






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, 11]:
              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)











import os

import pytest

from transformers.dynamic_module_utils import get_imports


UpperCamelCase    							=      '\nimport os\n'

UpperCamelCase    							=      '\ndef foo():\n    import os\n    return False\n'

UpperCamelCase    							=      '\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

UpperCamelCase    							=      [
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]


@pytest.mark.parametrize("""case"""					,							__lowerCAmelCase							)
def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Optional[Any]							)     ->  str:
   __UpperCamelCase	:			str					=					os.path.join(__lowerCAmelCase					,							"""test_file.py"""							)
   with open(__lowerCAmelCase					,							"""w"""							) as _tmp_file:
      _tmp_file.write(__lowerCAmelCase							)

   __UpperCamelCase	:			Optional[int]					=					get_imports(__lowerCAmelCase							)
   assert parsed_imports == ["os"]



