import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
      UpperCamelCase__											=					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__											=					parser.parse_args()

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

      UpperCamelCase__											=					model.state_dict()
      UpperCamelCase__											=					{}

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

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

                  UpperCamelCase__											=					state_dict[
                      f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
                  ]
                  UpperCamelCase__											=					state_dict[
                      f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
                  ]

                  UpperCamelCase__											=					state_dict[
                      f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
                  ]
                  UpperCamelCase__											=					state_dict[
                      f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
                  ]
                  UpperCamelCase__											=					state_dict[
                      f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
                  ]
            std_idx += 1

      UpperCamelCase__											=					state_dict['cls.predictions.decoder.weight']
      UpperCamelCase__											=					state_dict['cls.predictions.bias']
      if args.vocab_transform:
            for w in ["weight", "bias"]:
                  UpperCamelCase__											=					state_dict[f'''cls.predictions.transform.dense.{w}''']
                  UpperCamelCase__											=					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    lowerCAmelCase_ (     __A,  __A							)     ->				None:







      '''simple docstring'''


      UpperCAmelCase__								=  len(__A							)
      print("The following activities are selected:"							)

      # The first activity is always selected
      UpperCAmelCase__								=  0
      print(__A,  end=","							)

      # Consider rest of the activities
      for j in range(__A							):
            # If this activity has start time greater than
            # or equal to the finish time of previously
            # selected activity, then select it
            if start[j] >= finish[i]:
                  print(__A,  end=","							)
                  UpperCAmelCase__								=  j


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCamelCase__											=					[1, 3, 0, 5, 8, 5]
      UpperCamelCase__											=					[2, 4, 6, 7, 9, 9]
      print_max_activities(start, finish)



