


"""simple docstring"""


from typing import Any
class snake_case      :







		def __init__(					self      :       Optional[int]    ,   a__      :       Any		)       ->						str:





						'''simple docstring'''







						_A					  =			data
						_A					  =			None





class snake_case      :







		def __init__(					self      :       Optional[int]		)       ->						str:





						'''simple docstring'''







						_A					  =			None







		def  a_ (					self      :       List[Any]		)       ->						Any:





						'''simple docstring'''







						_A					  =			self.head
						while temp is not None:
										print(temp.data    ,   end=" "		)
										_A					  =			temp.next
						print()







		def  a_ (					self      :       Optional[Any]    ,   a__      :       Any		)       ->						Dict:





						'''simple docstring'''







						_A					  =			Node(a__		)
						_A					  =			self.head
						_A					  =			new_node







		def  a_ (					self      :       List[str]    ,   a__      :       Optional[Any]    ,   a__      :       List[Any]		)       ->						Tuple:





						'''simple docstring'''







						if node_data_a == node_data_a:
										return
						else:
										_A					  =			self.head
										while node_a is not None and node_a.data != node_data_a:
														_A					  =			node_a.next

										_A					  =			self.head
										while node_a is not None and node_a.data != node_data_a:
														_A					  =			node_a.next

										if node_a is None or node_a is None:
														return

										_A  ,		_A					  =			node_a.data, node_a.data


if __name__ == "__main__":
		a_       							=    LinkedList()
		for i in range(5, 0, -1):
				ll.push(i)

		ll.print_list()

		ll.swap_nodes(1, 4)
		print("After swapping")
		ll.print_list()



"""simple docstring"""


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
		a_       							=    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_       							=    parser.parse_args()

		if args.model_type == "roberta":
				a_       							=    RobertaForMaskedLM.from_pretrained(args.model_name)
				a_       							=    "roberta"
		elif args.model_type == "gpt2":
				a_       							=    GPTaLMHeadModel.from_pretrained(args.model_name)
				a_       							=    "transformer"

		a_       							=    model.state_dict()
		a_       							=    {}

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

    # Transformer Blocks #
		a_       							=    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_       							=    state_dict[
										    f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
										]
						a_       							=    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_       							=    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_       							=    state_dict[f'''{layer}''']
				if args.vocab_transform:
						for w in ["weight", "bias"]:
								a_       							=    state_dict[f'''lm_head.dense.{w}''']
								a_       							=    state_dict[f'''lm_head.layer_norm.{w}''']
		elif args.model_type == "gpt2":
				for w in ["weight", "bias"]:
						a_       							=    state_dict[f'''{prefix}.ln_f.{w}''']
				a_       							=    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)