import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
		__lowerCamelCase					:	int       =  argparse.ArgumentParser(
		    description=(
		        '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
		        ''' Distillation'''
		    )
		)
		parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
		parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
		parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
		parser.add_argument('''--vocab_transform''', action='''store_true''')
		__lowerCamelCase					:	Dict       =  parser.parse_args()

		if args.model_type == "bert":
				__lowerCamelCase					:	Any       =  BertForMaskedLM.from_pretrained(args.model_name)
				__lowerCamelCase					:	Any       =  '''bert'''
		else:
				raise ValueError('''args.model_type should be "bert".''')

		__lowerCamelCase					:	Dict       =  model.state_dict()
		__lowerCamelCase					:	str       =  {}

		for w in ["word_embeddings", "position_embeddings"]:
				__lowerCamelCase					:	int       =  state_dict[f'''{prefix}.embeddings.{w}.weight''']
		for w in ["weight", "bias"]:
				__lowerCamelCase					:	Any       =  state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']

		__lowerCamelCase					:	Tuple       =  0
		for teacher_idx in [0, 2, 4, 7, 9, 11]:
				for w in ["weight", "bias"]:
						__lowerCamelCase					:	Any       =  state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
						]
						__lowerCamelCase					:	str       =  state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
						]
						__lowerCamelCase					:	str       =  state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
						]

						__lowerCamelCase					:	str       =  state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
						]
						__lowerCamelCase					:	Any       =  state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
						]

						__lowerCamelCase					:	List[Any]       =  state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
						]
						__lowerCamelCase					:	int       =  state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
						]
						__lowerCamelCase					:	Optional[Any]       =  state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
						]
				std_idx += 1

		__lowerCamelCase					:	Optional[Any]       =  state_dict['''cls.predictions.decoder.weight''']
		__lowerCamelCase					:	Optional[int]       =  state_dict['''cls.predictions.bias''']
		if args.vocab_transform:
				for w in ["weight", "bias"]:
						__lowerCamelCase					:	str       =  state_dict[f'''cls.predictions.transform.dense.{w}''']
						__lowerCamelCase					:	Optional[Any]       =  state_dict[f'''cls.predictions.transform.LayerNorm.{w}''']

		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)








def __A    (      __lowerCAmelCase	)->  list:





						"""simple docstring"""



						if len(__lowerCAmelCase	) < 2:
												return collection

						def circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	) -> bool:
												_UpperCAmelCase									=			False

												if low == high:
																		return swapped

												_UpperCAmelCase									=			low
												_UpperCAmelCase									=			high

												while left < right:
																		if collection[left] > collection[right]:
																								_UpperCAmelCase					, _UpperCAmelCase									=			(
																								    collection[right],
																								    collection[left],
																								)
																								_UpperCAmelCase									=			True

																		left += 1
																		right -= 1

												if left == right and collection[left] > collection[right + 1]:
																		_UpperCAmelCase					, _UpperCAmelCase									=			(
																		    collection[right + 1],
																		    collection[left],
																		)

																		_UpperCAmelCase									=			True

												_UpperCAmelCase									=			low + int((high - low) / 2	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		mid + 1 ,		__lowerCAmelCase	)

												return swapped or left_swap or right_swap

						_UpperCAmelCase									=			True

						while is_not_sorted is True:
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		0 ,		len(__lowerCAmelCase	) - 1	)

						return collection


if __name__ == "__main__":
		_a      	=  input('''Enter numbers separated by a comma:\n''').strip()
		_a      	=  [int(item) for item in user_input.split(''',''')]
		print(circle_sort(unsorted))
