


def      snake_case_						( __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Optional[Any]						=   0
   UpperCAmelCase_							:      Dict						=   len(__lowercase       ) - 1

   while left <= right:
      # avoid divided by 0 during interpolation
      if sorted_collection[left] == sorted_collection[right]:
         if sorted_collection[left] == item:
            return left
         else:
            return None

      UpperCAmelCase_							:      int						=   left + ((item - sorted_collection[left]) * (right - left)) // (
          sorted_collection[right] - sorted_collection[left]
      )

      # out of range check
      if point < 0 or point >= len(__lowercase       ):
         return None

      UpperCAmelCase_							:      Optional[Any]						=   sorted_collection[point]
      if current_item == item:
         return point
      else:
         if point < left:
            UpperCAmelCase_							:      List[str]						=   left
            UpperCAmelCase_							:      int						=   point
         elif point > right:
            UpperCAmelCase_							:      int						=   right
            UpperCAmelCase_							:      Dict						=   point
         else:
            if item < current_item:
               UpperCAmelCase_							:      List[Any]						=   point - 1
            else:
               UpperCAmelCase_							:      List[Any]						=   point + 1
   return None
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase			,       __lowercase       ):

   # avoid divided by 0 during interpolation
   if sorted_collection[left] == sorted_collection[right]:
      if sorted_collection[left] == item:
         return left
      else:
         return None

   UpperCAmelCase_							:      Union[str, Any]						=   left + ((item - sorted_collection[left]) * (right - left)) // (
       sorted_collection[right] - sorted_collection[left]
   )

   # out of range check
   if point < 0 or point >= len(__lowercase       ):
      return None

   if sorted_collection[point] == item:
      return point
   elif point < left:
      return interpolation_search_by_recursion(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase       )
   elif point > right:
      return interpolation_search_by_recursion(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase       )
   else:
      if sorted_collection[point] > item:
         return interpolation_search_by_recursion(
             __lowercase			,       __lowercase			,       __lowercase			,       point - 1       )
      else:
         return interpolation_search_by_recursion(
             __lowercase			,       __lowercase			,       point + 1			,       __lowercase       )
def      snake_case_						( __lowercase       ):
   if collection != sorted(__lowercase       ):
      raise ValueError('''Collection must be ascending sorted'''       )
   return True


if __name__ == "__main__":
       import sys
       __UpperCamelCase				:  str      					=							0
       if debug == 1:
              __UpperCamelCase				:  List[Any]      					=							[10, 30, 40, 45, 50, 66, 77, 93]
              try:
                     __assert_sorted(collection)
              except ValueError:
                     sys.exit('Sequence must be ascending sorted to apply interpolation search')
              __UpperCamelCase				:  Tuple      					=							67

       __UpperCamelCase				:  Dict      					=							interpolation_search(collection, target)
       if result is not None:
              print(F'{target} found at positions: {result}')
       else:
              print('Not found')



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def      snake_case_						( __lowercase			,       __lowercase       ):
   # Load checkpoint
   UpperCAmelCase_							:      Tuple						=   torch.load(__lowercase			,       map_location='''cpu'''       )

   UpperCAmelCase_							:      Optional[int]						=   chkpt['''model''']

   # We have the base model one level deeper than the original XLM repository
   UpperCAmelCase_							:      str						=   {}
   for k, v in state_dict.items():
      if "pred_layer" in k:
         UpperCAmelCase_							:      Tuple						=   v
      else:
         UpperCAmelCase_							:      Union[str, Any]						=   v

   UpperCAmelCase_							:      int						=   chkpt['''params''']
   UpperCAmelCase_							:      Union[str, Any]						=   {n: v for n, v in config.items() if not isinstance(__lowercase			,       (torch.FloatTensor, numpy.ndarray)       )}

   UpperCAmelCase_							:      int						=   chkpt['''dico_word2id''']
   UpperCAmelCase_							:      List[Any]						=   {s + '''</w>''' if s.find('''@@'''       ) == -1 and i > 1_3 else s.replace('''@@'''			,       ''''''       ): i for s, i in vocab.items()}

   # Save pytorch-model
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + CONFIG_NAME
   UpperCAmelCase_							:      Dict						=   pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

   print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''       )
   torch.save(__lowercase			,       __lowercase       )

   print(F'''Save configuration file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )

   print(F'''Save vocab file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )


if __name__ == "__main__":
       __UpperCamelCase				:  str      					=							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       __UpperCamelCase				:  Dict      					=							parser.parse_args()
       convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)