


def 	lowerCAmelCase__			(      a__:		Optional[Any]	)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      0
							_UpperCAmelCase					=      len(a__	)

							for i in range(n - 1	):
														for j in range(i + 1      ,  a__	):
																					if arr[i] > arr[j]:
																												num_inversions += 1

							return num_inversions

def 	lowerCAmelCase__			(      a__:		Optional[int]	)  ->		List[str]:


							'''simple docstring'''





							if len(a__	) <= 1:
														return arr, 0
							_UpperCAmelCase					=      len(a__	) // 2
							_UpperCAmelCase					=      arr[0:mid]
							_UpperCAmelCase					=      arr[mid:]

							_UpperCAmelCase		,	_UpperCAmelCase					=      count_inversions_recursive(a__	)
							_UpperCAmelCase		,	_UpperCAmelCase					=      count_inversions_recursive(a__	)
							_UpperCAmelCase		,	_UpperCAmelCase					=      _count_cross_inversions(a__      ,  a__	)

							_UpperCAmelCase					=      inversion_p + inversions_q + cross_inversions
							return c, num_inversions

def 	lowerCAmelCase__			(      a__:		Tuple      ,  a__:		Optional[int]	)  ->		Any:


							'''simple docstring'''





							_UpperCAmelCase					=      []
							_UpperCAmelCase					=      _UpperCAmelCase					=      _UpperCAmelCase					=      0
							while i < len(a__	) and j < len(a__	):
														if p[i] > q[j]:
																					# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
																					# These are all inversions. The claim emerges from the
																					# property that P is sorted.
																					num_inversion += len(a__	) - i
																					r.append(q[j]	)
																					j += 1
														else:
																					r.append(p[i]	)
																					i += 1

							if i < len(a__	):
														r.extend(p[i:]	)
							else:
														r.extend(q[j:]	)

							return r, num_inversion

def 	lowerCAmelCase__			(      )  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      [1_0, 2, 1, 5, 5, 2, 1_1]

							# this arr has 8 inversions:
							# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

							_UpperCAmelCase					=      count_inversions_bf(a__	)
							_UpperCAmelCase		,	_UpperCAmelCase					=      count_inversions_recursive(a__	)

							assert num_inversions_bf == num_inversions_recursive == 8

							print('number of inversions = '      ,  a__	)

							# testing an array with zero inversion (a sorted arr_1)

							arr_a.sort()
							_UpperCAmelCase					=      count_inversions_bf(a__	)
							_UpperCAmelCase		,	_UpperCAmelCase					=      count_inversions_recursive(a__	)

							assert num_inversions_bf == num_inversions_recursive == 0
							print('number of inversions = '      ,  a__	)

							# an empty list should also have zero inversions
							_UpperCAmelCase					=      []
							_UpperCAmelCase					=      count_inversions_bf(a__	)
							_UpperCAmelCase		,	_UpperCAmelCase					=      count_inversions_recursive(a__	)

							assert num_inversions_bf == num_inversions_recursive == 0
							print('number of inversions = '      ,  a__	)


if __name__ == "__main__":
		main()




import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
		lowerCAmelCase__  :Tuple             =							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__  :Optional[int]             =							parser.parse_args()

		if args.model_type == "bert":
				lowerCAmelCase__  :Tuple             =							BertForMaskedLM.from_pretrained(args.model_name)
				lowerCAmelCase__  :Optional[int]             =							'''bert'''
		else:
				raise ValueError('''args.model_type should be "bert".''')

		lowerCAmelCase__  :Any             =							model.state_dict()
		lowerCAmelCase__  :Dict             =							{}

		for w in ["word_embeddings", "position_embeddings"]:
				lowerCAmelCase__  :List[Any]             =							state_dict[f'''{prefix}.embeddings.{w}.weight''']
		for w in ["weight", "bias"]:
				lowerCAmelCase__  :Union[str, Any]             =							state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']

		lowerCAmelCase__  :str             =							0
		for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
				for w in ["weight", "bias"]:
						lowerCAmelCase__  :Any             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
						]
						lowerCAmelCase__  :List[str]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
						]

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

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