

'''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,
)


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

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

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

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_       						=							[
        "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:
    A_       						=							[
        "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:
    A_       						=							[
        "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

    A_       						=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









'''simple docstring'''

from math import factorial





def    A_  ( snake_case      ,       snake_case ):

  # If either of the conditions are true, the function is being asked
  # to calculate a factorial of a negative number, which is not possible
  if n < k or k < 0:
    raise ValueError("Please enter positive integers for n and k where n >= k" )
  return factorial(snake_case ) // (factorial(snake_case ) * factorial(n - k ))


if __name__ == "__main__":
    print(
        "The number of five-card hands possible from a standard",
        f'''fifty-two card deck is: {combinations(52, 5)}\n''',
    )

    print(
        "If a class of 40 students must be arranged into groups of",
        f'''4 for group projects, there are {combinations(40, 4)} ways''',
        "to arrange them.\n",
    )

    print(
        "If 10 teams are competing in a Formula One race, there",
        f'''are {combinations(10, 3)} ways that first, second and''',
        "third place can be awarded.",
    )






