

import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE	:    List[str]       =     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''')
       _SCREAMING_SNAKE_CASE	:    List[str]       =     parser.parse_args()

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

       _SCREAMING_SNAKE_CASE	:    Optional[int]       =     model.state_dict()
       _SCREAMING_SNAKE_CASE	:    str       =     {}

       for w in ["word_embeddings", "position_embeddings"]:
              _SCREAMING_SNAKE_CASE	:    Optional[int]       =     state_dict[F"{prefix}.embeddings.{w}.weight"]
       for w in ["weight", "bias"]:
              _SCREAMING_SNAKE_CASE	:    Tuple       =     state_dict[F"{prefix}.embeddings.LayerNorm.{w}"]

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

                     _SCREAMING_SNAKE_CASE	:    Optional[Any]       =     state_dict[
                         F"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
                     ]
                     _SCREAMING_SNAKE_CASE	:    Optional[Any]       =     state_dict[
                         F"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
                     ]

                     _SCREAMING_SNAKE_CASE	:    Any       =     state_dict[
                         F"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
                     ]
                     _SCREAMING_SNAKE_CASE	:    Tuple       =     state_dict[
                         F"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
                     ]
                     _SCREAMING_SNAKE_CASE	:    Tuple       =     state_dict[
                         F"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
                     ]
              std_idx += 1

       _SCREAMING_SNAKE_CASE	:    Tuple       =     state_dict['''cls.predictions.decoder.weight''']
       _SCREAMING_SNAKE_CASE	:    Tuple       =     state_dict['''cls.predictions.bias''']
       if args.vocab_transform:
              for w in ["weight", "bias"]:
                     _SCREAMING_SNAKE_CASE	:    int       =     state_dict[F"cls.predictions.transform.dense.{w}"]
                     _SCREAMING_SNAKE_CASE	:    Dict       =     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)






import unittest

from knapsack import greedy_knapsack as kp




class 							lowerCamelCase   (  unittest.TestCase ):


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	[1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
							__UpperCAmelCase			:							str	    =	[2, 4, 6, 8, 1_0, 1_2]
							__UpperCAmelCase			:							List[Any]	    =	1_0_0
							self.assertEqual(kp.calc_profit(lowercase__				,     lowercase__				,     lowercase__)				,     2_1_0)


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''max_weight must greater than zero.''')


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''Weight can not be negative.''')


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''Profit can not be negative.''')


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''max_weight must greater than zero.''')


	def  A(    self):
							self.assertRaisesRegex(
							    lowercase__				,     '''The length of profit and weight must be same.''')


if __name__ == "__main__":
				unittest.main()

