






'''simple docstring'''







def     A__      (					__lowerCAmelCase		:      list[int] ,     __lowerCAmelCase		:      list[int]    ):
						lowerCamelCase__								=	len(__lowerCAmelCase    )
						print("""The following activities are selected:"""    )

						# The first activity is always selected
						lowerCamelCase__								=	0
						print(__lowerCAmelCase ,     end=""","""    )

						# Consider rest of the activities
						for j in range(__lowerCAmelCase    ):
												# If this activity has start time greater than
												# or equal to the finish time of previously
												# selected activity, then select it
												if start[j] >= finish[i]:
																		print(__lowerCAmelCase ,     end=""","""    )
																		lowerCamelCase__								=	j


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCamelCase     :   Union[str, Any]	      =		[1, 3, 0, 5, 8, 5]
							UpperCamelCase     :   int	      =		[2, 4, 6, 7, 9, 9]
							print_max_activities(start, finish)







from typing import TYPE_CHECKING

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


_lowerCamelCase      :						Any							=  {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

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

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						int							=  ["""XLNetTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						Union[str, Any]							=  [
      """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XLNetForMultipleChoice""",
      """XLNetForQuestionAnswering""",
      """XLNetForQuestionAnsweringSimple""",
      """XLNetForSequenceClassification""",
      """XLNetForTokenClassification""",
      """XLNetLMHeadModel""",
      """XLNetModel""",
      """XLNetPreTrainedModel""",
      """load_tf_weights_in_xlnet""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						Dict							=  [
      """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFXLNetForMultipleChoice""",
      """TFXLNetForQuestionAnsweringSimple""",
      """TFXLNetForSequenceClassification""",
      """TFXLNetForTokenClassification""",
      """TFXLNetLMHeadModel""",
      """TFXLNetMainLayer""",
      """TFXLNetModel""",
      """TFXLNetPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase      :						int							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


