



"""simple docstring"""

import argparse

import torch

from transformers import BertForMaskedLM


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

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

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

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

     lowercase__					:						int												=			0
     for teacher_idx in [0, 2, 4, 7, 9, 11]:
          for w in ["weight", "bias"]:
               lowercase__					:						Dict												=			state_dict[
                   f'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'
               ]
               lowercase__					:						int												=			state_dict[
                   f'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'
               ]
               lowercase__					:						Tuple												=			state_dict[
                   f'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'
               ]

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

               lowercase__					:						int												=			state_dict[
                   f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
               ]
               lowercase__					:						Optional[int]												=			state_dict[
                   f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
               ]
               lowercase__					:						List[str]												=			state_dict[
                   f'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'
               ]
          std_idx += 1

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










"""simple docstring"""

from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info




def __lowercase    (							_a					):
    return {key.lstrip('''-'''					): value for key, value in zip(unknown_args[::2]	,    unknown_args[1::2]					)}




def __lowercase    (							):
    snake_case_				:  List[str]       =			ArgumentParser(
        '''HuggingFace Datasets CLI tool'''	,    usage='''datasets-cli <command> [<args>]'''	,    allow_abbrev=_a					)
    snake_case_				:  List[Any]       =			parser.add_subparsers(help='''datasets-cli command helpers'''					)
    set_verbosity_info()

    # Register commands
    ConvertCommand.register_subcommand(_a					)
    EnvironmentCommand.register_subcommand(_a					)
    TestCommand.register_subcommand(_a					)
    RunBeamCommand.register_subcommand(_a					)
    DummyDataCommand.register_subcommand(_a					)

    # Parse args
    snake_case_,			snake_case_				:  Optional[Any]       =			parser.parse_known_args()
    if not hasattr(_a	,    '''func'''					):
        parser.print_help()
        exit(1					)
    snake_case_				:  Optional[int]       =			parse_unknown_args(_a					)

    # Run
    snake_case_				:  Optional[int]       =			args.func(_a	,    **_a					)
    service.run()


if __name__ == "__main__":
     main()





