: Optional[Any]						=						0
												try:
																		_lowercase						: str						=						proth(number)
												except ValueError:
																		print(f"""ValueError: there is no {number}th Proth number""")
																		continue

												print(f"""The {number}th Proth number: {value}""")





