



import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


def   _A       (    SCREAMING_SNAKE_CASE__  :     Any	):

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

    return orig_key


def   _A       (    SCREAMING_SNAKE_CASE__  :     Optional[Any]					,  SCREAMING_SNAKE_CASE__  :     str	):

    for key in orig_state_dict.copy().keys():
        UpperCamelCase	:Tuple     							=       orig_state_dict.pop(__lowercase	)

        if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
            continue
        else:
            UpperCamelCase	:Union[str, Any]     							=       val

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

    return orig_state_dict


def   _A       (    SCREAMING_SNAKE_CASE__  :     Union[str, Any]					,  SCREAMING_SNAKE_CASE__  :     Union[str, Any]					,  SCREAMING_SNAKE_CASE__  :     Union[str, Any]	):
    UpperCamelCase	:Dict     							=       torch.load(__lowercase					,  map_location='''cpu'''	)['''model_state_dict''']
    UpperCamelCase	:Union[str, Any]     							=       NystromformerConfig.from_json_file(__lowercase	)
    UpperCamelCase	:Union[str, Any]     							=       NystromformerForMaskedLM(__lowercase	)

    UpperCamelCase	:Dict     							=       convert_checkpoint_helper(__lowercase					,  __lowercase	)

    model.load_state_dict(__lowercase	)
    model.eval()
    model.save_pretrained(__lowercase	)

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


if __name__ == "__main__":
 __snake_case							 =  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."""
 )
 __snake_case							 =  parser.parse_args()
 convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)













def   _A       (    SCREAMING_SNAKE_CASE__  :     int = 998001	):

    # fetches the next number
    for number in range(n - 1					,  9999					,  -1	):
        UpperCamelCase	:Union[str, Any]     							=       str(SCREAMING_SNAKE_CASE__	)

        # checks whether 'str_number' is a palindrome.
        if str_number == str_number[::-1]:
            UpperCamelCase	:Dict     							=       999

            # if 'number' is a product of two 3-digit numbers
            # then number is the answer otherwise fetch next number.
            while divisor != 99:
                if (number % divisor == 0) and (len(str(number // divisor	)	) == 3.0):
                    return number
                divisor -= 1
    raise ValueError('''That number is larger than our acceptable range.'''	)


if __name__ == "__main__":
 print(f'''{solution() = }''')





