

'''simple docstring'''



import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


__snake_case			:     Optional[Any]										=      logging.get_logger(__name__)


__snake_case			:     Optional[int]										=      {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__snake_case			:     Any										=      {
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

__snake_case			:     Dict										=      {
    'allenai/led-base-16384': 1_6384,
}



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	VOCAB_FILES_NAMES
			__snake_case        =	PRETRAINED_VOCAB_FILES_MAP
			__snake_case        =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case        =	LEDTokenizer
			__snake_case        =	['input_ids', 'attention_mask']


			def __init__(				self	:						Optional[int]		,      lowerCAmelCase_	:						Optional[Any]=None		,      lowerCAmelCase_	:						Tuple=None		,      lowerCAmelCase_	:						List[Any]=None		,      lowerCAmelCase_	:						List[Any]="replace"		,      lowerCAmelCase_	:						Dict="<s>"		,      lowerCAmelCase_	:						List[str]="</s>"		,      lowerCAmelCase_	:						Optional[Any]="</s>"		,      lowerCAmelCase_	:						Any="<s>"		,      lowerCAmelCase_	:						Tuple="<unk>"		,      lowerCAmelCase_	:						Tuple="<pad>"		,      lowerCAmelCase_	:						Union[str, Any]="<mask>"		,      lowerCAmelCase_	:						Optional[Any]=False		,      lowerCAmelCase_	:						Dict=True		,      **lowerCAmelCase_	:						str		,      )      ->		str:





									'''simple docstring'''
									super().__init__(
									    lowerCAmelCase_		,      lowerCAmelCase_		,      tokenizer_file=lowerCAmelCase_		,      errors=lowerCAmelCase_		,      bos_token=lowerCAmelCase_		,      eos_token=lowerCAmelCase_		,      sep_token=lowerCAmelCase_		,      cls_token=lowerCAmelCase_		,      unk_token=lowerCAmelCase_		,      pad_token=lowerCAmelCase_		,      mask_token=lowerCAmelCase_		,      add_prefix_space=lowerCAmelCase_		,      trim_offsets=lowerCAmelCase_		,      **lowerCAmelCase_		,      )

									A__					:       Tuple     =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
									if pre_tok_state.get("""add_prefix_space"""		,      lowerCAmelCase_			) != add_prefix_space:
															A__					:       int     =getattr(lowerCAmelCase_		,      pre_tok_state.pop("""type"""			)			)
															A__					:       Tuple     =add_prefix_space
															A__					:       Any     =pre_tok_class(**lowerCAmelCase_			)

									A__					:       Union[str, Any]     =add_prefix_space

									# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
									A__					:       int     ="""post_processor"""
									A__					:       List[Any]     =getattr(self.backend_tokenizer		,      lowerCAmelCase_		,      lowerCAmelCase_			)
									if tokenizer_component_instance:
															A__					:       Tuple     =json.loads(tokenizer_component_instance.__getstate__()			)

															# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
															if "sep" in state:
																					A__					:       Tuple     =tuple(state["""sep"""]			)
															if "cls" in state:
																					A__					:       Optional[int]     =tuple(state["""cls"""]			)

															A__					:       Any     =False

															if state.get("""add_prefix_space"""		,      lowerCAmelCase_			) != add_prefix_space:
																					A__					:       Optional[Any]     =add_prefix_space
																					A__					:       Tuple     =True

															if state.get("""trim_offsets"""		,      lowerCAmelCase_			) != trim_offsets:
																					A__					:       Optional[int]     =trim_offsets
																					A__					:       Union[str, Any]     =True

															if changes_to_apply:
																					A__					:       Dict     =getattr(lowerCAmelCase_		,      state.pop("""type"""			)			)
																					A__					:       Dict     =component_class(**lowerCAmelCase_			)
																					setattr(self.backend_tokenizer		,      lowerCAmelCase_		,      lowerCAmelCase_			)


			@property
			# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
			def lowercase__						(				self	:						Any			)      ->		str:





									'''simple docstring'''
									if self._mask_token is None:
															if self.verbose:
																					logger.error("""Using mask_token, but it is not set yet."""			)
															return None
									return str(self._mask_token			)


			@mask_token.setter
			def lowercase__						(				self	:						Tuple		,      lowerCAmelCase_	:						List[Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       List[Any]     =AddedToken(lowerCAmelCase_		,      lstrip=lowerCAmelCase_		,      rstrip=lowerCAmelCase_			) if isinstance(lowerCAmelCase_		,      lowerCAmelCase_			) else value
									A__					:       Union[str, Any]     =value


			def lowercase__						(				self	:						Optional[int]		,      *lowerCAmelCase_	:						Any		,      **lowerCAmelCase_	:						int			)      ->		BatchEncoding:





									'''simple docstring'''
									A__					:       Dict     =kwargs.get("""is_split_into_words"""		,      lowerCAmelCase_			)

									if is_split_into_words and not self.add_prefix_space:
															raise ValueError(
															    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
															    """to use it with pretokenized inputs."""			)

									return super()._batch_encode_plus(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						Optional[int]		,      *lowerCAmelCase_	:						Union[str, Any]		,      **lowerCAmelCase_	:						Optional[int]			)      ->		BatchEncoding:





									'''simple docstring'''
									A__					:       Dict     =kwargs.get("""is_split_into_words"""		,      lowerCAmelCase_			)

									if is_split_into_words and not self.add_prefix_space:
															raise ValueError(
															    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
															    """to use it with pretokenized inputs."""			)

									return super()._encode_plus(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						str		,      lowerCAmelCase_	:						Optional[str] = None			)      ->		Tuple[str]:





									'''simple docstring'''
									A__					:       Any     =self._tokenizer.model.save(lowerCAmelCase_		,      name=lowerCAmelCase_			)
									return tuple(lowerCAmelCase_			)


			def lowercase__						(				self	:						List[str]		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						Tuple=None			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       Optional[Any]     =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
															return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						List[int]		,      lowerCAmelCase_	:						Optional[List[int]] = None			)      ->		List[int]:





									'''simple docstring'''
									A__					:       List[Any]     =[self.sep_token_id]
									A__					:       Optional[int]     =[self.cls_token_id]

									if token_ids_a is None:
															return len(cls + token_ids_a + sep			) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]



			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						Union[Dict[str, EncodedInput], BatchEncoding]		,      lowerCAmelCase_	:						Optional[int] = None		,      lowerCAmelCase_	:						PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,      lowerCAmelCase_	:						Optional[int] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      )      ->		dict:





									'''simple docstring'''
									A__					:       List[str]     =super()._pad(
									    encoded_inputs=lowerCAmelCase_		,      max_length=lowerCAmelCase_		,      padding_strategy=lowerCAmelCase_		,      pad_to_multiple_of=lowerCAmelCase_		,      return_attention_mask=lowerCAmelCase_		,      )

									# Load from model defaults
									if return_attention_mask is None:
															A__					:       Optional[Any]     ="""attention_mask""" in self.model_input_names

									if return_attention_mask and "global_attention_mask" in encoded_inputs:
															A__					:       Optional[Any]     =encoded_inputs[self.model_input_names[0]]
															# `global_attention_mask` need to have the same length as other (sequential) inputs.
															A__					:       Any     =len(encoded_inputs["""global_attention_mask"""]			) != len(lowerCAmelCase_			)

															if needs_to_be_padded:
																					A__					:       int     =len(lowerCAmelCase_			) - len(encoded_inputs["""global_attention_mask"""]			)

																					if self.padding_side == "right":
																											# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																											A__					:       Dict     =(
																											    encoded_inputs["""global_attention_mask"""] + [-1] * difference
																											)
																					elif self.padding_side == "left":
																											A__					:       Dict     =[-1] * difference + encoded_inputs[
																											    """global_attention_mask"""
																											]
																					else:
																											raise ValueError("""Invalid padding strategy:""" + str(self.padding_side			)			)

									return encoded_inputs




'''simple docstring'''



import math

__snake_case			:     List[Any]										=      10
__snake_case			:     Dict										=      7
__snake_case			:     str										=      BALLS_PER_COLOUR * NUM_COLOURS
def 						__lowerCamelCase	(       __snake_case					:		int = 20	)	->							str:






						"""simple docstring"""





						A__					:       Union[str, Any]     =math.comb(__snake_case,		__snake_case	)
						A__					:       str     =math.comb(NUM_BALLS - BALLS_PER_COLOUR,		__snake_case	)

						A__					:       Optional[int]     =NUM_COLOURS * (1 - missing_colour / total)

						return f"{result:.9f}"


if __name__ == "__main__":
		print(solution(20))

