




'''simple docstring'''
import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM
def     snake_case_  (       a__		:     List[Any]      ):



    """simple docstring"""




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

    return orig_key
def     snake_case_  (       a__		:     str   ,a__		:     List[str]      ):



    """simple docstring"""




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

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

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

    return orig_state_dict
def     snake_case_  (       a__		:     Dict   ,a__		:     Tuple   ,a__		:     int      ):



    """simple docstring"""




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

    __lowercase          =						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__":
  A							:				List[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."""
  )
  A							:				List[str]			     =	parser.parse_args()
  convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''
import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir






@dataclass
class 	SCREAMING_SNAKE_CASE:
       snake_case_			:					str






class 	SCREAMING_SNAKE_CASE(    unittest.TestCase					):

       def    snake_case__      (   self					)   -> Optional[int]:

           """simple docstring"""



           __lowercase          =						get_tests_dir("""fixtures/test_sentencepiece.model"""					)
           __lowercase          =						get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model"""					)

           __lowercase          =						FakeOriginalTokenizer(vocab_file=lowerCamelCase__					)

           with warnings.catch_warnings(record=lowerCamelCase__					) as w:
               __lowercase          =						SpmConverter(lowerCamelCase__					)
           self.assertEqual(len(lowerCamelCase__					) ,		0					)

           __lowercase          =						FakeOriginalTokenizer(vocab_file=lowerCamelCase__					)

           with warnings.catch_warnings(record=lowerCamelCase__					) as w:
               __lowercase          =						SpmConverter(lowerCamelCase__					)
           self.assertEqual(len(lowerCamelCase__					) ,		1					)

           self.assertIn(
               """The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"""
               """ which is not implemented in the fast tokenizers.""" ,		str(w[0].message					) ,		)




