


import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class    UpperCAmelCase__	( UpperCamelCase_       ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Any              =  LayoutLMTokenizer
    UpperCAmelCase__						:		Any              =  LayoutLMTokenizerFast
    UpperCAmelCase__						:		int              =  True
    UpperCAmelCase__						:		Optional[Any]              =  True






    def _a (      self   )  ->	Any:
         super().setUp()

         __UpperCamelCase   							=[
             """[UNK]""",
             """[CLS]""",
             """[SEP]""",
             """want""",
             """##want""",
             """##ed""",
             """wa""",
             """un""",
             """runn""",
             """##ing""",
             """,""",
             """low""",
             """lowest""",
         ]
         __UpperCamelCase   							=os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['vocab_file']   )
         with open(self.vocab_file ,       'w' ,       encoding='utf-8'   ) as vocab_writer:
              vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]   )   )






    def _a (      self ,       **A_   )  ->	List[Any]:
         return LayoutLMTokenizer.from_pretrained(self.tmpdirname ,       **_a   )






    def _a (      self ,       A_   )  ->	Optional[Any]:
         __UpperCamelCase   							="""UNwant\u00E9d,running"""
         __UpperCamelCase   							="""unwanted, running"""
         return input_text, output_text






    def _a (      self   )  ->	Union[str, Any]:
         __UpperCamelCase   							=self.tokenizer_class(self.vocab_file   )

         __UpperCamelCase   							=tokenizer.tokenize('UNwant\u00E9d,running'   )
         self.assertListEqual(_a ,       ['un', '##want', '##ed', ',', 'runn', '##ing']   )
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_a   ) ,       [7, 4, 5, 10, 8, 9]   )






    def _a (      self   )  ->	List[Any]:
         pass









import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
   _A			=						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			=						parser.parse_args()

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

   _A			=						model.state_dict()
   _A			=						{}

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

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

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

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

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





