



import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
		_lowercase		:				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")
		_lowercase		:				List[str]      		=parser.parse_args()

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

		_lowercase		:				Optional[Any]      		=model.state_dict()
		_lowercase		:				str      		={}

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

    # Transformer Blocks #
		_lowercase		:				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		:				str      		=state_dict[
										    f'{prefix}.h.{teacher_idx}.{layer}.{w}'
										]
						_lowercase		:				int      		=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		:				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"]:
						_lowercase		:				Union[str, Any]      		=state_dict[f'{layer}']
				if args.vocab_transform:
						for w in ["weight", "bias"]:
								_lowercase		:				int      		=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		:				Dict      		=state_dict[f'{prefix}.ln_f.{w}']
				_lowercase		:				List[str]      		=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 __future__ import annotations

def      lowerCAmelCase_					(  _lowercase    :       float						,       _lowercase    :       float						,       _lowercase    :       float						,       )							->	tuple[str, float]:







			"""simple docstring"""
			if (stress, tangential_force, area).count(0) != 1:
						raise ValueError("""You cannot supply more or less than 2 values""")
			elif stress < 0:
						raise ValueError("""Stress cannot be negative""")
			elif tangential_force < 0:
						raise ValueError("""Tangential Force cannot be negative""")
			elif area < 0:
						raise ValueError("""Area cannot be negative""")
			elif stress == 0:
						return (
						    "stress",
						    tangential_force / area,
						)
			elif tangential_force == 0:
						return (
						    "tangential_force",
						    stress * area,
						)
			else:
						return (
						    "area",
						    tangential_force / stress,
						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




