


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)




from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt

def 	lowerCAmelCase__			(      a__:		Sequence[float]      ,  a__:		int      ,  a__:		int	)  ->		tuple[int | None, int | None, float]:


							'''simple docstring'''





							if not arr:
														return None, None, 0
							if low == high:
														return low, high, arr[low]

							_UpperCAmelCase					=      (low + high) // 2
							_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      max_subarray(a__      ,  a__      ,  a__	)
							_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      max_subarray(a__      ,  mid + 1      ,  a__	)
							_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      max_cross_sum(a__      ,  a__      ,  a__      ,  a__	)
							if left_sum >= right_sum and left_sum >= cross_sum:
														return left_low, left_high, left_sum
							elif right_sum >= left_sum and right_sum >= cross_sum:
														return right_low, right_high, right_sum
							return cross_left, cross_right, cross_sum

def 	lowerCAmelCase__			(      a__:		Sequence[float]      ,  a__:		int      ,  a__:		int      ,  a__:		int	)  ->		tuple[int, int, float]:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      float('-inf'	), -1
							_UpperCAmelCase		,	_UpperCAmelCase					=      float('-inf'	), -1

							_UpperCAmelCase					=      0
							for i in range(a__      ,  low - 1      ,  -1	):
														summ += arr[i]
														if summ > left_sum:
																					_UpperCAmelCase					=      summ
																					_UpperCAmelCase					=      i

							_UpperCAmelCase					=      0
							for i in range(mid + 1      ,  high + 1	):
														summ += arr[i]
														if summ > right_sum:
																					_UpperCAmelCase					=      summ
																					_UpperCAmelCase					=      i

							return max_left, max_right, (left_sum + right_sum)

def 	lowerCAmelCase__			(      a__:		int	)  ->		float:


							'''simple docstring'''





							_UpperCAmelCase					=      [randint(1      ,  a__	) for _ in range(a__	)]
							_UpperCAmelCase					=      time.time()
							max_subarray(a__      ,  0      ,  input_size - 1	)
							_UpperCAmelCase					=      time.time()
							return end - start

def 	lowerCAmelCase__			(      )  ->		None:


							'''simple docstring'''





							_UpperCAmelCase					=      [1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
							_UpperCAmelCase					=      [time_max_subarray(a__	) for input_size in input_sizes]
							print('No of Inputs\t\tTime Taken'	)
							for input_size, runtime in zip(a__      ,  a__	):
														print(a__      ,  '\t\t'      ,  a__	)
							plt.plot(a__      ,  a__	)
							plt.xlabel('Number of Inputs'	)
							plt.ylabel('Time taken in seconds'	)
							plt.show()


if __name__ == "__main__":
		from doctest import testmod

		testmod()
