

"""simple docstring"""




import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM



def 						UpperCAmelCase__ (					A__ )   ->   str:




   """simple docstring"""
   if "model" in orig_key:
      lowerCamelCase__						       =      orig_key.replace("model."						,     "" )
   if "norm1" in orig_key:
      lowerCamelCase__						       =      orig_key.replace("norm1"						,     "attention.output.LayerNorm" )
   if "norm2" in orig_key:
      lowerCamelCase__						       =      orig_key.replace("norm2"						,     "output.LayerNorm" )
   if "norm" in orig_key:
      lowerCamelCase__						       =      orig_key.replace("norm"						,     "LayerNorm" )
   if "transformer" in orig_key:
      lowerCamelCase__						       =      orig_key.split("." )[0].split("_" )[-1]
      lowerCamelCase__						       =      orig_key.replace(f'transformer_{layer_num}'						,     f'encoder.layer.{layer_num}' )
   if "mha.attn" in orig_key:
      lowerCamelCase__						       =      orig_key.replace("mha.attn"						,     "attention.self" )
   if "mha" in orig_key:
      lowerCamelCase__						       =      orig_key.replace("mha"						,     "attention" )
   if "W_q" in orig_key:
      lowerCamelCase__						       =      orig_key.replace("W_q"						,     "self.query" )
   if "W_k" in orig_key:
      lowerCamelCase__						       =      orig_key.replace("W_k"						,     "self.key" )
   if "W_v" in orig_key:
      lowerCamelCase__						       =      orig_key.replace("W_v"						,     "self.value" )
   if "ff1" in orig_key:
      lowerCamelCase__						       =      orig_key.replace("ff1"						,     "intermediate.dense" )
   if "ff2" in orig_key:
      lowerCamelCase__						       =      orig_key.replace("ff2"						,     "output.dense" )
   if "ff" in orig_key:
      lowerCamelCase__						       =      orig_key.replace("ff"						,     "output.dense" )
   if "mlm_class" in orig_key:
      lowerCamelCase__						       =      orig_key.replace("mlm.mlm_class"						,     "cls.predictions.decoder" )
   if "mlm" in orig_key:
      lowerCamelCase__						       =      orig_key.replace("mlm"						,     "cls.predictions.transform" )
   if "cls" not in orig_key:
      lowerCamelCase__						       =      "nystromformer." + orig_key

   return orig_key



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   int:




   """simple docstring"""
   for key in orig_state_dict.copy().keys():
      lowerCamelCase__						       =      orig_state_dict.pop(A__ )

      if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
         continue
      else:
         lowerCamelCase__						       =      val

   lowerCamelCase__						       =      orig_state_dict["cls.predictions.decoder.bias"]
   lowerCamelCase__						       =      (
       torch.arange(config.max_position_embeddings ).expand((1, -1) ) + 2
   )

   return orig_state_dict



def 						UpperCAmelCase__ (					A__						,     A__						,     A__ )   ->   int:




   """simple docstring"""
   lowerCamelCase__						       =      torch.load(A__						,     map_location="cpu" )["model_state_dict"]
   lowerCamelCase__						       =      NystromformerConfig.from_json_file(A__ )
   lowerCamelCase__						       =      NystromformerForMaskedLM(A__ )

   lowerCamelCase__						       =      convert_checkpoint_helper(A__						,     A__ )

   model.load_state_dict(A__ )
   model.eval()
   model.save_pretrained(A__ )

   print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}' )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_  :      Any							=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Nystromformer pytorch checkpoint.'''
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help='''The json file for Nystromformer model config.''',
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     SCREAMING_SNAKE_CASE_  :      int							=      parser.parse_args()
     convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


SCREAMING_SNAKE_CASE_  :      str							=      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_  :      List[Any]							=      {'''vocab_file''': '''vocab.txt'''}

SCREAMING_SNAKE_CASE_  :      int							=      {
    '''vocab_file''': {
        '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt''',
        '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt''',
        '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt''',
        '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt''',
    }
}

SCREAMING_SNAKE_CASE_  :      Union[str, Any]							=      {
    '''tau/splinter-base''': 512,
    '''tau/splinter-base-qass''': 512,
    '''tau/splinter-large''': 512,
    '''tau/splinter-large-qass''': 512,
}

SCREAMING_SNAKE_CASE_  :      List[Any]							=      {
    '''tau/splinter-base''': {'''do_lower_case''': False},
    '''tau/splinter-base-qass''': {'''do_lower_case''': False},
    '''tau/splinter-large''': {'''do_lower_case''': False},
    '''tau/splinter-large-qass''': {'''do_lower_case''': False},
}


class 		_A							(      __a       ):
      __a             =						VOCAB_FILES_NAMES
      __a             =						PRETRAINED_VOCAB_FILES_MAP
      __a             =						PRETRAINED_INIT_CONFIGURATION
      __a             =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __a             =						SplinterTokenizer





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__=None     ,    SCREAMING_SNAKE_CASE__=None     ,    SCREAMING_SNAKE_CASE__=True     ,    SCREAMING_SNAKE_CASE__="[UNK]"     ,    SCREAMING_SNAKE_CASE__="[SEP]"     ,    SCREAMING_SNAKE_CASE__="[PAD]"     ,    SCREAMING_SNAKE_CASE__="[CLS]"     ,    SCREAMING_SNAKE_CASE__="[MASK]"     ,    SCREAMING_SNAKE_CASE__="[QUESTION]"     ,    SCREAMING_SNAKE_CASE__=True     ,    SCREAMING_SNAKE_CASE__=None     ,    **SCREAMING_SNAKE_CASE__     ,    ) ->       Optional[Any]:
         super().__init__(
             SCREAMING_SNAKE_CASE__     ,    tokenizer_file=SCREAMING_SNAKE_CASE__     ,    do_lower_case=SCREAMING_SNAKE_CASE__     ,    unk_token=SCREAMING_SNAKE_CASE__     ,    sep_token=SCREAMING_SNAKE_CASE__     ,    pad_token=SCREAMING_SNAKE_CASE__     ,    cls_token=SCREAMING_SNAKE_CASE__     ,    mask_token=SCREAMING_SNAKE_CASE__     ,    tokenize_chinese_chars=SCREAMING_SNAKE_CASE__     ,    strip_accents=SCREAMING_SNAKE_CASE__     ,    additional_special_tokens=(question_token,)     ,    **SCREAMING_SNAKE_CASE__     ,    )

         lowerCamelCase__						       =      json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
         if (
             pre_tok_state.get("lowercase"     ,    SCREAMING_SNAKE_CASE__		) != do_lower_case
             or pre_tok_state.get("strip_accents"     ,    SCREAMING_SNAKE_CASE__		) != strip_accents
         ):
            lowerCamelCase__						       =      getattr(SCREAMING_SNAKE_CASE__     ,    pre_tok_state.pop("type"		)		)
            lowerCamelCase__						       =      do_lower_case
            lowerCamelCase__						       =      strip_accents
            lowerCamelCase__						       =      pre_tok_class(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						       =      do_lower_case





      @property
      def        _lowerCamelCase	(	self		) ->       Optional[Any]:
         return self.convert_tokens_to_ids(self.question_token		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None		) ->       List[int]:
         if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         lowerCamelCase__						       =      [self.cls_token_id]
         lowerCamelCase__						       =      [self.sep_token_id]
         lowerCamelCase__						       =      [self.question_token_id] + [self.convert_tokens_to_ids("."		)]
         if self.padding_side == "right":
            # Input is question-then-context
            return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
         else:
            # Input is context-then-question
            return cls + token_ids_a + sep + token_ids_a + question_suffix + sep





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None		) ->       List[int]:
         lowerCamelCase__						       =      [self.sep_token_id]
         lowerCamelCase__						       =      [self.cls_token_id]
         lowerCamelCase__						       =      [self.question_token_id] + [self.convert_tokens_to_ids("."		)]
         if token_ids_a is None:
            return len(cls + token_ids_a + sep		) * [0]

         if self.padding_side == "right":
            # Input is question-then-context
            return len(cls + token_ids_a + question_suffix + sep		) * [0] + len(token_ids_a + sep		) * [1]
         else:
            # Input is context-then-question
            return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + question_suffix + sep		) * [1]





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None		) ->       Tuple[str]:
         lowerCamelCase__						       =      self._tokenizer.model.save(SCREAMING_SNAKE_CASE__     ,    name=SCREAMING_SNAKE_CASE__		)
         return tuple(SCREAMING_SNAKE_CASE__		)




