





"""simple docstring"""




import argparse

import torch

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


logging.set_verbosity_info()
_snake_case           =			logging.get_logger(__name__)

_snake_case           =			[
    ["""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       (   __magic_name__	):
   if k == "embeddings.weight":
      return "shared.weight"

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

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


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


_snake_case           =			["""START"""]


@torch.no_grad()
def _A       (   __magic_name__  , __magic_name__  , __magic_name__	):
   lowercase__			       =  torch.load(__magic_name__  , map_location="cpu"	)
   lowercase__			       =  model["model"]
   lowercase__			       =  BlenderbotConfig.from_json_file(__magic_name__	)
   lowercase__			       =  BlenderbotForConditionalGeneration(__magic_name__	)
   lowercase__			       =  m.model.state_dict().keys()
   lowercase__			       =  []
   lowercase__			       =  {}
   for k, v in sd.items():
      if k in IGNORE_KEYS:
         continue

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


if __name__ == "__main__":
  _snake_case           =			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"""
  )
  _snake_case           =			parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)













from __future__ import annotations


def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  0
   lowercase__			       =  len(__magic_name__	) - 1

   while i < j:
      if nums[i] + nums[j] == target:
         return [i, j]
      elif nums[i] + nums[j] < target:
         lowercase__			       =  i + 1
      else:
         lowercase__			       =  j - 1

   return []


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(F"""{two_pointer([2, 7, 11, 15], 9) = }""")






