
"""simple docstring"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       str		)     ->					list[int]:
  _snake_case					     =       [0 for i in range(len(__lowerCamelCase		)		)]

  # initialize interval's left pointer and right pointer
  _snake_case     ,					_snake_case					     =       0, 0

  for i in range(1 ,					len(__lowerCamelCase		)		):
    # case when current index is inside the interval
    if i <= right_pointer:
      _snake_case					     =       min(right_pointer - i + 1 ,					z_result[i - left_pointer]		)
      _snake_case					     =       min_edge

    while go_next(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		):
      z_result[i] += 1

    # if new index's result gives us more right interval,
    # we've to update left_pointer and right_pointer
    if i + z_result[i] - 1 > right_pointer:
      _snake_case     ,					_snake_case					     =       i, i + z_result[i] - 1

  return z_result
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int ,					__lowerCamelCase   :       list[int] ,					__lowerCamelCase   :       str		)     ->					bool:
  return i + z_result[i] < len(__lowerCamelCase		) and s[z_result[i]] == s[i + z_result[i]]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       str		)     ->					int:
  _snake_case					     =       0
  # concatenate 'pattern' and 'input_str' and call z_function
  # with concatenated string
  _snake_case					     =       z_function(pattern + input_str		)

  for val in z_result:
    # if value is greater then length of the pattern string
    # that means this index is starting position of substring
    # which is equal to pattern string
    if val >= len(__lowerCamelCase		):
      answer += 1

  return answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()





"""simple docstring"""



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)



