

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,
)


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

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

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:   Optional[Any]										=       [
					    """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:
					_UpperCAmelCase		:   List[Any]										=       [
					    """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:
					_UpperCAmelCase		:   Any										=       [
					    """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

					_UpperCAmelCase		:   List[str]										=       _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''')




