

from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE							 =			{
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _SCREAMING_SNAKE_CASE							 =			['''DebertaV2TokenizerFast''']

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _SCREAMING_SNAKE_CASE							 =			[
          '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFDebertaV2ForMaskedLM''',
          '''TFDebertaV2ForQuestionAnswering''',
          '''TFDebertaV2ForSequenceClassification''',
          '''TFDebertaV2ForTokenClassification''',
          '''TFDebertaV2Model''',
          '''TFDebertaV2PreTrainedModel''',
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _SCREAMING_SNAKE_CASE							 =			[
          '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DebertaV2ForMaskedLM''',
          '''DebertaV2ForMultipleChoice''',
          '''DebertaV2ForQuestionAnswering''',
          '''DebertaV2ForSequenceClassification''',
          '''DebertaV2ForTokenClassification''',
          '''DebertaV2Model''',
          '''DebertaV2PreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_deberta_va import (
          DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DebertaVaConfig,
          DebertaVaOnnxConfig,
      )
      from .tokenization_deberta_va import DebertaVaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_deberta_va import (
                TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDebertaVaForMaskedLM,
                TFDebertaVaForQuestionAnswering,
                TFDebertaVaForSequenceClassification,
                TFDebertaVaForTokenClassification,
                TFDebertaVaModel,
                TFDebertaVaPreTrainedModel,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_deberta_va import (
                DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                DebertaVaForMaskedLM,
                DebertaVaForMultipleChoice,
                DebertaVaForQuestionAnswering,
                DebertaVaForSequenceClassification,
                DebertaVaForTokenClassification,
                DebertaVaModel,
                DebertaVaPreTrainedModel,
            )

else:
      import sys

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











'''simple docstring'''


from __future__ import annotations

import random






class 						lowerCAmelCase_	:







  '''simple docstring'''

  UpperCamelCase_			:      Optional[int]										=				6



  def __init__( self				:							Dict	)			->		Dict:







       '''simple docstring'''



       A:		Union[str, Any]          =		list(range(1					,  Dice.NUM_SIDES + 1	)	)




  def 					_snake_case     ( self				:							Optional[Any]	)			->		Any:







       '''simple docstring'''



       return random.choice(self.sides	)






def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase = 2	)			-> list[float]:
     A:		Union[str, Any]          =		[Dice() for i in range(__lowercase	)]
     A:		Union[str, Any]          =		[0] * (len(__lowercase	) * Dice.NUM_SIDES + 1)
     for _ in range(__lowercase	):
          count_of_sum[sum(dice.roll() for dice in dices	)] += 1
     A:		Dict          =		[round((count * 1_0_0) / num_throws		,				2	) for count in count_of_sum]
     return probability[num_dice:]  # remove probability of sums that never appear


if __name__ == "__main__":
     import doctest

     doctest.testmod()






