




'''simple docstring'''


from typing import TYPE_CHECKING

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


UpperCamelCase			     =					{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase			     =					[
         '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTJForCausalLM''',
         '''GPTJForQuestionAnswering''',
         '''GPTJForSequenceClassification''',
         '''GPTJModel''',
         '''GPTJPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase			     =					[
         '''TFGPTJForCausalLM''',
         '''TFGPTJForQuestionAnswering''',
         '''TFGPTJForSequenceClassification''',
         '''TFGPTJModel''',
         '''TFGPTJPreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase			     =					[
         '''FlaxGPTJForCausalLM''',
         '''FlaxGPTJModel''',
         '''FlaxGPTJPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

     UpperCamelCase			     =					_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()






