


'''simple docstring'''



# Algorithm for the pigeonhole sorting






def      _a     (						_lowercase :     Dict  ):





     '''simple docstring'''






     __UpperCAmelCase     :			List[str]  							=      min(_lowercase  )  # min() finds the minimum value
     __UpperCAmelCase     :			str  							=      max(_lowercase  )  # max() finds the maximum value

     __UpperCAmelCase     :			Optional[int]  							=      max_val - min_val + 1  # size is difference of max and min values plus one

     # list of pigeonholes of size equal to the variable size
     __UpperCAmelCase     :			List[str]  							=      [0] * size

     # Populate the pigeonholes.
     for x in a:
          assert isinstance(_lowercase      , _lowercase  ), "integers only please"
          holes[x - min_val] += 1

     # Putting the elements back into the array in an order.
     __UpperCAmelCase     :			Union[str, Any]  							=      0
     for count in range(_lowercase  ):
          while holes[count] > 0:
               holes[count] -= 1
               __UpperCAmelCase     :			Dict  							=      count + min_val
               i += 1




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[Any]  							=      [8, 3, 2, 7, 4, 6, 8]
     pigeonhole_sort(_lowercase  )
     print('''Sorted order is:'''      , ''' '''.join(_lowercase  )  )


if __name__ == "__main__":
      main()



'''simple docstring'''



import argparse

import torch

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


logging.set_verbosity_info()
__UpperCAmelCase      :Any			    =       logging.get_logger(__name__)

__UpperCAmelCase      :Dict			    =       [
    ["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     (						_lowercase :     Tuple  ):





     '''simple docstring'''






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

     for parlai_name, hf_name in PATTERNS:
          __UpperCAmelCase     :			Any  							=      k.replace(_lowercase      , _lowercase  )

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




def      _a     (						_lowercase :     Union[str, Any]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			int  							=      [
         '''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     :			Any  							=      sd.pop(_lowercase  )
          __UpperCAmelCase     :			Optional[int]  							=      k.replace('''layernorm_embedding'''      , '''layer_norm'''  )
          assert new_k not in sd
          __UpperCAmelCase     :			List[str]  							=      v


__UpperCAmelCase      :str			    =       ["START"]




@torch.no_grad()
def      _a     (						_lowercase :     Optional[int]      , _lowercase :     Optional[int]      , _lowercase :     str  ):





     '''simple docstring'''






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

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


if __name__ == "__main__":
      __UpperCAmelCase      :Optional[int]			    =       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      :Tuple			    =       parser.parse_args()
      convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)