






"""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      lowerCAmelCase__	(	UpperCamelCase__							):



  '''simple docstring'''

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

  for parlai_name, hf_name in PATTERNS:
    _a    :			List[str]			 =      k.replace(__a	,  __a							)

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

def      lowerCAmelCase__	(	UpperCamelCase__							):



  '''simple docstring'''
  _a    :			Optional[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:
    _a    :			int			 =      sd.pop(__a							)
    _a    :			int			 =      k.replace("""layernorm_embedding"""	,  """layer_norm"""							)
    assert new_k not in sd
    _a    :			Optional[Any]			 =      v


_snake_case        =  ['''START''']

@torch.no_grad()
def      lowerCAmelCase__	(	UpperCamelCase__	,  UpperCamelCase__	,  UpperCamelCase__							):



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

    _a    :			int			 =      rename_state_dict_key(__a							)
    if new_k not in valid_keys:
      failures.append([k, new_k]							)
    else:
      _a    :			Union[str, Any]			 =      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__":
 _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)








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






		"""simple docstring"""

		A__     =  len(__a       )
		while cur > 1:
				# Find the maximum number in arr
				A__     =  arr.index(max(arr[0:cur]       )       )
				# Reverse from 0 to mi
				A__     =  arr[mi::-1] + arr[mi + 1 : len(__a       )]
				# Reverse whole list
				A__     =  arr[cur - 1 :: -1] + arr[cur : len(__a       )]
				cur -= 1
		return arr


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



