


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_   )						->Any:
   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_   )						->int:
   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)







import math






class   __lowercase				:





      def        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					list[list[float]]					,       __lowerCamelCase						:					list[int]       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     0.0
         UpperCAmelCase							=     0.0
         for i in range(len(__lowerCamelCase       )       ):
            da += math.pow((sample[i] - weights[0][i])					,       2       )
            da += math.pow((sample[i] - weights[1][i])					,       2       )
            return 0 if da > da else 1
         return 0





      def        _lowercase				(		self						:					List[Any]					,       __lowerCamelCase						:					list[list[int | float]]					,       __lowerCamelCase						:					list[int]					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					float       )    ->   list[list[int | float]]:






         """simple docstring"""







         for i in range(len(__lowerCamelCase       )       ):
            weights[j][i] += alpha * (sample[i] - weights[j][i])
         return weights

def   _UpperCamelCase				(						)						->None:
   # Training Examples ( m, n )
   UpperCAmelCase							=     [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

   # weight initialization ( n, C )
   UpperCAmelCase							=     [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

   # training
   UpperCAmelCase							=     SelfOrganizingMap()
   UpperCAmelCase							=     3
   UpperCAmelCase							=     0.5

   for _ in range(lowerCAmelCase_   ):
      for j in range(len(lowerCAmelCase_   )   ):
         # training sample
         UpperCAmelCase							=     training_samples[j]

         # Compute the winning vector
         UpperCAmelCase							=     self_organizing_map.get_winner(lowerCAmelCase_  ,						lowerCAmelCase_   )

         # Update the winning vector
         UpperCAmelCase							=     self_organizing_map.update(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )

    # classify test sample
   UpperCAmelCase							=     [0, 0, 0, 1]
   UpperCAmelCase							=     self_organizing_map.get_winner(lowerCAmelCase_  ,						lowerCAmelCase_   )

   # results
   print(F"""Clusters that the test sample belongs to : {winner}"""   )
   print(F"""Weights that have been trained : {weights}"""   )


# running the main() function
if __name__ == "__main__":
 main()



