






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							lowercase  :							str			  =    argparse.ArgumentParser(
							    description=(
							        '''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'''
							        ''' Distillation'''
							    )
							)
							parser.add_argument('''--model_type''', default='''roberta''', choices=['''roberta''', '''gpt2'''])
							parser.add_argument('''--model_name''', default='''roberta-large''', type=str)
							parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_roberta_048131723.pth''', type=str)
							parser.add_argument('''--vocab_transform''', action='''store_true''')
							lowercase  :							Any			  =    parser.parse_args()

							if args.model_type == "roberta":
														lowercase  :							str			  =    RobertaForMaskedLM.from_pretrained(args.model_name)
														lowercase  :							str			  =    '''roberta'''
							elif args.model_type == "gpt2":
														lowercase  :							List[Any]			  =    GPTaLMHeadModel.from_pretrained(args.model_name)
														lowercase  :							Optional[int]			  =    '''transformer'''

							lowercase  :							str			  =    model.state_dict()
							lowercase  :							Any			  =    {}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					lowercase  :							Union[str, Any]			  =    state_dict[F"""{prefix}.{param_name}"""]
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					lowercase  :							List[Any]			  =    F"""{prefix}.embeddings.{w}.weight"""
																					lowercase  :							int			  =    state_dict[param_name]
														for w in ["weight", "bias"]:
																					lowercase  :							List[Any]			  =    F"""{prefix}.embeddings.LayerNorm.{w}"""
																					lowercase  :							Tuple			  =    state_dict[param_name]

    # Transformer Blocks #
							lowercase  :							Union[str, Any]			  =    0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			lowercase  :							Union[str, Any]			  =    state_dict[
																																			    F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
																																			]
																					lowercase  :							List[str]			  =    state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			lowercase  :							List[str]			  =    state_dict[
																																			    F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					lowercase  :							int			  =    state_dict[F"""{layer}"""]
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												lowercase  :							Dict			  =    state_dict[F"""lm_head.dense.{w}"""]
																												lowercase  :							Optional[Any]			  =    state_dict[F"""lm_head.layer_norm.{w}"""]
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					lowercase  :							List[Any]			  =    state_dict[F"""{prefix}.ln_f.{w}"""]
														lowercase  :							Optional[int]			  =    state_dict['''lm_head.weight''']

							print(F"""N layers selected for distillation: {std_idx}""")
							print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

							print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
							torch.save(compressed_sd, args.dump_checkpoint)








def      lowerCAmelCase__	(    _a       :			int    ):
		if num < 0:
				return False

		snake_case_				:							int						 =       num
		snake_case_				:							int						 =       0
		while num > 0:
				snake_case_				:							Union[str, Any]						 =       rev_num * 10 + (num % 10)
				num //= 10

		return num_copy == rev_num


if __name__ == "__main__":
							import doctest

							doctest.testmod()
