




import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase__ :   Union[str, Any]					=							logging.get_logger(__name__)

lowerCamelCase__ :   Optional[Any]					=							[
    ['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_							(      __UpperCAmelCase						:	Optional[int]							)					-> Optional[int]:
      if k == "embeddings.weight":
            return "shared.weight"

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

      if k.startswith('encoder'							):
            SCREAMING_SNAKE_CASE_							=			k.replace('.attn'			,			'.self_attn'							)
            SCREAMING_SNAKE_CASE_							=			k.replace('norm1'			,			'self_attn_layer_norm'							)
            SCREAMING_SNAKE_CASE_							=			k.replace('norm2'			,			'final_layer_norm'							)
      elif k.startswith('decoder'							):
            SCREAMING_SNAKE_CASE_							=			k.replace('norm1'			,			'self_attn_layer_norm'							)
            SCREAMING_SNAKE_CASE_							=			k.replace('norm2'			,			'encoder_attn_layer_norm'							)
            SCREAMING_SNAKE_CASE_							=			k.replace('norm3'			,			'final_layer_norm'							)
      return k

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Tuple							)					-> Dict:
      SCREAMING_SNAKE_CASE_							=			[
          'model.encoder.layernorm_embedding.weight',
          'model.encoder.layernorm_embedding.bias',
          'model.decoder.layernorm_embedding.weight',
          'model.decoder.layernorm_embedding.bias',
      ]
      for k in keys:
            SCREAMING_SNAKE_CASE_							=			sd.pop(__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_							=			k.replace('layernorm_embedding'			,			'layer_norm'							)
            assert new_k not in sd
            SCREAMING_SNAKE_CASE_							=			v


lowerCamelCase__ :   Optional[int]					=							['START']

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

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


if __name__ == "__main__":
   lowerCamelCase__ :   Any					=							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'
   )
   lowerCamelCase__ :   Optional[Any]					=							parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)





import re

from filelock import FileLock


try:
   import nltk

   lowerCamelCase__ :   str					=							True
except (ImportError, ModuleNotFoundError):
   lowerCamelCase__ :   Union[str, Any]					=							False

if NLTK_AVAILABLE:
   with FileLock('.lock') as lock:
      nltk.download('punkt', quiet=True)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str							)					-> str:
      re.sub('<n>'			,			''			,			__UpperCAmelCase							)  # remove pegasus newline char
      assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
      return "\n".join(nltk.sent_tokenize(__UpperCAmelCase							)							)