
'''simple docstring'''
import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase







@require_tokenizers
class 			__A					(			unittest.TestCase      ):
 lowerCamelCase												=EsmTokenizer
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   super().setUp()
   __A		:  Tuple       				=		tempfile.mkdtemp()
   # fmt: off
   __A		:  List[str]       				=		["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
   # fmt: on
   __A		:  int       				=		os.path.join(self.tmpdirname					, VOCAB_FILES_NAMES["""vocab_file"""]							)
   with open(self.vocab_file					, """w"""					, encoding="""utf-8"""							) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)
 def 		lowercase_( self      :     Tuple					, **lowerCamelCase      :     List[str]							):



   """simple docstring"""





   return [self.get_tokenizer(**lowerCamelCase							)]
 def 		lowercase_( self      :     Optional[int]					, **lowerCamelCase      :     Tuple							):



   """simple docstring"""





   return self.tokenizer_class.from_pretrained(self.tmpdirname					, **lowerCamelCase							)
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   __A		:  str       				=		self.tokenizer_class(self.vocab_file							)

   __A		:  Optional[int]       				=		tokenizer.tokenize("""LAGVS"""							)
   self.assertListEqual(lowerCamelCase					, ["""L""", """A""", """G""", """V""", """S"""]							)
   self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase							)					, [4, 5, 6, 7, 8]							)
 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A		:  List[Any]       				=		self.tokenizer_class(self.vocab_file							)

   __A		:  str       				=		"""LAGVS"""
   self.assertListEqual(tokenizer.encode(lowerCamelCase							)					, [0, 4, 5, 6, 7, 8, 2]							)
 def 		lowercase_( self      :     Dict							):



   """simple docstring"""





   __A		:  str       				=		self.tokenizer_class(self.vocab_file							)

   __A		:  Optional[Any]       				=		["""LAGVS""", """WCB"""]
   __A		:  Union[str, Any]       				=		tokenizer(lowerCamelCase					, padding=lowerCamelCase							)["""input_ids"""]

   self.assertListEqual(lowerCamelCase					, [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]							)
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   __A		:  Any       				=		self.tokenizer_class(self.vocab_file							)

   __A		:  Any       				=		["""LAGVS""", """WCB"""]
   __A		:  Tuple       				=		tokenizer(lowerCamelCase					, padding=lowerCamelCase							)["""input_ids"""]

   self.assertListEqual(lowerCamelCase					, [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]							)






 def 		lowercase_( self      :     str							):



   """simple docstring"""





   __A		:  Optional[int]       				=		self.get_tokenizers(fast=lowerCamelCase							)
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"							):
       __A		:  str       				=		"""<unk>"""
       __A		:  int       				=		"""<mask>"""

       __A		:  List[str]       				=		tokenizer.tokenize(lowerCamelCase							)
       __A		:  Optional[Any]       				=		tokenizer.tokenize(lowerCamelCase							)

       self.assertEqual(len(lowerCamelCase							)					, 1							)
       self.assertEqual(len(lowerCamelCase							)					, 1							)
       self.assertEqual(token_a[0]					, lowerCamelCase							)
       self.assertEqual(token_a[0]					, lowerCamelCase							)










import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







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

  return orig_key







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[int]:
  for key in orig_state_dict.copy().keys():
    __snake_case:		str					    =		orig_state_dict.pop(SCREAMING_SNAKE_CASE__)

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

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

  return orig_state_dict







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[Any]:
  __snake_case:		int					    =		torch.load(SCREAMING_SNAKE_CASE__   ,   map_location="""cpu""")["""model_state_dict"""]
  __snake_case:		List[Any]					    =		NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE__)
  __snake_case:		Optional[int]					    =		NystromformerForMaskedLM(SCREAMING_SNAKE_CASE__)

  __snake_case:		Dict					    =		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__":
      __UpperCAmelCase			:  str										=				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."
      )
      __UpperCAmelCase			:  Optional[Any]										=				parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






