



import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			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__      :			str							=		parser.parse_args()

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

					SCREAMING_SNAKE_CASE__      :			Tuple							=		model.state_dict()
					SCREAMING_SNAKE_CASE__      :			Any							=		{}

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

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














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		{"""tokenization_herbert""": ["""HerbertTokenizer"""]}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		["""HerbertTokenizerFast"""]


if TYPE_CHECKING:
					from .tokenization_herbert import HerbertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_herbert_fast import HerbertTokenizerFast

else:
					import sys

					SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






