
'''simple docstring'''
import argparse

import torch

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


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

A__   :		int 		=[
    ['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_     (						__SCREAMING_SNAKE_CASE						:  Optional[int] )				->       Dict:





  """simple docstring"""

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

  for parlai_name, hf_name in PATTERNS:
    __A		:     int												=		k.replace(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

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







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       Optional[Any]:





  """simple docstring"""

  __A		:     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:
    __A		:     List[str]												=		sd.pop(__SCREAMING_SNAKE_CASE )
    __A		:     Tuple												=		k.replace("""layernorm_embedding"""			,     """layer_norm""" )
    assert new_k not in sd
    __A		:     Any												=		v


A__   :		List[str] 		=['START']







@torch.no_grad()
def        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  Optional[int]			,     __SCREAMING_SNAKE_CASE						:  int )				->       Optional[Any]:





  """simple docstring"""

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

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


if __name__ == "__main__":
     A__   :		List[str] 		=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__   :		Union[str, Any] 		=parser.parse_args()
     convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)





'''simple docstring'''
import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       List[Any]:





  """simple docstring"""

  __A		:     Optional[int]												=		RemBertConfig.from_json_file(__SCREAMING_SNAKE_CASE )
  print("""Building PyTorch model from configuration: {}""".format(str(__SCREAMING_SNAKE_CASE ) ) )
  __A		:     str												=		RemBertModel(__SCREAMING_SNAKE_CASE )

  # Load weights from tf checkpoint
  load_tf_weights_in_rembert(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  # Save pytorch-model
  print("""Save PyTorch model to {}""".format(__SCREAMING_SNAKE_CASE ) )
  torch.save(model.state_dict()			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     A__   :		Optional[Any] 		=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
     )
     parser.add_argument(
         '--rembert_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained RemBERT model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     A__   :		Dict 		=parser.parse_args()
     convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



