



"""simple docstring"""




from itertools import product


def      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase )   ->	list[int]:






      '''simple docstring'''


      lowercase     :					List[str] 		=      sides_number
      lowercase     :					str 		=      max_face_number * dice_number
      lowercase     :					Any 		=      [0] * (max_total + 1)

      lowercase     :					List[Any] 		=      1
      lowercase     :					Dict 		=      range(UpperCamelCase__		,					max_face_number + 1 )
      for dice_numbers in product(UpperCamelCase__		,					repeat=UpperCamelCase__ ):
            lowercase     :					Optional[int] 		=      sum(UpperCamelCase__ )
            totals_frequencies[total] += 1

      return totals_frequencies


def      lowercase__   (	)   ->	float:






      '''simple docstring'''


      lowercase     :					int 		=      total_frequency_distribution(
          sides_number=4		,					dice_number=9 )
      lowercase     :					Dict 		=      total_frequency_distribution(
          sides_number=6		,					dice_number=6 )

      lowercase     :					str 		=      0
      lowercase     :					List[Any] 		=      9
      lowercase     :					List[Any] 		=      4 * 9
      lowercase     :					str 		=      6
      for peter_total in range(UpperCamelCase__		,					max_peter_total + 1 ):
            peter_wins_count += peter_totals_frequencies[peter_total] * sum(
                colin_totals_frequencies[min_colin_total:peter_total] )

      lowercase     :					str 		=      (4**9) * (6**6)
      lowercase     :					int 		=      peter_wins_count / total_games_number

      lowercase     :					Union[str, Any] 		=      round(UpperCamelCase__		,					ndigits=7 )

      return rounded_peter_win_probability


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







import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY




@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						hf_hub_download(
            repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset''' )
        A__											=						VideoClassificationPipeline(model=__lowerCamelCase,image_processor=__lowerCamelCase,top_k=2 )
        A__											=						[
            example_video_filepath,
            '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
        ]
        return video_classifier, examples



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        for example in examples:
               A__											=						video_classifier(__lowerCamelCase )

               self.assertEqual(
                   __lowerCamelCase,[
                       {'''score''': ANY(__lowerCamelCase ), '''label''': ANY(__lowerCamelCase )},
                       {'''score''': ANY(__lowerCamelCase ), '''label''': ANY(__lowerCamelCase )},
                   ],)



 @require_torch
 def 				UpperCamelCase			(							self ):
        A__											=						'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
        A__											=						VideoMAEFeatureExtractor(
            size={'''shortest_edge''': 10},crop_size={'''height''': 10, '''width''': 10} )
        A__											=						pipeline(
            '''video-classification''',model=__lowerCamelCase,feature_extractor=__lowerCamelCase,frame_sampling_rate=4 )

        A__											=						hf_hub_download(repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset''' )
        A__											=						video_classifier(__lowerCamelCase,top_k=2 )
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4 ),[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],)

        A__											=						video_classifier(
            [
                video_file_path,
                video_file_path,
            ],top_k=2,)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4 ),[
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
            ],)




 @require_tf
 def 				UpperCamelCase			(							self ):
        pass





