



import argparse

import torch

from transformers import BertForMaskedLM


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

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

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

     for w in ["word_embeddings", "position_embeddings"]:
          a_						:     List[str]	     =		state_dict[f"""{prefix}.embeddings.{w}.weight"""]
     for w in ["weight", "bias"]:
          a_						:     List[Any]	     =		state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

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

               a_						:     List[Any]	     =		state_dict[
                   f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
               ]
               a_						:     int	     =		state_dict[
                   f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
               ]

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

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




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_						:     Tuple	     =		{
    'configuration_xmod': [
        'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XmodConfig',
        'XmodOnnxConfig',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     List[Any]	     =		[
         'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
         'XmodForCausalLM',
         'XmodForMaskedLM',
         'XmodForMultipleChoice',
         'XmodForQuestionAnswering',
         'XmodForSequenceClassification',
         'XmodForTokenClassification',
         'XmodModel',
         'XmodPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xmod import (
              XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
              XmodForCausalLM,
              XmodForMaskedLM,
              XmodForMultipleChoice,
              XmodForQuestionAnswering,
              XmodForSequenceClassification,
              XmodForTokenClassification,
              XmodModel,
              XmodPreTrainedModel,
          )

else:
     import sys

     a_						:     str	     =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)