"""simple docstring"""







import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
	_a  :  str=				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  :  Any=				parser.parse_args()

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

	_a  :  Optional[int]=				model.state_dict()
	_a  :  Optional[int]=				{}

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

    # Transformer Blocks #
	_a  :  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"]:
					_a  :  Tuple=				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  :  Tuple=				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  :  List[Any]=				state_dict[f'''{layer}''']
		if args.vocab_transform:
			for w in ["weight", "bias"]:
				_a  :  List[Any]=				state_dict[f'''lm_head.dense.{w}''']
				_a  :  Any=				state_dict[f'''lm_head.layer_norm.{w}''']
	elif args.model_type == "gpt2":
		for w in ["weight", "bias"]:
			_a  :  str=				state_dict[f'''{prefix}.ln_f.{w}''']
		_a  :  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)






"""simple docstring"""







from __future__ import annotations

_a  :  str=				"#"

class 	UpperCamelCase    :



				def __init__(self       :   str)					->			None:
					__snake_case    :							dict   	=				{}



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			None:
					__snake_case    :							Optional[Any]   	=				self._trie
					for char in text:
						if char not in trie:
							__snake_case    :							Optional[int]   	=				{}
						__snake_case    :							Optional[int]   	=				trie[char]
					__snake_case    :							Optional[int]   	=				True



				def       _lowercase  (self       :   List[str]			,						_A       :   str)					->			tuple | list:
					__snake_case    :							str   	=				self._trie
					for char in prefix:
						if char in trie:
							__snake_case    :							Optional[int]   	=				trie[char]
						else:
							return []
					return self._elements(_A)






				def       _lowercase  (self       :   List[Any]			,						_A       :   dict)					->			tuple:
					__snake_case    :							str   	=				[]
					for c, v in d.items():
						__snake_case    :							Optional[Any]   	=				[' '] if c == END else [(c + s) for s in self._elements(_A)]
						result.extend(_A)
					return tuple(_A)







_a  :  str=				Trie()
_a  :  Any=				("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
	trie.insert_word(word)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str						)							->    tuple:

	'''simple docstring'''





	__snake_case    :							Optional[int]   	=				trie.find_word(UpperCAmelCase_						)
	return tuple(string + word for word in suffixes						)
def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	print(autocomplete_using_trie('de'						)						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





