




import argparse

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


logging.set_verbosity_info()


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		List[Any]:





  """simple docstring"""





  _lowercase:			Tuple      =     BertConfig.from_pretrained(
      'bert-large-cased'       ,  vocab_size=lowerCAmelCase__       ,  max_position_embeddings=512       ,  is_decoder=lowerCAmelCase__       ,  add_cross_attention=lowerCAmelCase__       ,  )
  _lowercase:			Tuple      =     bert_config.to_dict()
  del bert_config_dict["type_vocab_size"]
  _lowercase:			str      =     BertGenerationConfig(**lowerCAmelCase__				)
  if is_encoder:
    _lowercase:			Tuple      =     BertGenerationEncoder(lowerCAmelCase__				)
  else:
    _lowercase:			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__    :							Tuple       		=     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=50358, type=int, help='Vocab size of model')
   UpperCAmelCase__    :							Dict       		=     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()					)




