






'''simple docstring'''

UpperCAmelCase__						:List[Any]          =			256
# Modulus to hash a string
UpperCAmelCase__						:str          =			1_000_003
def     __lowercase  (_lowercase,						_lowercase     )   ->     bool:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				len(_lowercase     )
     __lowerCamelCase						:       List[str]				   =				len(_lowercase     )
     if p_len > t_len:
          return False

     __lowerCamelCase						:       Optional[Any]				   =				0
     __lowerCamelCase						:       Tuple				   =				0
     __lowerCamelCase						:       Dict				   =				1

     # Calculating the hash of pattern and substring of text
     for i in range(_lowercase     ):
          __lowerCamelCase						:       Optional[int]				   =				(ord(pattern[i]     ) + p_hash * alphabet_size) % modulus
          __lowerCamelCase						:       Dict				   =				(ord(text[i]     ) + text_hash * alphabet_size) % modulus
          if i == p_len - 1:
               continue
          __lowerCamelCase						:       Dict				   =				(modulus_power * alphabet_size) % modulus

     for i in range(0,						t_len - p_len + 1     ):
          if text_hash == p_hash and text[i : i + p_len] == pattern:
               return True
          if i == t_len - p_len:
               continue
          # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
          __lowerCamelCase						:       Dict				   =				(
              (text_hash - ord(text[i]     ) * modulus_power) * alphabet_size
              + ord(text[i + p_len]     )
          ) % modulus
     return False
def     __lowercase  ()   ->     None:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				"""abc1abc12"""
     __lowerCamelCase						:       Optional[Any]				   =				"""alskfjaldsabc1abc1abc12k23adsfabcabc"""
     __lowerCamelCase						:       List[Any]				   =				"""alskfjaldsk23adsfabcabc"""
     assert rabin_karp(_lowercase,						_lowercase     ) and not rabin_karp(_lowercase,						_lowercase     )

     # Test 2)
     __lowerCamelCase						:       Optional[int]				   =				"""ABABX"""
     __lowerCamelCase						:       Dict				   =				"""ABABZABABYABABX"""
     assert rabin_karp(_lowercase,						_lowercase     )

     # Test 3)
     __lowerCamelCase						:       Any				   =				"""AAAB"""
     __lowerCamelCase						:       int				   =				"""ABAAAAAB"""
     assert rabin_karp(_lowercase,						_lowercase     )

     # Test 4)
     __lowerCamelCase						:       Any				   =				"""abcdabcy"""
     __lowerCamelCase						:       Dict				   =				"""abcxabcdabxabcdabcdabcy"""
     assert rabin_karp(_lowercase,						_lowercase     )

     # Test 5)
     __lowerCamelCase						:       str				   =				"""Lü"""
     __lowerCamelCase						:       str				   =				"""Lüsai"""
     assert rabin_karp(_lowercase,						_lowercase     )
     __lowerCamelCase						:       Tuple				   =				"""Lue"""
     assert not rabin_karp(_lowercase,						_lowercase     )
     print("""Success."""     )


if __name__ == "__main__":
   test_rabin_karp()











'''simple docstring'''

import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     # Initialise PyTorch model
     __lowerCamelCase						:       str				   =				RemBertConfig.from_json_file(_lowercase     )
     print("""Building PyTorch model from configuration: {}""".format(str(_lowercase     )     )     )
     __lowerCamelCase						:       List[Any]				   =				RemBertModel(_lowercase     )

     # Load weights from tf checkpoint
     load_tf_weights_in_rembert(_lowercase,						_lowercase,						_lowercase     )

     # Save pytorch-model
     print("""Save PyTorch model to {}""".format(_lowercase     )     )
     torch.save(model.state_dict(),						_lowercase     )


if __name__ == "__main__":
   UpperCAmelCase__						:Tuple          =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   UpperCAmelCase__						:List[Any]          =			parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



