





import argparse

import torch

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


logging.set_verbosity_info()
A       :   List[str]					= logging.get_logger(__name__)

A       :   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  :Optional[int]       )						->							Union[str, Any]:






		"""simple docstring"""

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

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

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







def   __lowerCamelCase   (				__a  :Optional[int]       )						->							Tuple:






		"""simple docstring"""

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


A       :   Tuple					= ['''START''']







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






		"""simple docstring"""

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

				A__     =  rename_state_dict_key(__a       )
				if new_k not in valid_keys:
						failures.append([k, new_k]       )
				else:
						A__     =  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__":
					A       :   Dict					= 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'''
					)
					A       :   Any					= parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)










def   __lowerCamelCase   (				__a  :int = 4_0_0_0_0_0_0       )						->							int:






		"""simple docstring"""

		A__     =  []
		A__ ,     A__     =  0, 1
		while b <= n:
				if b % 2 == 0:
						even_fibs.append(__a       )
				A__ ,     A__     =  b, a + b
		return sum(__a       )


if __name__ == "__main__":
					print(F'''{solution() = }''')



