

import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


lowerCAmelCase__	   = logging.get_logger(__name__)

class 					a__						:
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase       )  ->						Any:
							'''simple docstring'''






							A__        =			question_encoder
							A__        =			generator
							A__        =			self.question_encoder


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						List[Any]:
							'''simple docstring'''






							if os.path.isfile(UpperCAmelCase_       ):
											raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'       )
							os.makedirs(UpperCAmelCase_      ,      exist_ok=UpperCAmelCase_       )
							A__        =			os.path.join(UpperCAmelCase_      ,      "question_encoder_tokenizer"       )
							A__        =			os.path.join(UpperCAmelCase_      ,      "generator_tokenizer"       )
							self.question_encoder.save_pretrained(UpperCAmelCase_       )
							self.generator.save_pretrained(UpperCAmelCase_       )


			@classmethod
			def      UpperCamelCase		(    cls      ,      lowercase      ,      **lowercase       )  ->						str:
							'''simple docstring'''






							from ..auto.tokenization_auto import AutoTokenizer

							A__        =			kwargs.pop("config"      ,      UpperCAmelCase_       )

							if config is None:
											A__        =			RagConfig.from_pretrained(UpperCAmelCase_       )

							A__        =			AutoTokenizer.from_pretrained(
							    UpperCAmelCase_      ,      config=config.question_encoder      ,      subfolder="question_encoder_tokenizer"       )
							A__        =			AutoTokenizer.from_pretrained(
							    UpperCAmelCase_      ,      config=config.generator      ,      subfolder="generator_tokenizer"       )
							return cls(question_encoder=UpperCAmelCase_      ,      generator=UpperCAmelCase_       )


			def __call__(    self      ,      *lowercase      ,      **lowercase       )  ->						Dict:
							'''simple docstring'''






							return self.current_tokenizer(*UpperCAmelCase_      ,      **UpperCAmelCase_       )


			def      UpperCamelCase		(    self      ,      *lowercase      ,      **lowercase       )  ->						Tuple:
							'''simple docstring'''






							return self.generator.batch_decode(*UpperCAmelCase_      ,      **UpperCAmelCase_       )


			def      UpperCamelCase		(    self      ,      *lowercase      ,      **lowercase       )  ->						str:
							'''simple docstring'''






							return self.generator.decode(*UpperCAmelCase_      ,      **UpperCAmelCase_       )


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							A__        =			self.question_encoder


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							A__        =			self.generator


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = "longest"      ,      lowercase = None      ,      lowercase = True      ,      **lowercase      ,      )  ->						str:
							'''simple docstring'''






							warnings.warn(
							    "`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
							    "regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
							    "context manager to prepare your targets. See the documentation of your specific tokenizer for more "
							    "details"      ,      UpperCAmelCase_      ,      )
							if max_length is None:
											A__        =			self.current_tokenizer.model_max_length
							A__        =			self(
							    UpperCAmelCase_      ,      add_special_tokens=UpperCAmelCase_      ,      return_tensors=UpperCAmelCase_      ,      max_length=UpperCAmelCase_      ,      padding=UpperCAmelCase_      ,      truncation=UpperCAmelCase_      ,      **UpperCAmelCase_      ,      )
							if tgt_texts is None:
											return model_inputs
							# Process tgt_texts
							if max_target_length is None:
											A__        =			self.current_tokenizer.model_max_length
							A__        =			self(
							    text_target=UpperCAmelCase_      ,      add_special_tokens=UpperCAmelCase_      ,      return_tensors=UpperCAmelCase_      ,      padding=UpperCAmelCase_      ,      max_length=UpperCAmelCase_      ,      truncation=UpperCAmelCase_      ,      **UpperCAmelCase_      ,      )
							A__        =			labels['input_ids']
							return model_inputs




import math

lowerCAmelCase__	   = 1_0
lowerCAmelCase__	   = 7
lowerCAmelCase__	   = BALLS_PER_COLOUR * NUM_COLOURS



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int = 2_0      )			->      str:




				'''simple docstring'''





				A__        =			math.comb(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
				A__        =			math.comb(NUM_BALLS - BALLS_PER_COLOUR	,       SCREAMING_SNAKE_CASE_      )

				A__        =			NUM_COLOURS * (1 - missing_colour / total)

				return F'{result:.9f}'


if __name__ == "__main__":
	print(solution(2_0))

