

import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						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''')
       _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    parser.parse_args()

       if args.model_type == "bert":
              _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    BertForMaskedLM.from_pretrained(args.model_name)
              _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    '''bert'''
       else:
              raise ValueError('''args.model_type should be "bert".''')

       _SCREAMING_SNAKE_CASE  :						List[str]         =    model.state_dict()
       _SCREAMING_SNAKE_CASE  :						Dict         =    {}

       for w in ["word_embeddings", "position_embeddings"]:
              _SCREAMING_SNAKE_CASE  :						Dict         =    state_dict[F"{prefix}.embeddings.{w}.weight"]
       for w in ["weight", "bias"]:
              _SCREAMING_SNAKE_CASE  :						int         =    state_dict[F"{prefix}.embeddings.LayerNorm.{w}"]

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

                     _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    state_dict[
                         F"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
                     ]
                     _SCREAMING_SNAKE_CASE  :						List[str]         =    state_dict[
                         F"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
                     ]

                     _SCREAMING_SNAKE_CASE  :						Any         =    state_dict[
                         F"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
                     ]
                     _SCREAMING_SNAKE_CASE  :						str         =    state_dict[
                         F"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
                     ]
                     _SCREAMING_SNAKE_CASE  :						List[Any]         =    state_dict[
                         F"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
                     ]
              std_idx += 1

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





from __future__ import annotations

import collections
import pprint
from pathlib import Path
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return "".join(sorted(_A       )       )
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return word_by_signature[signature(_A       )]


_SCREAMING_SNAKE_CASE  :						str         =    Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
_SCREAMING_SNAKE_CASE  :						str         =    sorted({word.strip().lower() for word in data.splitlines()})

_SCREAMING_SNAKE_CASE  :						List[Any]         =    collections.defaultdict(list)
for word in word_list:
       word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						Tuple         =    {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

       with open('''anagrams.txt''', '''w''') as file:
              file.write('''all_anagrams = \n ''')
              file.write(pprint.pformat(all_anagrams))


