"""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"""







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       list[int]    ,							UpperCAmelCase_					:       str						)							->    list[int]:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				int(UpperCAmelCase_						)

	# Initialize Result
	__snake_case    :							int   	=				[]

	# Traverse through all denomination
	for denomination in reversed(UpperCAmelCase_						):
		# Find denominations
		while int(UpperCAmelCase_						) >= int(UpperCAmelCase_						):
			total_value -= int(UpperCAmelCase_						)
			answer.append(UpperCAmelCase_						)  # Append the "answers" array

	return answer


# Driver Code
if __name__ == "__main__":
	_a  :  Optional[int]=				[]
	_a  :  Optional[int]=				"0"

	if (
	    input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
	    == "y"
	):
		_a  :  int=				int(input("Enter the number of denominations you want to add: ").strip())

		for i in range(0, n):
			denominations.append(int(input(f'''Denomination {i}: ''').strip()))
		_a  :  Optional[int]=				input("Enter the change you want to make in Indian Currency: ").strip()
	else:
		# All denominations of Indian Currency if user does not enter
		_a  :  Tuple=				[1, 2, 5, 10, 20, 50, 100, 500, 2_000]
		_a  :  List[str]=				input("Enter the change you want to make: ").strip()

	if int(value) == 0 or int(value) < 0:
		print("The total value cannot be zero or negative.")

	else:
		print(f'''Following is minimal change for {value}: ''')
		_a  :  List[Any]=				find_minimum_change(denominations, value)
		# Print result
		for i in range(len(answer)):
			print(answer[i], end=" ")





