





"""simple docstring"""




import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
		a		:				Optional[Any]  			=    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''')
		a		:				str  			=    parser.parse_args()

		if args.model_type == "roberta":
				a		:				int  			=    RobertaForMaskedLM.from_pretrained(args.model_name)
				a		:				Dict  			=    '''roberta'''
		elif args.model_type == "gpt2":
				a		:				Tuple  			=    GPTaLMHeadModel.from_pretrained(args.model_name)
				a		:				List[str]  			=    '''transformer'''

		a		:				Dict  			=    model.state_dict()
		a		:				int  			=    {}

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

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




a		:				str  			=    8.314_4598






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					float					, _lowercase		:					float      )				->float:






	'''simple docstring'''






	if temperature < 0:
		raise Exception("Temperature cannot be less than 0 K"      )
	if molar_mass <= 0:
		raise Exception("Molar mass cannot be less than or equal to 0 kg/mol"      )
	else:
		return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
		import doctest

		# run doctest
		doctest.testmod()

		# example
		a		:				Any  			=    300
		a		:				Dict  			=    28
		a		:				Dict  			=    rms_speed_of_molecule(temperature, molar_mass)
		print(F'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''')


