


"""simple docstring"""







from itertools import product
def _A						(						lowercase	,   lowercase		):







    """simple docstring"""
    a					=sides_number
    a					=max_face_number * dice_number
    a					=[0] * (max_total + 1)

    a					=1
    a					=range(lowercase	,   max_face_number + 1		)
    for dice_numbers in product(lowercase	,   repeat=lowercase		):
        a					=sum(lowercase		)
        totals_frequencies[total] += 1

    return totals_frequencies
def _A						(						):







    """simple docstring"""
    a					=total_frequency_distribution(
        sides_number=4	,   dice_number=9		)
    a					=total_frequency_distribution(
        sides_number=6	,   dice_number=6		)

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

    a					=(4**9) * (6**6)
    a					=peter_wins_count / total_games_number

    a					=round(lowercase	,   ndigits=7		)

    return rounded_peter_win_probability


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



"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_       :       str      						=  logging.get_logger(__name__)

lowerCamelCase_       :       Optional[int]      						=  {
    """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/config.json""",
    """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/config.json""",
    """xlm-roberta-large-finetuned-conll02-dutch""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll02-spanish""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-english""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-german""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"""
    ),
}






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  "xlm-roberta"


 def __init__(		self			,   __A=3_0522			,   __A=768			,   __A=12			,   __A=12			,   __A=3072			,   __A="gelu"			,   __A=0.1			,   __A=0.1			,   __A=512			,   __A=2			,   __A=0.02			,   __A=1E-1_2			,   __A=1			,   __A=0			,   __A=2			,   __A="absolute"			,   __A=True			,   __A=None			,   **__A			,   ) ->					str:
     super().__init__(pad_token_id=__A			,   bos_token_id=__A			,   eos_token_id=__A			,   **__A						)

     a					=vocab_size
     a					=hidden_size
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=hidden_act
     a					=intermediate_size
     a					=hidden_dropout_prob
     a					=attention_probs_dropout_prob
     a					=max_position_embeddings
     a					=type_vocab_size
     a					=initializer_range
     a					=layer_norm_eps
     a					=position_embedding_type
     a					=use_cache
     a					=classifier_dropout






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
         a					={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         a					={0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]						)