


'''simple docstring'''







import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput
@dataclass
class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      42
    __UpperCAmelCase       =      None
    __UpperCAmelCase       =      None

class    lowercase_	(       unittest.TestCase  ):


    def   __a				(	self       ):
          UpperCamelCase__      =							ModelOutputTest(a=30       )
          self.assertEqual(x.a    ,   30       )
          self.assertIsNone(x.b       )
          self.assertIsNone(x.c       )
          with self.assertRaises(a       ):
                UpperCamelCase__      =							x.d


    def   __a				(	self       ):
          UpperCamelCase__      =							ModelOutputTest(a=30    ,   b=10       )
          self.assertEqual(x[0]    ,   30       )
          self.assertEqual(x[1]    ,   10       )
          self.assertEqual(x[:2]    ,   (30, 10)       )
          self.assertEqual(x[:]    ,   (30, 10)       )

          UpperCamelCase__      =							ModelOutputTest(a=30    ,   c=10       )
          self.assertEqual(x[0]    ,   30       )
          self.assertEqual(x[1]    ,   10       )
          self.assertEqual(x[:2]    ,   (30, 10)       )
          self.assertEqual(x[:]    ,   (30, 10)       )


    def   __a				(	self       ):
          UpperCamelCase__      =							ModelOutputTest(a=30    ,   b=10       )
          self.assertEqual(x["a"]    ,   30       )
          self.assertEqual(x["b"]    ,   10       )
          with self.assertRaises(a       ):
                UpperCamelCase__      =							x["c"]

          UpperCamelCase__      =							ModelOutputTest(a=30    ,   c=10       )
          self.assertEqual(x["a"]    ,   30       )
          self.assertEqual(x["c"]    ,   10       )
          with self.assertRaises(a       ):
                UpperCamelCase__      =							x["b"]


    def   __a				(	self       ):
          UpperCamelCase__      =							ModelOutputTest(a=30       )
          self.assertEqual(list(x.keys()       )    ,   ["a"]       )
          self.assertEqual(list(x.values()       )    ,   [30]       )
          self.assertEqual(list(x.items()       )    ,   [("a", 30)]       )
          self.assertEqual(list(a       )    ,   ["a"]       )

          UpperCamelCase__      =							ModelOutputTest(a=30    ,   b=10       )
          self.assertEqual(list(x.keys()       )    ,   ["a", "b"]       )
          self.assertEqual(list(x.values()       )    ,   [30, 10]       )
          self.assertEqual(list(x.items()       )    ,   [("a", 30), ("b", 10)]       )
          self.assertEqual(list(a       )    ,   ["a", "b"]       )

          UpperCamelCase__      =							ModelOutputTest(a=30    ,   c=10       )
          self.assertEqual(list(x.keys()       )    ,   ["a", "c"]       )
          self.assertEqual(list(x.values()       )    ,   [30, 10]       )
          self.assertEqual(list(x.items()       )    ,   [("a", 30), ("c", 10)]       )
          self.assertEqual(list(a       )    ,   ["a", "c"]       )

          with self.assertRaises(a       ):
                UpperCamelCase__      =							x.update({"d": 20}       )
          with self.assertRaises(a       ):
                del x["a"]
          with self.assertRaises(a       ):
                UpperCamelCase__      =							x.pop("a"       )
          with self.assertRaises(a       ):
                UpperCamelCase__      =							x.setdefault("d"    ,   32       )


    def   __a				(	self       ):
          UpperCamelCase__      =							ModelOutputTest(a=30       )
          UpperCamelCase__      =							10
          self.assertEqual(x.a    ,   10       )
          self.assertEqual(x["a"]    ,   10       )


    def   __a				(	self       ):
          UpperCamelCase__      =							ModelOutputTest(a=30       )
          UpperCamelCase__      =							10
          self.assertEqual(x.a    ,   10       )
          self.assertEqual(x["a"]    ,   10       )


    def   __a				(	self       ):
          UpperCamelCase__      =							ModelOutputTest({"a": 30, "b": 10}       )
          self.assertEqual(list(x.keys()       )    ,   ["a", "b"]       )
          self.assertEqual(x.a    ,   30       )
          self.assertEqual(x.b    ,   10       )


    def   __a				(	self       ):
          UpperCamelCase__      =							ModelOutputTest([("a", 30), ("b", 10)]       )
          self.assertEqual(list(x.keys()       )    ,   ["a", "b"]       )
          self.assertEqual(x.a    ,   30       )
          self.assertEqual(x.b    ,   10       )

          with self.assertRaises(a       ):
                UpperCamelCase__      =							ModelOutputTest([("a", 30), (10, 10)]       )

          UpperCamelCase__      =							ModelOutputTest(a=(30, 30)       )
          self.assertEqual(list(x.keys()       )    ,   ["a"]       )
          self.assertEqual(x.a    ,   (30, 30)       )









'''simple docstring'''







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							RoFormerConfig.from_json_file(__A       )
      print(F'''Building PyTorch model from configuration: {config}'''       )
      UpperCamelCase__      =							RoFormerForMaskedLM(__A       )

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(__A ,     __A ,     __A       )

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
      torch.save(model.state_dict() ,     __A ,     _use_new_zipfile_serialization=__A       )


if __name__ == "__main__":
  a__    :   str								=			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(
      '--bert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained BERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  a__    :   Dict								=			parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





