
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			:     Optional[int]           =   {
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Any           =   ['XLMRobertaTokenizer']

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Tuple           =   ['XLMRobertaTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     List[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:
    __lowerCAmelCase			:     Union[str, 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:
    __lowerCAmelCase			:     Optional[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

    __lowerCAmelCase			:     List[Any]           =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



def a__    (       A_					):






  '''simple docstring'''





  if not isinstance(A_,     A_					):
    raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""					)
  if len(A_					) == 0:
    raise ValueError("""Input list must be a non empty list"""					)
  if len(A_					) == 1:
    return True
  __magic_name__				     =       series[1] - series[0]
  for index in range(len(A_					) - 1					):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True

def a__    (       A_					):






  '''simple docstring'''





  if not isinstance(A_,     A_					):
    raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""					)
  if len(A_					) == 0:
    raise ValueError("""Input list must be a non empty list"""					)
  __magic_name__				     =       0
  for val in series:
    answer += val
  return answer / len(A_					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

