





import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows



def _a				(	lowerCamelCase:   Tuple )	->						List[str]:  # picklable for multiprocessing







      '''simple docstring'''






      return i + 1



@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def _a				(	)	->						List[Any]:







      '''simple docstring'''






      with parallel_backend('''spark''' ):
            assert ParallelBackendConfig.backend_name == "spark"

      __A          =				[1, 2, 3]
      with pytest.raises(lowerCamelCase ):
            with parallel_backend('''unsupported backend''' ):
                  map_nested(lowerCamelCase					,				lowerCamelCase					,				num_proc=2 )

      with pytest.raises(lowerCamelCase ):
            with parallel_backend('''unsupported backend''' ):
                  map_nested(lowerCamelCase					,				lowerCamelCase					,				num_proc=-1 )







@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('''num_proc'''					,				[2, -1] )
def _a				(	lowerCamelCase:   Dict )	->						List[Any]:







      '''simple docstring'''






      __A          =				[1, 2]
      __A          =				{'''a''': 1, '''b''': 2}
      __A          =				{'''a''': [1, 2], '''b''': [3, 4]}
      __A          =				{'''a''': {'''1''': 1}, '''b''': 2}
      __A          =				{'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
      __A          =				[2, 3]
      __A          =				{'''a''': 2, '''b''': 3}
      __A          =				{'''a''': [2, 3], '''b''': [4, 5]}
      __A          =				{'''a''': {'''1''': 2}, '''b''': 3}
      __A          =				{'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}

      with parallel_backend('''spark''' ):
            assert map_nested(lowerCamelCase					,				lowerCamelCase					,				num_proc=lowerCamelCase ) == expected_map_nested_sa
            assert map_nested(lowerCamelCase					,				lowerCamelCase					,				num_proc=lowerCamelCase ) == expected_map_nested_sa
            assert map_nested(lowerCamelCase					,				lowerCamelCase					,				num_proc=lowerCamelCase ) == expected_map_nested_sa
            assert map_nested(lowerCamelCase					,				lowerCamelCase					,				num_proc=lowerCamelCase ) == expected_map_nested_sa
            assert map_nested(lowerCamelCase					,				lowerCamelCase					,				num_proc=lowerCamelCase ) == expected_map_nested_sa












import argparse

import torch

from transformers import BertForMaskedLM


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

       if args.model_type == "bert":
              snake_case__					:							Dict													=     BertForMaskedLM.from_pretrained(args.model_name)
              snake_case__					:							Union[str, Any]													=     'bert'
       else:
              raise ValueError('args.model_type should be "bert".')

       snake_case__					:							Optional[int]													=     model.state_dict()
       snake_case__					:							List[Any]													=     {}

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

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

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

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

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





