

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,
)


_SCREAMING_SNAKE_CASE  :						int         =    {
    '''configuration_albert''': ['''ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''AlbertConfig''', '''AlbertOnnxConfig'''],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						str         =    ['''AlbertTokenizer''']

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						str         =    [
           '''ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''AlbertForMaskedLM''',
           '''AlbertForMultipleChoice''',
           '''AlbertForPreTraining''',
           '''AlbertForQuestionAnswering''',
           '''AlbertForSequenceClassification''',
           '''AlbertForTokenClassification''',
           '''AlbertModel''',
           '''AlbertPreTrainedModel''',
           '''load_tf_weights_in_albert''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Tuple         =    [
           '''TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFAlbertForMaskedLM''',
           '''TFAlbertForMultipleChoice''',
           '''TFAlbertForPreTraining''',
           '''TFAlbertForQuestionAnswering''',
           '''TFAlbertForSequenceClassification''',
           '''TFAlbertForTokenClassification''',
           '''TFAlbertMainLayer''',
           '''TFAlbertModel''',
           '''TFAlbertPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						str         =    [
           '''FlaxAlbertForMaskedLM''',
           '''FlaxAlbertForMultipleChoice''',
           '''FlaxAlbertForPreTraining''',
           '''FlaxAlbertForQuestionAnswering''',
           '''FlaxAlbertForSequenceClassification''',
           '''FlaxAlbertForTokenClassification''',
           '''FlaxAlbertModel''',
           '''FlaxAlbertPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_albert import AlbertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_albert_fast import AlbertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_albert import (
                  ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  AlbertForMaskedLM,
                  AlbertForMultipleChoice,
                  AlbertForPreTraining,
                  AlbertForQuestionAnswering,
                  AlbertForSequenceClassification,
                  AlbertForTokenClassification,
                  AlbertModel,
                  AlbertPreTrainedModel,
                  load_tf_weights_in_albert,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_albert import (
                  TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFAlbertForMaskedLM,
                  TFAlbertForMultipleChoice,
                  TFAlbertForPreTraining,
                  TFAlbertForQuestionAnswering,
                  TFAlbertForSequenceClassification,
                  TFAlbertForTokenClassification,
                  TFAlbertMainLayer,
                  TFAlbertModel,
                  TFAlbertPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_albert import (
                  FlaxAlbertForMaskedLM,
                  FlaxAlbertForMultipleChoice,
                  FlaxAlbertForPreTraining,
                  FlaxAlbertForQuestionAnswering,
                  FlaxAlbertForSequenceClassification,
                  FlaxAlbertForTokenClassification,
                  FlaxAlbertModel,
                  FlaxAlbertPreTrainedModel,
              )
else:
       import sys

       _SCREAMING_SNAKE_CASE  :						Optional[int]         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





import random
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					a[left_index]
     SCREAMING_SNAKE_CASE__					=					left_index + 1
     for j in range(left_index + 1   ,	_A       ):
          if a[j] < pivot:
               SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					a[i], a[j]
               i += 1
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					a[i - 1], a[left_index]
     return i - 1
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     if left < right:
          SCREAMING_SNAKE_CASE__					=					random.randint(_A   ,	right - 1       )
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					(
              a[left],
              a[pivot],
          )  # switches the pivot with the left most bound
          SCREAMING_SNAKE_CASE__					=					partition(_A   ,	_A   ,	_A       )
          quick_sort_random(
              _A   ,	_A   ,	_A       )  # recursive quicksort to the left of the pivot point
          quick_sort_random(
              _A   ,	pivot_index + 1   ,	_A       )  # recursive quicksort to the right of the pivot point
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					input('''Enter numbers separated by a comma:\n'''       ).strip()
     SCREAMING_SNAKE_CASE__					=					[int(_A       ) for item in user_input.split(''','''       )]

     quick_sort_random(_A   ,	0   ,	len(_A       )       )

     print(_A       )


if __name__ == "__main__":
       main()


