






import argparse

import torch

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__   :      str				=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   :      Optional[int]				=		[
    ["""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   __lowercase       (			snake_case			):







				"""simple docstring"""






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

				for parlai_name, hf_name in PATTERNS:
								__magic_name__		:Any   =   k.replace(snake_case,	snake_case			)

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

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:List[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:
								__magic_name__		:Dict   =   sd.pop(snake_case			)
								__magic_name__		:int   =   k.replace('''layernorm_embedding''',	'''layer_norm'''			)
								assert new_k not in sd
								__magic_name__		:Optional[Any]   =   v


SCREAMING_SNAKE_CASE__   :      List[str]				=		["""START"""]

@torch.no_grad()
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






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

								__magic_name__		:Dict   =   rename_state_dict_key(snake_case			)
								if new_k not in valid_keys:
												failures.append([k, new_k]			)
								else:
												__magic_name__		:Optional[Any]   =   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__":
					SCREAMING_SNAKE_CASE__   :      Optional[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"""
					)
					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)











def 		a__      (		_UpperCamelCase      :			list[int]				):
   if not numbers:
      return 0

   if not isinstance(_UpperCamelCase			,(list, tuple)				) or not all(
       isinstance(_UpperCamelCase			,_UpperCamelCase				) for number in numbers				):
      raise ValueError('''numbers must be an iterable of integers'''				)

   __lowerCamelCase    				=     __lowerCamelCase    				=     __lowerCamelCase    				=     numbers[0]

   for i in range(1			,len(_UpperCamelCase				)				):
      # update the maximum and minimum subarray products
      __lowerCamelCase    				=     numbers[i]
      if number < 0:
         __lowerCamelCase				,__lowerCamelCase    				=     min_till_now, max_till_now
      __lowerCamelCase    				=     max(_UpperCamelCase			,max_till_now * number				)
      __lowerCamelCase    				=     min(_UpperCamelCase			,min_till_now * number				)

      # update the maximum product found till now
      __lowerCamelCase    				=     max(_UpperCamelCase			,_UpperCamelCase				)

   return max_prod






