


import argparse

import torch

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


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

__a	 =      [
    ["""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 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   if k == "embeddings.weight":
      return "shared.weight"

   for parlai_name, hf_name in PATTERNS:
      UpperCAmelCase      					=  k.replace(lowerCAmelCase_	,    lowerCAmelCase_ )

   if k.startswith("""encoder""" ):
      UpperCAmelCase      					=  k.replace(""".attn"""	,    """.self_attn""" )
      UpperCAmelCase      					=  k.replace("""norm1"""	,    """self_attn_layer_norm""" )
      UpperCAmelCase      					=  k.replace("""norm2"""	,    """final_layer_norm""" )
   elif k.startswith("""decoder""" ):
      UpperCAmelCase      					=  k.replace("""norm1"""	,    """self_attn_layer_norm""" )
      UpperCAmelCase      					=  k.replace("""norm2"""	,    """encoder_attn_layer_norm""" )
      UpperCAmelCase      					=  k.replace("""norm3"""	,    """final_layer_norm""" )
   return k
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Union[str, Any]:
   UpperCAmelCase      					=  [
       """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      					=  sd.pop(lowerCAmelCase_ )
      UpperCAmelCase      					=  k.replace("""layernorm_embedding"""	,    """layer_norm""" )
      assert new_k not in sd
      UpperCAmelCase      					=  v


__a	 =      ["""START"""]
@torch.no_grad()
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Dict:
   UpperCAmelCase      					=  torch.load(lowerCAmelCase_	,    map_location="""cpu""" )
   UpperCAmelCase      					=  model["""model"""]
   UpperCAmelCase      					=  BlenderbotConfig.from_json_file(lowerCAmelCase_ )
   UpperCAmelCase      					=  BlenderbotForConditionalGeneration(lowerCAmelCase_ )
   UpperCAmelCase      					=  m.model.state_dict().keys()
   UpperCAmelCase      					=  []
   UpperCAmelCase      					=  {}
   for k, v in sd.items():
      if k in IGNORE_KEYS:
         continue

      UpperCAmelCase      					=  rename_state_dict_key(lowerCAmelCase_ )
      if new_k not in valid_keys:
         failures.append([k, new_k] )
      else:
         UpperCAmelCase      					=  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__":
 __a	 =      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"""
 )
 __a	 =      parser.parse_args()
 convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)







class 	__lowercase					:





      def __init__(		self    :       Optional[int]							)     -> int:






         """simple docstring"""







         UpperCAmelCase      					=  {}





      def 							_lowercase					(		self    :       str							)     -> None:






         """simple docstring"""







         print(self.vertex							)
         for i in self.vertex:
            print(__lowerCamelCase       ,							""" -> """       ,							""" -> """.join([str(__lowerCamelCase							) for j in self.vertex[i]]							)							)





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       int							)     -> None:






         """simple docstring"""







         if from_vertex in self.vertex:
            self.vertex[from_vertex].append(__lowerCamelCase							)
         else:
            # else make a new vertex
            UpperCAmelCase      					=  [to_vertex]





      def 							_lowercase					(		self    :       str							)     -> None:






         """simple docstring"""







         UpperCAmelCase      					=  [False] * len(self.vertex							)

         # call the recursive helper function
         for i in range(len(self.vertex							)							):
            if not visited[i]:
               self.dfs_recursive(__lowerCamelCase       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       Optional[int]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       list							)     -> None:






         """simple docstring"""







         UpperCAmelCase      					=  True

         print(__lowerCamelCase       ,							end=""" """							)

         # Recur for all the vertices that are adjacent to this node
         for i in self.vertex:
            if not visited[i]:
               self.dfs_recursive(__lowerCamelCase       ,							__lowerCamelCase							)


if __name__ == "__main__":
 __a	 =      Graph()
 g.add_edge(0, 1)
 g.add_edge(0, 2)
 g.add_edge(1, 2)
 g.add_edge(2, 0)
 g.add_edge(2, 3)
 g.add_edge(3, 3)

 g.print_graph()
 print("""DFS:""")
 g.dfs()

 # OUTPUT:
 # 0  ->  1 -> 2
 # 1  ->  2
 # 2  ->  0 -> 3
 # 3  ->  3
 # DFS:
 #  0 1 2 3



