





import argparse

import torch

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


logging.set_verbosity_info()
UpperCAmelCase	:	List[str]		     =       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       (		SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



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

  for parlai_name, hf_name in PATTERNS:
    a__	:					Union[str, Any]										=k.replace(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

  if k.startswith("encoder"						):
    a__	:					Tuple										=k.replace(".attn"       ,       ".self_attn"						)
    a__	:					List[str]										=k.replace("norm1"       ,       "self_attn_layer_norm"						)
    a__	:					int										=k.replace("norm2"       ,       "final_layer_norm"						)
  elif k.startswith("decoder"						):
    a__	:					Tuple										=k.replace("norm1"       ,       "self_attn_layer_norm"						)
    a__	:					Any										=k.replace("norm2"       ,       "encoder_attn_layer_norm"						)
    a__	:					Optional[Any]										=k.replace("norm3"       ,       "final_layer_norm"						)
  return k
def      _A       (		SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  a__	:					str										=[
      "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__	:					List[Any]										=sd.pop(SCREAMING_SNAKE_CASE						)
    a__	:					List[Any]										=k.replace("layernorm_embedding"       ,       "layer_norm"						)
    assert new_k not in sd
    a__	:					Union[str, Any]										=v


UpperCAmelCase	:	Any		     =       ["""START"""]
@torch.no_grad()
def      _A       (		SCREAMING_SNAKE_CASE     :   List[str]       ,       SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   Optional[int]						):





  """simple docstring"""



  a__	:					int										=torch.load(SCREAMING_SNAKE_CASE       ,       map_location="cpu"						)
  a__	:					Any										=model["model"]
  a__	:					List[Any]										=BlenderbotConfig.from_json_file(SCREAMING_SNAKE_CASE						)
  a__	:					Optional[Any]										=BlenderbotForConditionalGeneration(SCREAMING_SNAKE_CASE						)
  a__	:					List[Any]										=m.model.state_dict().keys()
  a__	:					List[Any]										=[]
  a__	:					List[Any]										={}
  for k, v in sd.items():
    if k in IGNORE_KEYS:
      continue

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


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













def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]						):





  """simple docstring"""



  a__	:					Optional[Any]										=len(SCREAMING_SNAKE_CASE						)
  while cur > 1:
    # Find the maximum number in arr
    a__	:					List[Any]										=arr.index(max(arr[0:cur]						)						)
    # Reverse from 0 to mi
    a__	:					int										=arr[mi::-1] + arr[mi + 1 : len(SCREAMING_SNAKE_CASE						)]
    # Reverse whole list
    a__	:					List[str]										=arr[cur - 1 :: -1] + arr[cur : len(SCREAMING_SNAKE_CASE						)]
    cur -= 1
  return arr


if __name__ == "__main__":
     UpperCAmelCase	:	int		     =       input("""Enter numbers separated by a comma:\n""").strip()
     UpperCAmelCase	:	Optional[int]		     =       [int(item) for item in user_input.split(""",""")]
     print(pancake_sort(unsorted))






