import argparse

import torch

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


logging.set_verbosity_info()
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase						)       ->       List[str]:

						'''simple docstring'''



						_snake_case						:     str				=    RoFormerConfig.from_json_file(__lowercase						)
						print(F"""Building PyTorch model from configuration: {config}"""						)
						_snake_case						:     Union[str, Any]				=    RoFormerForMaskedLM(__lowercase						)

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

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Optional[Any]     = 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.'
				)
				__SCREAMING_SNAKE_CASE						:    Optional[Any]     = parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
				import tensorflow as tf

				from transformers.activations_tf import get_tf_activation



@require_tf
class    lowercase_    (     unittest.TestCase		):



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]				)
													_snake_case						:     List[Any]				=    get_tf_activation("gelu"				)
													_snake_case						:     Any				=    get_tf_activation("gelu_10"				)

													_snake_case						:     Dict				=    gelu(lowercase_				)
													_snake_case						:     Optional[int]				=    geluaa(lowercase_				)

													_snake_case						:     Tuple				=    tf.where(y_gelu_aa < 10.0      ,				1.0      ,				0.0				)

													self.assertEqual(tf.math.reduce_max(lowercase_				).numpy().item()      ,				10.0				)
													self.assertTrue(np.allclose(y_gelu * clipped_mask      ,				y_gelu_aa * clipped_mask				)				)



							def        UpperCamelCase       (						self				):
													get_tf_activation("gelu"				)
													get_tf_activation("gelu_10"				)
													get_tf_activation("gelu_fast"				)
													get_tf_activation("gelu_new"				)
													get_tf_activation("glu"				)
													get_tf_activation("mish"				)
													get_tf_activation("quick_gelu"				)
													get_tf_activation("relu"				)
													get_tf_activation("sigmoid"				)
													get_tf_activation("silu"				)
													get_tf_activation("swish"				)
													get_tf_activation("tanh"				)
													with self.assertRaises(lowercase_				):
																			get_tf_activation("bogus"				)
													with self.assertRaises(lowercase_				):
																			get_tf_activation(lowercase_				)