


'''simple docstring'''




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 		UpperCAmelCase_      (  unittest.TestCase		):





     def 							__UpperCAmelCase      ( self       :      int    )				->  List[str]:
        lowerCAmelCase     =			inspect.getfile(accelerate.test_utils    )
        lowerCAmelCase     =			os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ['scripts', 'test_script.py']    )
        lowerCAmelCase     =			os.path.sep.join(
            mod_file.split(os.path.sep    )[:-1] + ['scripts', 'test_distributed_data_loop.py']    )
        lowerCAmelCase     =			os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ['scripts', 'test_ops.py']    )





     @require_multi_gpu
     def 							__UpperCAmelCase      ( self       :      Any    )				->  List[Any]:
        print(F'''Found {torch.cuda.device_count()} devices.'''    )
        lowerCAmelCase     =			['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 							__UpperCAmelCase      ( self       :      List[str]    )				->  Optional[int]:
        print(F'''Found {torch.cuda.device_count()} devices.'''    )
        lowerCAmelCase     =			['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 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Union[str, Any]:
        lowerCAmelCase     =			['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 							__UpperCAmelCase      ( self       :      str    )				->  Optional[int]:
        print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only'''    )
        lowerCAmelCase     =			['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__":
       __snake_case    						=Accelerator()
       __snake_case    						=(accelerator.state.process_index + 2, 10)
       __snake_case    						=torch.randint(0, 10, shape).to(accelerator.device)

       __snake_case    						=""""""

       __snake_case    						=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)."

       __snake_case    						=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."
       __snake_case    						=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 multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 	a_					( lowerCamelCase	:     Dict				):
   lowerCAmelCase     =			{}
   lowerCAmelCase     =			tokenizer(example['content']	, truncation=lowerCamelCase				)['input_ids']
   lowerCAmelCase     =			len(example['content']				) / len(output['input_ids']				)
   return output


__snake_case    						=HfArgumentParser(PretokenizationArguments)
__snake_case    						=parser.parse_args()
if args.num_workers is None:
       __snake_case    						=multiprocessing.cpu_count()
__snake_case    						=AutoTokenizer.from_pretrained(args.tokenizer_dir)

__snake_case    						=time.time()
__snake_case    						=load_dataset(args.dataset_name, split="""train""")
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__snake_case    						=time.time()
__snake_case    						=ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__snake_case    						=time.time()
ds.push_to_hub(args.tokenized_data_repo)




print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')





