





from typing import TYPE_CHECKING

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


UpperCAmelCase_     							= {
    'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
    'tokenization_electra': ['ElectraTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= ['ElectraTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= [
      'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ElectraForCausalLM',
      'ElectraForMaskedLM',
      'ElectraForMultipleChoice',
      'ElectraForPreTraining',
      'ElectraForQuestionAnswering',
      'ElectraForSequenceClassification',
      'ElectraForTokenClassification',
      'ElectraModel',
      'ElectraPreTrainedModel',
      'load_tf_weights_in_electra',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= [
      'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFElectraForMaskedLM',
      'TFElectraForMultipleChoice',
      'TFElectraForPreTraining',
      'TFElectraForQuestionAnswering',
      'TFElectraForSequenceClassification',
      'TFElectraForTokenClassification',
      'TFElectraModel',
      'TFElectraPreTrainedModel',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= [
      'FlaxElectraForCausalLM',
      'FlaxElectraForMaskedLM',
      'FlaxElectraForMultipleChoice',
      'FlaxElectraForPreTraining',
      'FlaxElectraForQuestionAnswering',
      'FlaxElectraForSequenceClassification',
      'FlaxElectraForTokenClassification',
      'FlaxElectraModel',
      'FlaxElectraPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
  from .tokenization_electra import ElectraTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_electra_fast import ElectraTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_electra import (
        ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
        ElectraForCausalLM,
        ElectraForMaskedLM,
        ElectraForMultipleChoice,
        ElectraForPreTraining,
        ElectraForQuestionAnswering,
        ElectraForSequenceClassification,
        ElectraForTokenClassification,
        ElectraModel,
        ElectraPreTrainedModel,
        load_tf_weights_in_electra,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_electra import (
        TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFElectraForMaskedLM,
        TFElectraForMultipleChoice,
        TFElectraForPreTraining,
        TFElectraForQuestionAnswering,
        TFElectraForSequenceClassification,
        TFElectraForTokenClassification,
        TFElectraModel,
        TFElectraPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_electra import (
        FlaxElectraForCausalLM,
        FlaxElectraForMaskedLM,
        FlaxElectraForMultipleChoice,
        FlaxElectraForPreTraining,
        FlaxElectraForQuestionAnswering,
        FlaxElectraForSequenceClassification,
        FlaxElectraForTokenClassification,
        FlaxElectraModel,
        FlaxElectraPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_     							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








from __future__ import annotations




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			int      )      ->	list[list[int]]:
    UpperCamelCase__							:				list[list[int]]          =      []
    create_all_state(1		,   __UpperCAmelCase		,   __UpperCAmelCase		,   []		,   __UpperCAmelCase      )
    return result




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			list[int]		,   __UpperCAmelCase:			list[list[int]]		,   )      ->	None:
    if level == 0:
        total_list.append(current_list[:]      )
        return

    for i in range(__UpperCAmelCase		,   total_number - level + 2      ):
        current_list.append(__UpperCAmelCase      )
        create_all_state(i + 1		,   __UpperCAmelCase		,   level - 1		,   __UpperCAmelCase		,   __UpperCAmelCase      )
        current_list.pop()




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list[list[int]]      )      ->	None:
    for i in total_list:
        print(*__UpperCAmelCase      )


if __name__ == "__main__":
  UpperCAmelCase_     							= 4
  UpperCAmelCase_     							= 2
  UpperCAmelCase_     							= generate_all_combinations(n, k)
  print_all_state(total_list)

