



"""simple docstring"""





import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()
def    a__	(      lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__      ):
      UpperCAmelCase_			 =							ElectraConfig.from_json_file(lowerCAmelCase_      )
      print(f"""Building PyTorch model from configuration: {config}"""      )

      if discriminator_or_generator == "discriminator":
            UpperCAmelCase_			 =							ElectraForPreTraining(lowerCAmelCase_      )
      elif discriminator_or_generator == "generator":
            UpperCAmelCase_			 =							ElectraForMaskedLM(lowerCAmelCase_      )
      else:
            raise ValueError("The discriminator_or_generator argument should be either 'discriminator' or 'generator'"      )

      # Load weights from tf checkpoint
      load_tf_weights_in_electra(
          lowerCAmelCase_	,			lowerCAmelCase_	,			lowerCAmelCase_	,			discriminator_or_generator=lowerCAmelCase_      )

      # Save pytorch-model
      print(f"""Save PyTorch model to {pytorch_dump_path}"""      )
      torch.save(model.state_dict()	,			lowerCAmelCase_      )


if __name__ == "__main__":
  lowerCamelCase          =						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  parser.add_argument(
      """--discriminator_or_generator""",
      default=None,
      type=str,
      required=True,
      help=(
          """Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or """
          """\'generator\'."""
      ),
  )
  lowerCamelCase          =						parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
  )










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  __lowerCamelCase       (    unittest.TestCase    ):




    """simple docstring"""

    snake_case__					=      EsmTokenizer


    def    a      (       self     :     str   )      ->       Tuple:
         super().setUp()
         lowerCAmelCase__		= tempfile.mkdtemp()
         # fmt: off
         lowerCAmelCase__		= ["<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
         lowerCAmelCase__		= 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    a      (       self     :     Dict   ,	**SCREAMING_SNAKE_CASE__     :     Optional[Any]   )      ->       List[PreTrainedTokenizerBase]:
         return [self.get_tokenizer(**SCREAMING_SNAKE_CASE__   )]


    def    a      (       self     :     List[str]   ,	**SCREAMING_SNAKE_CASE__     :     str   )      ->       PreTrainedTokenizer:
         return self.tokenizer_class.from_pretrained(self.tmpdirname   ,	**SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     str   )      ->       Tuple:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= tokenizer.tokenize("LAGVS"   )
         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	["L", "A", "G", "V", "S"]   )
         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__   )   ,	[4, 5, 6, 7, 8]   )


    def    a      (       self     :     int   )      ->       Union[str, Any]:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= "LAGVS"
         self.assertListEqual(tokenizer.encode(SCREAMING_SNAKE_CASE__   )   ,	[0, 4, 5, 6, 7, 8, 2]   )


    def    a      (       self     :     Union[str, Any]   )      ->       str:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= ["LAGVS", "WCB"]
         lowerCAmelCase__		= tokenizer(SCREAMING_SNAKE_CASE__   ,	padding=SCREAMING_SNAKE_CASE__   )["input_ids"]

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


    def    a      (       self     :     Any   )      ->       Union[str, Any]:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= ["LAGVS", "WCB"]
         lowerCAmelCase__		= tokenizer(SCREAMING_SNAKE_CASE__   ,	padding=SCREAMING_SNAKE_CASE__   )["input_ids"]

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


    def    a      (       self     :     str   )      ->       Optional[Any]:
         lowerCAmelCase__		= self.get_tokenizers(fast=SCREAMING_SNAKE_CASE__   )
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'   ):
                   lowerCAmelCase__		= "<unk>"
                   lowerCAmelCase__		= "<mask>"

                   lowerCAmelCase__		= tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )
                   lowerCAmelCase__		= tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )

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