






"""simple docstring"""







import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput


@dataclass
class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase				       =					42
						__lowerCamelCase				       =					None
						__lowerCamelCase				       =					None


class     __UpperCAmelCase(   unittest.TestCase							):




						"""simple docstring"""




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Any=       ModelOutputTest(a=30			)
												self.assertEqual(x.a       ,  30			)
												self.assertIsNone(x.b			)
												self.assertIsNone(x.c			)
												with self.assertRaises(snake_case__			):
																		lowercase__				:      Any=       x.d




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[Any]=       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)			)

												lowercase__				:      List[Any]=       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 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[Any]=       ModelOutputTest(a=30       ,  b=10			)
												self.assertEqual(x["a"]       ,  30			)
												self.assertEqual(x["b"]       ,  10			)
												with self.assertRaises(snake_case__			):
																		lowercase__				:      List[str]=       x["c"]

												lowercase__				:      List[str]=       ModelOutputTest(a=30       ,  c=10			)
												self.assertEqual(x["a"]       ,  30			)
												self.assertEqual(x["c"]       ,  10			)
												with self.assertRaises(snake_case__			):
																		lowercase__				:      Any=       x["b"]




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      str=       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(snake_case__			)       ,  ["a"]			)

												lowercase__				:      Union[str, Any]=       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(snake_case__			)       ,  ["a", "b"]			)

												lowercase__				:      Any=       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(snake_case__			)       ,  ["a", "c"]			)

												with self.assertRaises(snake_case__			):
																		lowercase__				:      List[Any]=       x.update({"d": 20}			)
												with self.assertRaises(snake_case__			):
																		del x["a"]
												with self.assertRaises(snake_case__			):
																		lowercase__				:      List[Any]=       x.pop("a"			)
												with self.assertRaises(snake_case__			):
																		lowercase__				:      Dict=       x.setdefault("d"       ,  32			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[str]=       ModelOutputTest(a=30			)
												lowercase__				:      List[Any]=       10
												self.assertEqual(x.a       ,  10			)
												self.assertEqual(x["a"]       ,  10			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[Any]=       ModelOutputTest(a=30			)
												lowercase__				:      List[str]=       10
												self.assertEqual(x.a       ,  10			)
												self.assertEqual(x["a"]       ,  10			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[Any]=       ModelOutputTest({"a": 30, "b": 10}			)
												self.assertEqual(list(x.keys()			)       ,  ["a", "b"]			)
												self.assertEqual(x.a       ,  30			)
												self.assertEqual(x.b       ,  10			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       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(snake_case__			):
																		lowercase__				:      str=       ModelOutputTest([("a", 30), (10, 10)]			)

												lowercase__				:      Any=       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 						lowercase__(A					,		A					,		A						)				->int:




						"""simple docstring"""



						lowercase__				:      List[str]=       RoFormerConfig.from_json_file(A						)
						print(f'''Building PyTorch model from configuration: {config}'''						)
						lowercase__				:      Tuple=       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 :				List[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 :				str      					=		parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






