





'''simple docstring'''






import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser
def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							{}
      _lowerCAmelCase							     =							tokenizer(example["""content"""]							,	truncation=lowerCAmelCase  )["""input_ids"""]
      _lowerCAmelCase							     =							len(example["""content"""]  ) / len(output["""input_ids"""]  )
      return output


A__				:   int      =HfArgumentParser(PretokenizationArguments)
A__				:   Dict      =parser.parse_args()
if args.num_workers is None:
      A__				:   int      =multiprocessing.cpu_count()
A__				:   Optional[int]      =AutoTokenizer.from_pretrained(args.tokenizer_dir)

A__				:   Tuple      =time.time()
A__				:   Optional[int]      =load_dataset(args.dataset_name, split='''train''')
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")

A__				:   Union[str, Any]      =time.time()
A__				:   Optional[int]      =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""")

A__				:   Tuple      =time.time()
ds.push_to_hub(args.tokenized_data_repo)





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











import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
				import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin

@require_torch
class 		UpperCAmelCase__			(			unittest.TestCase						,__UpperCamelCase   ):




			'''simple docstring'''






			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	load_tool('''text-to-speech''' )
					self.tool.setup()




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     List[Any]													=	self.tool('''hey''' )
					__UpperCAmelCase    :     Union[str, Any]													=	result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]       ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] )       ,       ) )




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     Optional[int]													=	self.tool('''hey''' )
					__UpperCAmelCase    :     List[str]													=	result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]       ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] )       ,       ) )





