






'''simple docstring'''

from __future__ import annotations

from collections import deque







class 	SCREAMING_SNAKE_CASE	:




       def __init__(					self      : int	,    A__      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       list[dict]				   =				[]
            self.adlist.append(
                {"""value""": """""", """next_states""": [], """fail_state""": 0, """output""": []}					)

            for keyword in keywords:
                 self.add_keyword(A__					)
            self.set_fail_transitions()




       def 					a_					(					self      : List[str]	,    A__      : Optional[int]	,    A__      : Union[str, Any]					):


            """simple docstring"""







            for state in self.adlist[current_state]["next_states"]:
                 if char == self.adlist[state]["value"]:
                      return state
            return None




       def 					a_					(					self      : Optional[int]	,    A__      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				0
            for character in keyword:
                 __lowerCamelCase						:       int				   =				self.find_next_state(A__	,    A__					)
                 if next_state is None:
                      self.adlist.append(
                          {
                              """value""": character,
                              """next_states""": [],
                              """fail_state""": 0,
                              """output""": [],
                          }					)
                      self.adlist[current_state]["next_states"].append(len(self.adlist					) - 1					)
                      __lowerCamelCase						:       List[Any]				   =				len(self.adlist					) - 1
                 else:
                      __lowerCamelCase						:       Optional[int]				   =				next_state
            self.adlist[current_state]["output"].append(A__					)




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       deque				   =				deque()
            for node in self.adlist[0]["next_states"]:
                 q.append(A__					)
                 __lowerCamelCase						:       Tuple				   =				0
            while q:
                 __lowerCamelCase						:       List[Any]				   =				q.popleft()
                 for child in self.adlist[r]["next_states"]:
                      q.append(A__					)
                      __lowerCamelCase						:       Dict				   =				self.adlist[r]["""fail_state"""]
                      while (
                          self.find_next_state(A__	,    self.adlist[child]["""value"""]					) is None
                          and state != 0
                      ):
                           __lowerCamelCase						:       Tuple				   =				self.adlist[state]["""fail_state"""]
                      __lowerCamelCase						:       Optional[int]				   =				self.find_next_state(
                          A__	,    self.adlist[child]["""value"""]					)
                      if self.adlist[child]["fail_state"] is None:
                           __lowerCamelCase						:       int				   =				0
                      __lowerCamelCase						:       Union[str, Any]				   =				(
                          self.adlist[child]["""output"""]
                          + self.adlist[self.adlist[child]["""fail_state"""]]["""output"""]
                      )




       def 					a_					(					self      : Dict	,    A__      : Optional[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       dict				   =				{}  # returns a dict with keywords and list of its occurrences
            __lowerCamelCase						:       List[str]				   =				0
            for i in range(len(A__					)					):
                 while (
                     self.find_next_state(A__	,    string[i]					) is None
                     and current_state != 0
                 ):
                      __lowerCamelCase						:       int				   =				self.adlist[current_state]["""fail_state"""]
                 __lowerCamelCase						:       Optional[int]				   =				self.find_next_state(A__	,    string[i]					)
                 if next_state is None:
                      __lowerCamelCase						:       Optional[int]				   =				0
                 else:
                      __lowerCamelCase						:       List[Any]				   =				next_state
                      for key in self.adlist[current_state]["output"]:
                           if key not in result:
                                __lowerCamelCase						:       List[Any]				   =				[]
                           result[key].append(i - len(A__					) + 1					)
            return result


if __name__ == "__main__":
   import doctest

   doctest.testmod()










'''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)



