




import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
				lowercase_		=							[]

				lowercase_		=							os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
				lowercase_		=							glob.glob(pattern)
				# TODO: deal with TF/Flax too
				lowercase_		=							[
				    x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
				]

				for test_file in test_files:
								lowercase_		=							get_tester_classes(test_file)
								for tester_class in tester_classes:
												# A few tester classes don't have `parent` parameter in `__init__`.
												# TODO: deal this better
												try:
																lowercase_		=							tester_class(parent=None)
												except Exception:
																continue
												if hasattr(tester, '''get_config'''):
																lowercase_		=							tester.get_config()
																for k, v in config.to_dict().items():
																				if isinstance(v, int):
																								lowercase_		=							None
																								if k in ["vocab_size"]:
																												lowercase_		=							100
																								elif k in ["max_position_embeddings"]:
																												lowercase_		=							128
																								elif k in ["hidden_size", "d_model"]:
																												lowercase_		=							40
																								elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																												lowercase_		=							5
																								if target is not None and v > target:
																												failures.append(
																												    f"{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"
																												    f" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"
																												    f" than {target}."
																												)

				if len(failures) > 0:
								raise Exception(f"There were {len(failures)} failures:\n" + '''\n'''.join(failures))











"""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)






