


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
       lowercase_	   =     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')
       lowercase_	   =     parser.parse_args()

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

       lowercase_	   =     model.state_dict()
       lowercase_	   =     {}

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

       lowercase_	   =     0
       for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
              for w in ["weight", "bias"]:
                     lowercase_	   =     state_dict[
                         F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
                     ]
                     lowercase_	   =     state_dict[
                         F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
                     ]
                     lowercase_	   =     state_dict[
                         F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
                     ]

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

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

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





def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							str		 =						0
 __lowerCamelCase		:							Tuple		 =						len(SCREAMING_SNAKE_CASE__						)

 for i in range(n - 1						):
  for j in range(i + 1      ,	SCREAMING_SNAKE_CASE__						):
   if arr[i] > arr[j]:
    num_inversions += 1

 return num_inversions
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 if len(SCREAMING_SNAKE_CASE__						) <= 1:
  return arr, 0
 __lowerCamelCase		:							Optional[int]		 =						len(SCREAMING_SNAKE_CASE__						) // 2
 __lowerCamelCase		:							Union[str, Any]		 =						arr[0:mid]
 __lowerCamelCase		:							List[Any]		 =						arr[mid:]

 __lowerCamelCase ,					__lowerCamelCase		:							Any		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							List[str]		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						_count_cross_inversions(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							List[str]		 =						inversion_p + inversions_q + cross_inversions
 return c, num_inversions
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[int]		 =						[]
 __lowerCamelCase		:							List[Any]		 =						0
 while i < len(SCREAMING_SNAKE_CASE__						) and j < len(SCREAMING_SNAKE_CASE__						):
  if p[i] > q[j]:
   # if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
   # These are all inversions. The claim emerges from the
   # property that P is sorted.
   num_inversion += len(SCREAMING_SNAKE_CASE__						) - i
   r.append(q[j]						)
   j += 1
  else:
   r.append(p[i]						)
   i += 1

 if i < len(SCREAMING_SNAKE_CASE__						):
  r.extend(p[i:]						)
 else:
  r.extend(q[j:]						)

 return r, num_inversion
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							Optional[int]		 =						[10, 2, 1, 5, 5, 2, 11]

 # this arr has 8 inversions:
 # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

 __lowerCamelCase		:							Optional[Any]		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Optional[Any]		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 8

 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)

 # testing an array with zero inversion (a sorted arr_1)

 arr_a.sort()
 __lowerCamelCase		:							Optional[Any]		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							int		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 0
 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)

 # an empty list should also have zero inversions
 __lowerCamelCase		:							List[str]		 =						[]
 __lowerCamelCase		:							Dict		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 0
 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       main()

