





"""simple docstring"""





import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Dict      ):







      '''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__							(  SCREAMING_SNAKE_CASE					:						Any							,						SCREAMING_SNAKE_CASE					:						str      ):







      '''simple docstring'''




      for key in orig_state_dict.copy().keys():
            lowerCAmelCase  						=	orig_state_dict.pop(SCREAMING_SNAKE_CASE      )

            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__							(  SCREAMING_SNAKE_CASE					:						Any							,						SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      lowerCAmelCase  						=	torch.load(SCREAMING_SNAKE_CASE							,						map_location="""cpu"""      )["""model_state_dict"""]
      lowerCAmelCase  						=	NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	NystromformerForMaskedLM(SCREAMING_SNAKE_CASE      )

      lowerCAmelCase  						=	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__ =       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__ =       parser.parse_args()
       convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)













"""simple docstring"""





def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      lowerCAmelCase  						=	1.0
      for i in range(1							,						r + 1      ):
            ret *= (n + 1 - i) / i
      return round(SCREAMING_SNAKE_CASE      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      return choose(8 + n							,						SCREAMING_SNAKE_CASE      ) + choose(9 + n							,						SCREAMING_SNAKE_CASE      ) - 10


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      return sum(non_bouncy_exact(SCREAMING_SNAKE_CASE      ) for i in range(1							,						n + 1      )      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int = 1_00      ):







      '''simple docstring'''




      return non_bouncy_upto(SCREAMING_SNAKE_CASE      )


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






