


'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__					=   {
    """configuration_xlm_roberta""": [
        """XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaConfig""",
        """XLMRobertaOnnxConfig""",
    ],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__					=   ["""XLMRobertaTokenizer"""]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__					=   ["""XLMRobertaTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__					=   [
						    """XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """XLMRobertaForCausalLM""",
						    """XLMRobertaForMaskedLM""",
						    """XLMRobertaForMultipleChoice""",
						    """XLMRobertaForQuestionAnswering""",
						    """XLMRobertaForSequenceClassification""",
						    """XLMRobertaForTokenClassification""",
						    """XLMRobertaModel""",
						    """XLMRobertaPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__					=   [
						    """TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFXLMRobertaForCausalLM""",
						    """TFXLMRobertaForMaskedLM""",
						    """TFXLMRobertaForMultipleChoice""",
						    """TFXLMRobertaForQuestionAnswering""",
						    """TFXLMRobertaForSequenceClassification""",
						    """TFXLMRobertaForTokenClassification""",
						    """TFXLMRobertaModel""",
						    """TFXLMRobertaPreTrainedModel""",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__					=   [
						    """FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """FlaxXLMRobertaForMaskedLM""",
						    """FlaxXLMRobertaForCausalLM""",
						    """FlaxXLMRobertaForMultipleChoice""",
						    """FlaxXLMRobertaForQuestionAnswering""",
						    """FlaxXLMRobertaForSequenceClassification""",
						    """FlaxXLMRobertaForTokenClassification""",
						    """FlaxXLMRobertaModel""",
						    """FlaxXLMRobertaPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_xlm_roberta import (
						    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    XLMRobertaConfig,
						    XLMRobertaOnnxConfig,
						)

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlm_roberta import XLMRobertaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_roberta import (
												    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMRobertaForCausalLM,
												    XLMRobertaForMaskedLM,
												    XLMRobertaForMultipleChoice,
												    XLMRobertaForQuestionAnswering,
												    XLMRobertaForSequenceClassification,
												    XLMRobertaForTokenClassification,
												    XLMRobertaModel,
												    XLMRobertaPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_xlm_roberta import (
												    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFXLMRobertaForCausalLM,
												    TFXLMRobertaForMaskedLM,
												    TFXLMRobertaForMultipleChoice,
												    TFXLMRobertaForQuestionAnswering,
												    TFXLMRobertaForSequenceClassification,
												    TFXLMRobertaForTokenClassification,
												    TFXLMRobertaModel,
												    TFXLMRobertaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_xlm_roberta import (
												    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaxXLMRobertaForCausalLM,
												    FlaxXLMRobertaForMaskedLM,
												    FlaxXLMRobertaForMultipleChoice,
												    FlaxXLMRobertaForQuestionAnswering,
												    FlaxXLMRobertaForSequenceClassification,
												    FlaxXLMRobertaForTokenClassification,
												    FlaxXLMRobertaModel,
												    FlaxXLMRobertaPreTrainedModel,
												)

else:
						import sys

						lowerCamelCase__					=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    {}
							snake_case_							=    tokenizer(example['content']   ,							truncation=UpperCamelCase__      )['input_ids']
							snake_case_							=    len(example['content']      ) / len(output['input_ids']      )
							return output


_UpperCAmelCase		:   Dict										=       HfArgumentParser(PretokenizationArguments)
_UpperCAmelCase		:   List[Any]										=       parser.parse_args()
if args.num_workers is None:
					_UpperCAmelCase		:   Union[str, Any]										=       multiprocessing.cpu_count()
_UpperCAmelCase		:   int										=       AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

_UpperCAmelCase		:   Tuple										=       time.time()
_UpperCAmelCase		:   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''')

_UpperCAmelCase		:   Dict										=       time.time()
ds.push_to_hub(args.tokenized_data_repo)


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




