


'''simple docstring'''
import argparse

import torch

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


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

UpperCamelCase__          = [
    ['''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__			(   lowerCAmelCase__		)       ->      Optional[Any]:
    if k == "embeddings.weight":
        return "shared.weight"

    for parlai_name, hf_name in PATTERNS:
        UpperCAmelCase__     :						str						=							k.replace(lowerCAmelCase__    ,   lowerCAmelCase__		)

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





def    a__			(   lowerCAmelCase__		)       ->      Any:
    UpperCAmelCase__     :						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:
        UpperCAmelCase__     :						str						=							sd.pop(lowerCAmelCase__		)
        UpperCAmelCase__     :						int						=							k.replace('''layernorm_embedding'''    ,   '''layer_norm'''		)
        assert new_k not in sd
        UpperCAmelCase__     :						Any						=							v


UpperCamelCase__          = ['''START''']





@torch.no_grad()
def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      List[Any]:
    UpperCAmelCase__     :						Optional[int]						=							torch.load(lowerCAmelCase__    ,   map_location='''cpu'''		)
    UpperCAmelCase__     :						Union[str, Any]						=							model['''model''']
    UpperCAmelCase__     :						Dict						=							BlenderbotConfig.from_json_file(lowerCAmelCase__		)
    UpperCAmelCase__     :						List[str]						=							BlenderbotForConditionalGeneration(lowerCAmelCase__		)
    UpperCAmelCase__     :						List[str]						=							m.model.state_dict().keys()
    UpperCAmelCase__     :						Union[str, Any]						=							[]
    UpperCAmelCase__     :						Union[str, Any]						=							{}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

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


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





'''simple docstring'''





import functools





def   a_   (		_UpperCAmelCase    :      list[int] ,_UpperCAmelCase    :      list[int]				)    ->  int:

      # Validation
      if not isinstance(_UpperCAmelCase ,_UpperCAmelCase				) or not all(isinstance(_UpperCAmelCase ,_UpperCAmelCase				) for day in days				):
            raise ValueError('The parameter days should be a list of integers'				)

      if len(_UpperCAmelCase				) != 3 or not all(isinstance(_UpperCAmelCase ,_UpperCAmelCase				) for cost in costs				):
            raise ValueError('The parameter costs should be a list of three integers'				)

      if len(_UpperCAmelCase				) == 0:
            return 0

      if min(_UpperCAmelCase				) <= 0:
            raise ValueError('All days elements should be greater than 0'				)

      if max(_UpperCAmelCase				) >= 3_66:
            raise ValueError('All days elements should be less than 366'				)

      __snake_case :   str													=    set(_UpperCAmelCase				)

      @functools.cache
      def dynamic_programming(_UpperCAmelCase    :      int				) -> int:
            if index > 3_65:
                  return 0

            if index not in days_set:
                  return dynamic_programming(index + 1				)

            return min(
                costs[0] + dynamic_programming(index + 1				) ,costs[1] + dynamic_programming(index + 7				) ,costs[2] + dynamic_programming(index + 30				) ,)

      return dynamic_programming(1				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



