




import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class 		_A   (    unittest.TestCase ):






  def    __a     (    self						:      List[Any] )						->       Any:






       """simple docstring"""







       lowercase   :  str        =					inspect.getfile(accelerate.test_utils )
       lowercase   :  Any        =					os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_script.py'''] )
       lowercase   :  List[str]        =					os.path.sep.join(
           mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_distributed_data_loop.py'''] )
       lowercase   :  List[Any]        =					os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_ops.py'''] )






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






       """simple docstring"""







       print(f"""Found {torch.cuda.device_count()} devices.""" )
       lowercase   :  Optional[Any]        =					['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() )






  @require_multi_gpu
  def    __a     (    self						:      Dict )						->       int:






       """simple docstring"""







       print(f"""Found {torch.cuda.device_count()} devices.""" )
       lowercase   :  Dict        =					['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
       print(f"""Command: {cmd}""" )
       with patch_environment(omp_num_threads=1 ):
            execute_subprocess_async(_UpperCAmelCase ,		env=os.environ.copy() )






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






       """simple docstring"""







       lowercase   :  Optional[int]        =					['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
       with patch_environment(omp_num_threads=1 ):
            execute_subprocess_async(_UpperCAmelCase ,		env=os.environ.copy() )






  @require_multi_gpu
  def    __a     (    self						:      List[Any] )						->       Optional[int]:






       """simple docstring"""







       print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only""" )
       lowercase   :  Optional[int]        =					['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
       with patch_environment(omp_num_threads=1 ,		cuda_visible_devices='''0,1''' ):
            execute_subprocess_async(_UpperCAmelCase ,		env=os.environ.copy() )


if __name__ == "__main__":
 lowerCAmelCase_						 =					Accelerator()
 lowerCAmelCase_						 =					(accelerator.state.process_index + 2, 10)
 lowerCAmelCase_						 =					torch.randint(0, 10, shape).to(accelerator.device)

 lowerCAmelCase_						 =					''''''

 lowerCAmelCase_						 =					accelerator.pad_across_processes(tensor)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
  error_msg += "Padding was not done with the right value (0)."

 lowerCAmelCase_						 =					accelerator.pad_across_processes(tensor, pad_first=True)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 lowerCAmelCase_						 =					accelerator.state.num_processes - accelerator.state.process_index - 1
 if not torch.equal(tensora[index:], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[:index] == 0):
  error_msg += "Padding was not done with the right value (0)."

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)






'''simple docstring'''




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__       : Tuple         =       logging.get_logger(__name__)






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str     )	->	Union[str, Any]:
     __A		:					int						=			RobertaPreLayerNormConfig.from_pretrained(
         __snake_case					,				architectures=['RobertaPreLayerNormForMaskedLM']     )

     # convert state_dict
     __A		:					Tuple						=			torch.load(hf_hub_download(repo_id=__snake_case					,				filename='pytorch_model.bin'     )     )
     __A		:					str						=			{}
     for tensor_key, tensor_value in original_state_dict.items():
          # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
          if tensor_key.startswith('roberta.'     ):
               __A		:					Dict						=			'roberta_prelayernorm.' + tensor_key[len('roberta.'     ) :]

          # The original implementation contains weights which are not used, remove them from the state_dict
          if tensor_key.endswith('.self.LayerNorm.weight'     ) or tensor_key.endswith('.self.LayerNorm.bias'     ):
               continue

          __A		:					str						=			tensor_value

     __A		:					Union[str, Any]						=			RobertaPreLayerNormForMaskedLM.from_pretrained(
         pretrained_model_name_or_path=__snake_case					,				config=__snake_case					,				state_dict=__snake_case     )
     model.save_pretrained(__snake_case     )

     # convert tokenizer
     __A		:					List[Any]						=			AutoTokenizer.from_pretrained(__snake_case     )
     tokenizer.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Dict         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint-repo''',
      default=None,
      type=str,
      required=True,
      help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)