


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 	__lowercase		(  _UpperCamelCase   )			->Any:







      """simple docstring"""

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

      for parlai_name, hf_name in PATTERNS:
            lowercase	:      str          =						k.replace(snake_case_,			snake_case_   )

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




def 	__lowercase		(  _UpperCamelCase   )			->List[Any]:







      """simple docstring"""

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


__a       =					['''START''']




@torch.no_grad()
def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase   )			->Dict:







      """simple docstring"""

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

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


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)







from __future__ import annotations

import numpy as np







def 				lowerCAmelCase_	(     snake_case_     ):
      return np.maximum(0,snake_case_     )


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]

