




def 	A ( _SCREAMING_SNAKE_CASE       )      ->					Optional[int]:
       lowerCamelCase  : List[str]     					=   0
       lowerCamelCase  : Optional[int]     					=   len(__snake_case       )

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

       return num_inversions

def 	A ( _SCREAMING_SNAKE_CASE       )      ->					Optional[Any]:
       if len(__snake_case       ) <= 1:
              return arr, 0
       lowerCamelCase  : List[Any]     					=   len(__snake_case       ) // 2
       lowerCamelCase  : int     					=   arr[0:mid]
       lowerCamelCase  : List[Any]     					=   arr[mid:]

       lowerCamelCase  : Optional[Any]     					=   count_inversions_recursive(__snake_case       )
       lowerCamelCase  : Any     					=   count_inversions_recursive(__snake_case       )
       lowerCamelCase  : str     					=   _count_cross_inversions(__snake_case					,__snake_case       )

       lowerCamelCase  : int     					=   inversion_p + inversions_q + cross_inversions
       return c, num_inversions

def 	A ( _SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE       )      ->					Tuple:
       lowerCamelCase  : List[str]     					=   []
       lowerCamelCase  : Optional[int]     					=   0
       while i < len(__snake_case       ) and j < len(__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(__snake_case       ) - i
                     r.append(q[j]       )
                     j += 1
              else:
                     r.append(p[i]       )
                     i += 1

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

       return r, num_inversion

def 	A ( )      ->					Any:
       lowerCamelCase  : Optional[Any]     					=   [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  : Tuple     					=   count_inversions_bf(__snake_case       )
       lowerCamelCase  : Optional[Any]     					=   count_inversions_recursive(__snake_case       )

       assert num_inversions_bf == num_inversions_recursive == 8

       print("number of inversions = "					,__snake_case       )

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

       arr_a.sort()
       lowerCamelCase  : str     					=   count_inversions_bf(__snake_case       )
       lowerCamelCase  : Any     					=   count_inversions_recursive(__snake_case       )

       assert num_inversions_bf == num_inversions_recursive == 0
       print("number of inversions = "					,__snake_case       )

       # an empty list should also have zero inversions
       lowerCamelCase  : Union[str, Any]     					=   []
       lowerCamelCase  : List[Any]     					=   count_inversions_bf(__snake_case       )
       lowerCamelCase  : List[Any]     					=   count_inversions_recursive(__snake_case       )

       assert num_inversions_bf == num_inversions_recursive == 0
       print("number of inversions = "					,__snake_case       )


if __name__ == "__main__":
       main()













'''simple docstring'''




import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__       : Tuple         =       logging.get_logger(__name__)

lowercase__       : str         =       [
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	Tuple:
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          __A		:					Optional[Any]						=			k.replace(__snake_case					,				__snake_case     )

     if k.startswith('encoder'     ):
          __A		:					Any						=			k.replace('.attn'					,				'.self_attn'     )
          __A		:					Any						=			k.replace('norm1'					,				'self_attn_layer_norm'     )
          __A		:					str						=			k.replace('norm2'					,				'final_layer_norm'     )
     elif k.startswith('decoder'     ):
          __A		:					Tuple						=			k.replace('norm1'					,				'self_attn_layer_norm'     )
          __A		:					str						=			k.replace('norm2'					,				'encoder_attn_layer_norm'     )
          __A		:					int						=			k.replace('norm3'					,				'final_layer_norm'     )
     return k






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	Dict:
     __A		:					Optional[int]						=			[
         'model.encoder.layernorm_embedding.weight',
         'model.encoder.layernorm_embedding.bias',
         'model.decoder.layernorm_embedding.weight',
         'model.decoder.layernorm_embedding.bias',
     ]
     for k in keys:
          __A		:					Tuple						=			sd.pop(__snake_case     )
          __A		:					Union[str, Any]						=			k.replace('layernorm_embedding'					,				'layer_norm'     )
          assert new_k not in sd
          __A		:					str						=			v


lowercase__       : Tuple         =       ['''START''']






@torch.no_grad()
def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					Any					,				__snake_case    :					List[Any]     )	->	int:
     __A		:					List[str]						=			torch.load(__snake_case					,				map_location='cpu'     )
     __A		:					Tuple						=			model['model']
     __A		:					str						=			BlenderbotConfig.from_json_file(__snake_case     )
     __A		:					int						=			BlenderbotForConditionalGeneration(__snake_case     )
     __A		:					List[Any]						=			m.model.state_dict().keys()
     __A		:					Optional[int]						=			[]
     __A		:					Optional[int]						=			{}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

          __A		:					Union[str, Any]						=			rename_state_dict_key(__snake_case     )
          if new_k not in valid_keys:
               failures.append([k, new_k]     )
          else:
               __A		:					Optional[Any]						=			v
     if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
          rename_layernorm_keys(__snake_case     )
     m.model.load_state_dict(__snake_case					,				strict=__snake_case     )
     m.half()
     m.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Dict         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
  parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
  parser.add_argument(
      '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)