






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  List[str]:





						"""simple docstring"""



						_UpperCAmelCase									=			('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

						_UpperCAmelCase									=			(
						    ('layer.', 'layer_'),
						    ('word_embeddings.weight', 'word_embeddings'),
						    ('position_embeddings.weight', 'position_embeddings'),
						    ('token_type_embeddings.weight', 'token_type_embeddings'),
						    ('.', '/'),
						    ('LayerNorm/weight', 'LayerNorm/gamma'),
						    ('LayerNorm/bias', 'LayerNorm/beta'),
						    ('weight', 'kernel'),
						)

						if not os.path.isdir(__lowerCAmelCase	):
												os.makedirs(__lowerCAmelCase	)

						_UpperCAmelCase									=			model.state_dict()

						def to_tf_var_name(__lowerCAmelCase	):
												for patt, repl in iter(__lowerCAmelCase	):
																		_UpperCAmelCase									=			name.replace(__lowerCAmelCase ,		__lowerCAmelCase	)
												return F"""bert/{name}"""

						def create_tf_var(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	):
												_UpperCAmelCase									=			tf.dtypes.as_dtype(tensor.dtype	)
												_UpperCAmelCase									=			tf.get_variable(dtype=__lowerCAmelCase ,		shape=tensor.shape ,		name=__lowerCAmelCase ,		initializer=tf.zeros_initializer()	)
												session.run(tf.variables_initializer([tf_var]	)	)
												session.run(__lowerCAmelCase	)
												return tf_var

						tf.reset_default_graph()
						with tf.Session() as session:
												for var_name in state_dict:
																		_UpperCAmelCase									=			to_tf_var_name(__lowerCAmelCase	)
																		_UpperCAmelCase									=			state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose	):
																								_UpperCAmelCase									=			torch_tensor.T
																		_UpperCAmelCase									=			create_tf_var(tensor=__lowerCAmelCase ,		name=__lowerCAmelCase ,		session=__lowerCAmelCase	)
																		tf.keras.backend.set_value(__lowerCAmelCase ,		__lowerCAmelCase	)
																		_UpperCAmelCase									=			session.run(__lowerCAmelCase	)
																		print(F"""Successfully created {tf_name}: {np.allclose(__lowerCAmelCase ,		__lowerCAmelCase	)}"""	)

												_UpperCAmelCase									=			tf.train.Saver(tf.trainable_variables()	)
												saver.save(__lowerCAmelCase ,		os.path.join(__lowerCAmelCase ,		model_name.replace('-' ,		'_'	) + '.ckpt'	)	)


def __A    (      __lowerCAmelCase=None	)->  Any:





						"""simple docstring"""



						_UpperCAmelCase									=			argparse.ArgumentParser()
						parser.add_argument('--model_name' ,		type=__lowerCAmelCase ,		required=__lowerCAmelCase ,		help='model name e.g. bert-base-uncased'	)
						parser.add_argument(
						    '--cache_dir' ,		type=__lowerCAmelCase ,		default=__lowerCAmelCase ,		required=__lowerCAmelCase ,		help='Directory containing pytorch model'	)
						parser.add_argument('--pytorch_model_path' ,		type=__lowerCAmelCase ,		required=__lowerCAmelCase ,		help='/path/to/<pytorch-model-name>.bin'	)
						parser.add_argument('--tf_cache_dir' ,		type=__lowerCAmelCase ,		required=__lowerCAmelCase ,		help='Directory in which to save tensorflow model'	)
						_UpperCAmelCase									=			parser.parse_args(__lowerCAmelCase	)

						_UpperCAmelCase									=			BertModel.from_pretrained(
						    pretrained_model_name_or_path=args.model_name ,		state_dict=torch.load(args.pytorch_model_path	) ,		cache_dir=args.cache_dir ,		)

						convert_pytorch_checkpoint_to_tf(model=__lowerCAmelCase ,		ckpt_dir=args.tf_cache_dir ,		model_name=args.model_name	)


if __name__ == "__main__":
		main()








import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory


def __A    (      )->  Tuple:





						"""simple docstring"""



						raise RuntimeError('CUDA out of memory.'	)





class __lowerCamelCase (      nn.Module):





	"""simple docstring"""






	def __init__(							self    ):
							"""simple docstring"""







							super().__init__()
							_UpperCAmelCase									=			nn.Linear(3       ,    4    )
							_UpperCAmelCase									=			nn.BatchNormad(4    )
							_UpperCAmelCase									=			nn.Linear(4       ,    5    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return self.lineara(self.batchnorm(self.lineara(UpperCAmelCase    )    )    )





class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]

							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase    ):
													nonlocal batch_sizes
													batch_sizes.append(UpperCAmelCase    )
													if batch_size != 8:
																			raise_fake_out_of_memory()

							mock_training_loop_function()
							self.assertListEqual(UpperCAmelCase       ,    [128, 64, 32, 16, 8]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]

							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase       ,    UpperCAmelCase    ):
													nonlocal batch_sizes
													batch_sizes.append(UpperCAmelCase    )
													if batch_size != 8:
																			raise_fake_out_of_memory()
													return batch_size, arga

							_UpperCAmelCase					, _UpperCAmelCase									=			mock_training_loop_function('hello'    )
							self.assertListEqual(UpperCAmelCase       ,    [128, 64, 32, 16, 8]    )
							self.assertListEqual([bs, arga]       ,    [8, 'hello']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=0    )
							def mock_training_loop_function(UpperCAmelCase    ):
													pass

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('No executable batch size found, reached zero.'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=16    )
							def mock_training_loop_function(UpperCAmelCase    ):
													if batch_size > 0:
																			raise_fake_out_of_memory()
													pass

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('No executable batch size found, reached zero.'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
													if batch_size != 8:
																			raise raise_fake_out_of_memory()

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function(128       ,    'hello'       ,    'world'    )
													self.assertIn('Batch size was passed into `f`'       ,    cm.exception.args[0]    )
													self.assertIn('`f(arg1=\'hello\', arg2=\'world\')'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=16    )
							def mock_training_loop_function(UpperCAmelCase    ):
													raise ValueError('Oops, we had an error!'    )

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('Oops, we had an error!'       ,    cm.exception.args[0]    )

	@require_cuda
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			torch.cuda.memory_allocated()
							_UpperCAmelCase									=			ModelForTest()
							model.cuda()
							self.assertGreater(torch.cuda.memory_allocated()       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			release_memory(UpperCAmelCase    )
							self.assertEqual(torch.cuda.memory_allocated()       ,    UpperCAmelCase    )
