"""simple docstring"""





from itertools import product


def       lowercase_      (      _lowerCamelCase:		int  ,      _lowerCamelCase:		int					)       ->					int:



							'''simple docstring'''

							__lowerCamelCase    :						Tuple						      =   sides_number
							__lowerCamelCase    :						Optional[int]						      =   max_face_number * dice_number
							__lowerCamelCase    :						Optional[int]						      =   [0] * (max_total + 1)

							__lowerCamelCase    :						Optional[int]						      =   1
							__lowerCamelCase    :						Any						      =   range(_lowerCamelCase  ,      max_face_number + 1					)
							for dice_numbers in product(_lowerCamelCase  ,      repeat=_lowerCamelCase					):
														__lowerCamelCase    :						Tuple						      =   sum(_lowerCamelCase					)
														totals_frequencies[total] += 1

							return totals_frequencies


def       lowercase_      (      )       ->					Optional[Any]:



							'''simple docstring'''

							__lowerCamelCase    :						List[Any]						      =   total_frequency_distribution(
							    sides_number=4  ,      dice_number=9					)
							__lowerCamelCase    :						Any						      =   total_frequency_distribution(
							    sides_number=6  ,      dice_number=6					)

							__lowerCamelCase    :						Optional[Any]						      =   0
							__lowerCamelCase    :						Union[str, Any]						      =   9
							__lowerCamelCase    :						Dict						      =   4 * 9
							__lowerCamelCase    :						Dict						      =   6
							for peter_total in range(_lowerCamelCase  ,      max_peter_total + 1					):
														peter_wins_count += peter_totals_frequencies[peter_total] * sum(
														    colin_totals_frequencies[min_colin_total:peter_total]					)

							__lowerCamelCase    :						Dict						      =   (4**9) * (6**6)
							__lowerCamelCase    :						Any						      =   peter_wins_count / total_games_number

							__lowerCamelCase    :						int						      =   round(_lowerCamelCase  ,      ndigits=7					)

							return rounded_peter_win_probability


if __name__ == "__main__":
				print(F"""{solution() = }""")





from typing import TYPE_CHECKING

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


__lowerCAmelCase		:				List[str]       			=		{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Dict       			=		[
		    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ResNetForImageClassification',
		    'ResNetModel',
		    'ResNetPreTrainedModel',
		    'ResNetBackbone',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				int       			=		[
		    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFResNetForImageClassification',
		    'TFResNetModel',
		    'TFResNetPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Optional[Any]       			=		[
		    'FlaxResNetForImageClassification',
		    'FlaxResNetModel',
		    'FlaxResNetPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_resnet import (
				    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ResNetBackbone,
				    ResNetForImageClassification,
				    ResNetModel,
				    ResNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_resnet import (
				    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFResNetForImageClassification,
				    TFResNetModel,
				    TFResNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
		import sys

		__lowerCAmelCase		:				int       			=		_LazyModule(__name__, globals()['__file__'], _import_structure)






