





"""simple docstring"""




import argparse

import torch

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


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

UpperCamelCase_									=[
    ["""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_    (							_lowercase       ):
						if k == "embeddings.weight":
												return "shared.weight"

						for parlai_name, hf_name in PATTERNS:
												_UpperCamelCase      : List[str]         =   k.replace(_lowercase	,						_lowercase       )

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





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : Optional[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:
												_UpperCamelCase      : Any         =   sd.pop(_lowercase       )
												_UpperCamelCase      : int         =   k.replace('''layernorm_embedding'''	,						'''layer_norm'''       )
												assert new_k not in sd
												_UpperCamelCase      : List[str]         =   v


UpperCamelCase_									=["""START"""]





@torch.no_grad()
def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : int         =   torch.load(_lowercase	,						map_location='''cpu'''       )
						_UpperCamelCase      : str         =   model['''model''']
						_UpperCamelCase      : List[Any]         =   BlenderbotConfig.from_json_file(_lowercase       )
						_UpperCamelCase      : List[str]         =   BlenderbotForConditionalGeneration(_lowercase       )
						_UpperCamelCase      : Optional[int]         =   m.model.state_dict().keys()
						_UpperCamelCase      : Optional[Any]         =   []
						_UpperCamelCase      : Tuple         =   {}
						for k, v in sd.items():
												if k in IGNORE_KEYS:
																		continue

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


if __name__ == "__main__":
		UpperCamelCase_									=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"""
		)
		UpperCamelCase_									=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 MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						# Initialise PyTorch model
						_UpperCamelCase      : List[Any]         =   MobileBertConfig.from_json_file(_lowercase       )
						print(F"""Building PyTorch model from configuration: {config}"""       )
						_UpperCamelCase      : List[str]         =   MobileBertForPreTraining(_lowercase       )
						# Load weights from tf checkpoint
						_UpperCamelCase      : Union[str, Any]         =   load_tf_weights_in_mobilebert(_lowercase	,						_lowercase	,						_lowercase       )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
						torch.save(model.state_dict()	,						_lowercase       )


if __name__ == "__main__":
						UpperCamelCase_									=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(
						    """--mobilebert_config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """The config json file corresponding to the pre-trained MobileBERT 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."""
						)
						UpperCamelCase_									=parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



