
"""simple docstring"""




import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
					UpperCAmelCase__      :    Optional[Any]    							= 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')
					UpperCAmelCase__      :    Optional[int]    							= parser.parse_args()

					if args.model_type == "bert":
										UpperCAmelCase__      :    str    							= BertForMaskedLM.from_pretrained(args.model_name)
										UpperCAmelCase__      :    str    							= 'bert'
					else:
										raise ValueError('args.model_type should be \"bert\".')

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

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

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

															UpperCAmelCase__      :    int    							= state_dict[
															    f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
															]
															UpperCAmelCase__      :    str    							= state_dict[
															    f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
															]

															UpperCAmelCase__      :    Tuple    							= state_dict[
															    f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
															]
															UpperCAmelCase__      :    Dict    							= state_dict[
															    f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
															]
															UpperCAmelCase__      :    Dict    							= state_dict[
															    f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
															]
										std_idx += 1

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









def        a						(      _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	0
		__UpperCAmelCase    :     str													=	len(_UpperCAmelCase						)

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

		return num_inversions

def        a						(      _UpperCAmelCase							:					str						):







		'''simple docstring'''





		if len(_UpperCAmelCase						) <= 1:
				return arr, 0
		__UpperCAmelCase    :     Dict													=	len(_UpperCAmelCase						) // 2
		__UpperCAmelCase    :     Union[str, Any]													=	arr[0:mid]
		__UpperCAmelCase    :     Optional[Any]													=	arr[mid:]

		__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	count_inversions_recursive(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[Any]													=	count_inversions_recursive(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	_count_cross_inversions(_UpperCAmelCase ,   _UpperCAmelCase						)

		__UpperCAmelCase    :     List[str]													=	inversion_p + inversions_q + cross_inversions
		return c, num_inversions

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	[]
		__UpperCAmelCase    :     Any													=	0
		while i < len(_UpperCAmelCase						) and j < len(_UpperCAmelCase						):
				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(_UpperCAmelCase						) - i
						r.append(q[j]						)
						j += 1
				else:
						r.append(p[i]						)
						i += 1

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

		return r, num_inversion

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	[10, 2, 1, 5, 5, 2, 11]

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

		__UpperCAmelCase    :     List[str]													=	count_inversions_bf(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	count_inversions_recursive(_UpperCAmelCase						)

		assert num_inversions_bf == num_inversions_recursive == 8

		print('''number of inversions = ''' ,   _UpperCAmelCase						)

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

		arr_a.sort()
		__UpperCAmelCase    :     Any													=	count_inversions_bf(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	count_inversions_recursive(_UpperCAmelCase						)

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

		# an empty list should also have zero inversions
		__UpperCAmelCase    :     Tuple													=	[]
		__UpperCAmelCase    :     Union[str, Any]													=	count_inversions_bf(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	count_inversions_recursive(_UpperCAmelCase						)

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


if __name__ == "__main__":
				main()





