



'''simple docstring'''
import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()


def 	lowercase_						( lowerCAmelCase__						:	Union[str, Any]      ,				lowerCAmelCase__						:	int      ,				lowerCAmelCase__						:	int      ,				lowerCAmelCase__						:	int      ,				lowerCAmelCase__						:	List[Any]			):

			"""simple docstring"""





			__UpperCAmelCase :   Tuple    					=  BertConfig.from_pretrained(
			    """bert-large-cased"""      ,				vocab_size=lowerCAmelCase__      ,				max_position_embeddings=512      ,				is_decoder=lowerCAmelCase__      ,				add_cross_attention=lowerCAmelCase__      ,				)
			__UpperCAmelCase :   Tuple    					=  bert_config.to_dict()
			del bert_config_dict["type_vocab_size"]
			__UpperCAmelCase :   str    					=  BertGenerationConfig(**lowerCAmelCase__			)
			if is_encoder:
						__UpperCAmelCase :   Tuple    					=  BertGenerationEncoder(lowerCAmelCase__			)
			else:
						__UpperCAmelCase :   Tuple    					=  BertGenerationDecoder(lowerCAmelCase__			)
			print(f'Building PyTorch model from configuration: {config}'			)

			# Load weights from tf checkpoint
			load_tf_weights_in_bert_generation(
			    lowerCAmelCase__      ,				lowerCAmelCase__      ,				model_class="""bert"""      ,				is_encoder_named_decoder=lowerCAmelCase__      ,				is_encoder=lowerCAmelCase__      ,				)

			# Save pytorch-model
			print(f'Save PyTorch model and config to {pytorch_dump_path}'			)
			model.save_pretrained(lowerCAmelCase__			)


if __name__ == "__main__":
					_UpperCamelCase									=      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--tf_hub_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
					)
					parser.add_argument(
					    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
					)
					parser.add_argument(
					    '''--is_encoder_named_decoder''',
					    action='''store_true''',
					    help='''If decoder has to be renamed to encoder in PyTorch model.''',
					)
					parser.add_argument('''--is_encoder''', action='''store_true''', help='''If model is an encoder.''')
					parser.add_argument('''--vocab_size''', default=5_0358, type=int, help='''Vocab size of model''')
					_UpperCamelCase									=      parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_hub_path,
					    args.pytorch_dump_path,
					    args.is_encoder_named_decoder,
					    args.vocab_size,
					    is_encoder=args.is_encoder,
					)









'''simple docstring'''
import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment



class _A	(     unittest.TestCase     ):





		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   int    					=  inspect.getfile(accelerate.test_utils					)
					__UpperCAmelCase :   int    					=  os.path.sep.join(mod_file.split(os.path.sep					)[:-1] + ["""scripts""", """test_sync.py"""]					)





		@require_cpu
		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					debug_launcher(test_sync.main			,							num_processes=1					)





		@require_cpu
		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					debug_launcher(test_sync.main					)





		@require_single_gpu
		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					test_sync.main()





		@require_multi_gpu
		def 				__A							(  self					)							->  List[Any]:

					'''simple docstring'''
					print(f'Found {torch.cuda.device_count()} devices.'					)
					__UpperCAmelCase :   List[str]    					=  ["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
					with patch_environment(omp_num_threads=1					):
								execute_subprocess_async(__UpperCAmelCase			,							env=os.environ.copy()					)




