




'''simple docstring'''





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:   Optional[int]      		= 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")
		_SCREAMING_SNAKE_CASE							:   List[str]      		= parser.parse_args()

		if args.model_type == "roberta":
				_SCREAMING_SNAKE_CASE							:   int      		= RobertaForMaskedLM.from_pretrained(args.model_name)
				_SCREAMING_SNAKE_CASE							:   List[Any]      		= "roberta"
		elif args.model_type == "gpt2":
				_SCREAMING_SNAKE_CASE							:   Tuple      		= GPTaLMHeadModel.from_pretrained(args.model_name)
				_SCREAMING_SNAKE_CASE							:   Any      		= "transformer"

		_SCREAMING_SNAKE_CASE							:   Tuple      		= model.state_dict()
		_SCREAMING_SNAKE_CASE							:   str      		= {}

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

    # Transformer Blocks #
		_SCREAMING_SNAKE_CASE							:   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"]:
										_SCREAMING_SNAKE_CASE							:   Any      		= state_dict[
										    F"{prefix}.h.{teacher_idx}.{layer}.{w}"
										]
						_SCREAMING_SNAKE_CASE							:   Dict      		= 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"]:
										_SCREAMING_SNAKE_CASE							:   Union[str, Any]      		= 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"]:
						_SCREAMING_SNAKE_CASE							:   Union[str, Any]      		= state_dict[F"{layer}"]
				if args.vocab_transform:
						for w in ["weight", "bias"]:
								_SCREAMING_SNAKE_CASE							:   List[str]      		= state_dict[F"lm_head.dense.{w}"]
								_SCREAMING_SNAKE_CASE							:   Dict      		= state_dict[F"lm_head.layer_norm.{w}"]
		elif args.model_type == "gpt2":
				for w in ["weight", "bias"]:
						_SCREAMING_SNAKE_CASE							:   List[Any]      		= state_dict[F"{prefix}.ln_f.{w}"]
				_SCREAMING_SNAKE_CASE							:   Optional[Any]      		= 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)









'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import _LazyModule


A_							: int						 =	{"""tokenization_bertweet""": ["""BertweetTokenizer"""]}


if TYPE_CHECKING:
   from .tokenization_bertweet import BertweetTokenizer

else:
   import sys

   A_							: int						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



