






'''simple docstring'''
import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser



def     A__						(	A_		)     ->				Optional[Any]:
					_lowercase			= {}
					_lowercase			= tokenizer(example["content"]    ,      truncation=lowercase__		)["input_ids"]
					_lowercase			= len(example["content"]		) / len(output["input_ids"]		)
					return output


__magic_name__		:     str					=    HfArgumentParser(PretokenizationArguments)
__magic_name__		:     Optional[int]					=    parser.parse_args()
if args.num_workers is None:
	__magic_name__		:     Tuple					=    multiprocessing.cpu_count()
__magic_name__		:     Any					=    AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

__magic_name__		:     Tuple					=    time.time()
__magic_name__		:     Union[str, Any]					=    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''')

__magic_name__		:     str					=    time.time()
ds.push_to_hub(args.tokenized_data_repo)



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




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A									=  {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MraForMaskedLM',
		    'MraForMultipleChoice',
		    'MraForQuestionAnswering',
		    'MraForSequenceClassification',
		    'MraForTokenClassification',
		    'MraLayer',
		    'MraModel',
		    'MraPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mra import (
				    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MraForMaskedLM,
				    MraForMultipleChoice,
				    MraForQuestionAnswering,
				    MraForSequenceClassification,
				    MraForTokenClassification,
				    MraLayer,
				    MraModel,
				    MraPreTrainedModel,
				)
else:
		import sys

		A									=  _LazyModule(__name__, globals()['__file__'], _import_structure)
