



'''simple docstring'''







import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		if "model" in orig_key:
				SCREAMING_SNAKE_CASE_						:Dict										=      orig_key.replace('model.' ,		''     )
		if "norm1" in orig_key:
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      orig_key.replace('norm1' ,		'attention.output.LayerNorm'     )
		if "norm2" in orig_key:
				SCREAMING_SNAKE_CASE_						:Tuple										=      orig_key.replace('norm2' ,		'output.LayerNorm'     )
		if "norm" in orig_key:
				SCREAMING_SNAKE_CASE_						:str										=      orig_key.replace('norm' ,		'LayerNorm'     )
		if "transformer" in orig_key:
				SCREAMING_SNAKE_CASE_						:Tuple										=      orig_key.split('.'     )[0].split('_'     )[-1]
				SCREAMING_SNAKE_CASE_						:Tuple										=      orig_key.replace(F'transformer_{layer_num}' ,		F'encoder.layer.{layer_num}'     )
		if "mha.attn" in orig_key:
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      orig_key.replace('mha.attn' ,		'attention.self'     )
		if "mha" in orig_key:
				SCREAMING_SNAKE_CASE_						:List[str]										=      orig_key.replace('mha' ,		'attention'     )
		if "W_q" in orig_key:
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      orig_key.replace('W_q' ,		'self.query'     )
		if "W_k" in orig_key:
				SCREAMING_SNAKE_CASE_						:List[Any]										=      orig_key.replace('W_k' ,		'self.key'     )
		if "W_v" in orig_key:
				SCREAMING_SNAKE_CASE_						:List[str]										=      orig_key.replace('W_v' ,		'self.value'     )
		if "ff1" in orig_key:
				SCREAMING_SNAKE_CASE_						:Tuple										=      orig_key.replace('ff1' ,		'intermediate.dense'     )
		if "ff2" in orig_key:
				SCREAMING_SNAKE_CASE_						:str										=      orig_key.replace('ff2' ,		'output.dense'     )
		if "ff" in orig_key:
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      orig_key.replace('ff' ,		'output.dense'     )
		if "mlm_class" in orig_key:
				SCREAMING_SNAKE_CASE_						:str										=      orig_key.replace('mlm.mlm_class' ,		'cls.predictions.decoder'     )
		if "mlm" in orig_key:
				SCREAMING_SNAKE_CASE_						:Any										=      orig_key.replace('mlm' ,		'cls.predictions.transform'     )
		if "cls" not in orig_key:
				SCREAMING_SNAKE_CASE_						:str										=      'nystromformer.' + orig_key

		return orig_key







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		for key in orig_state_dict.copy().keys():
				SCREAMING_SNAKE_CASE_						:Dict										=      orig_state_dict.pop(SCREAMING_SNAKE_CASE     )

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

		SCREAMING_SNAKE_CASE_						:Tuple										=      orig_state_dict['cls.predictions.decoder.bias']
		SCREAMING_SNAKE_CASE_						:Dict										=      (
		    torch.arange(config.max_position_embeddings     ).expand((1, -1)     ) + 2
		)

		return orig_state_dict







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:List[str]										=      torch.load(SCREAMING_SNAKE_CASE ,		map_location='cpu'     )['model_state_dict']
		SCREAMING_SNAKE_CASE_						:Any										=      NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      NystromformerForMaskedLM(SCREAMING_SNAKE_CASE     )

		SCREAMING_SNAKE_CASE_						:str										=      convert_checkpoint_helper(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )

		model.load_state_dict(SCREAMING_SNAKE_CASE     )
		model.eval()
		model.save_pretrained(SCREAMING_SNAKE_CASE     )

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__     : int				=   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__     : List[str]				=   parser.parse_args()
							convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase     =       logging.get_logger(__name__)







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 __snake_case				=    ["input_ids", "attention_mask"]



 def __init__(      self						, _UpperCAmelCase="[PAD]"						, _UpperCAmelCase="[BOS]"						, _UpperCAmelCase="[EOS]"						, _UpperCAmelCase="[MASK]"						, _UpperCAmelCase="[CLS]"						, _UpperCAmelCase="[SEP]"						, _UpperCAmelCase=20_48						, **_UpperCAmelCase						, ):
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else pad_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else bos_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else eos_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else mask_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else cls_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else sep_token

       super().__init__(
           pad_token=_UpperCAmelCase						, bos_token=_UpperCAmelCase						, eos_token=_UpperCAmelCase						, mask_token=_UpperCAmelCase						, cls_token=_UpperCAmelCase						, sep_token=_UpperCAmelCase						, model_max_length=_UpperCAmelCase						, **_UpperCAmelCase						, )

       snake_case_			= 2**8  # utf is 8 bits

       # define special tokens dict
       snake_case_			= {
           self.pad_token: 0,
           self.bos_token: 1,
           self.eos_token: 2,
           self.mask_token: 3,
           self.cls_token: 4,
           self.sep_token: 5,
       }
       snake_case_			= len(self.special_tokens_encoder   )
       snake_case_			= {v: k for k, v in self.special_tokens_encoder.items()}



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.special_tokens_encoder.copy()
       vocab.update(self.added_tokens_encoder   )
       for i in range(self._utf_vocab_size   ):
             snake_case_			= chr(_UpperCAmelCase   )
             snake_case_			= i + len(self.special_tokens_encoder   )
       return vocab



 @property
 def 		UpperCamelCase__	(      self   ):
       return self._utf_vocab_size + self._num_special_tokens



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = None						, _UpperCAmelCase = False   ):
       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_UpperCAmelCase						, token_ids_a=_UpperCAmelCase						, already_has_special_tokens=_UpperCAmelCase   )

       # normal case: some special tokens
       if token_ids_a is None:
             return [1] + [0] * len(_UpperCAmelCase   ) + [1]
       return [1] + ([0] * len(_UpperCAmelCase   )) + [1] + ([0] * len(_UpperCAmelCase   )) + [1]



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = None   ):
       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       else:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       snake_case_			= [chr(_UpperCAmelCase   ) for i in text.encode('''utf-8'''   )]
       return tokens



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       if token in self.special_tokens_encoder:
             snake_case_			= self.special_tokens_encoder[token]
       elif token in self.added_tokens_encoder:
             snake_case_			= self.added_tokens_encoder[token]
       elif len(_UpperCAmelCase   ) != 1:
             snake_case_			= self.unk_token_id
       else:
             snake_case_			= ord(_UpperCAmelCase   ) + self._num_special_tokens
       return token_id



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       if index in self.special_tokens_decoder:
             snake_case_			= self.special_tokens_decoder[index]
       elif index in self.added_tokens_decoder:
             snake_case_			= self.added_tokens_decoder[index]
       else:
             snake_case_			= chr(index - self._num_special_tokens   )
       return token



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       snake_case_			= b''''''
       for token in tokens:
             if token in self.special_tokens_decoder:
                   snake_case_			= self.special_tokens_decoder[token].encode('''utf-8'''   )
             elif token in self.added_tokens_decoder:
                   snake_case_			= self.special_tokens_decoder[token].encode('''utf-8'''   )
             elif token in self.special_tokens_encoder:
                   snake_case_			= token.encode('''utf-8'''   )
             elif token in self.added_tokens_encoder:
                   snake_case_			= token.encode('''utf-8'''   )
             else:
                   snake_case_			= bytes([ord(_UpperCAmelCase   )]   )
             bstring += tok_string
       snake_case_			= bstring.decode('''utf-8'''						, errors='''replace'''   )
       return string





 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = None   ):
       return ()