
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,
)


lowercase  : Optional[Any]						   =							{
    """configuration_xlm_roberta""": [
        """XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaConfig""",
        """XLMRobertaOnnxConfig""",
    ],
}

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

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  : Tuple						   =							[
      """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:
  lowercase  : str						   =							[
      """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:
  lowercase  : int						   =							[
      """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

  lowercase  : Optional[Any]						   =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat







def 						_snake_case(  )			->   tuple[list[int], int]:
       lowercase							:					List[Any]	     =				[randint(-1_000 ,     1_000    ) for i in range(10    )]
       lowercase							:					Tuple	     =				randint(-5_000 ,     5_000    )
       return (arr, r)


lowercase  : List[Any]						   =							make_dataset()







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   tuple[int, ...]:
       for triplet in permutations(SCREAMING_SNAKE_CASE__ ,     3    ):
              if sum(SCREAMING_SNAKE_CASE__    ) == target:
                     return tuple(sorted(SCREAMING_SNAKE_CASE__    )    )
       return (0, 0, 0)







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   tuple[int, int, int]:
       arr.sort()
       lowercase							:					Optional[int]	     =				len(SCREAMING_SNAKE_CASE__    )
       for i in range(n - 1    ):
              lowercase     ,      lowercase							:					Optional[Any]	     =				i + 1, n - 1
              while left < right:
                     if arr[i] + arr[left] + arr[right] == target:
                            return (arr[i], arr[left], arr[right])
                     elif arr[i] + arr[left] + arr[right] < target:
                            left += 1
                     elif arr[i] + arr[left] + arr[right] > target:
                            right -= 1
       return (0, 0, 0)







def 						_snake_case(  )			->   tuple[float, float]:
       lowercase							:					Dict	     =				"""
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
       lowercase							:					Tuple	     =				"""
triplet_sum1(*dataset)
"""
       lowercase							:					int	     =				"""
triplet_sum2(*dataset)
"""
       lowercase							:					str	     =				repeat(setup=SCREAMING_SNAKE_CASE__ ,     stmt=SCREAMING_SNAKE_CASE__ ,     repeat=5 ,     number=10_000    )
       lowercase							:					Dict	     =				repeat(setup=SCREAMING_SNAKE_CASE__ ,     stmt=SCREAMING_SNAKE_CASE__ ,     repeat=5 ,     number=10_000    )
       return (min(SCREAMING_SNAKE_CASE__    ), min(SCREAMING_SNAKE_CASE__    ))


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  lowercase  : Union[str, Any]						   =							solution_times()
  print(F'''The time for naive implementation is {times[0]}.''')
  print(F'''The time for optimized implementation is {times[1]}.''')
