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


A						=			logging.get_logger(__name__)


A						=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

A						=			{
    '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',
    },
}

A						=			{
    'allenai/led-base-16384': 1_6_3_8_4,
}





class     lowerCAmelCase__     (				__snake_case			):


		'''simple docstring'''






		lowerCAmelCase_     =   VOCAB_FILES_NAMES
		lowerCAmelCase_     =   PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase_     =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase_     =   LEDTokenizer
		lowerCAmelCase_     =   ["""input_ids""", """attention_mask"""]
		def __init__(       self     :     str     ,  snake_case__     :     str=None     ,  snake_case__     :     str=None     ,  snake_case__     :     List[str]=None     ,  snake_case__     :     List[Any]="replace"     ,  snake_case__     :     str="<s>"     ,  snake_case__     :     str="</s>"     ,  snake_case__     :     int="</s>"     ,  snake_case__     :     str="<s>"     ,  snake_case__     :     Union[str, Any]="<unk>"     ,  snake_case__     :     List[str]="<pad>"     ,  snake_case__     :     Optional[Any]="<mask>"     ,  snake_case__     :     Union[str, Any]=False     ,  snake_case__     :     Optional[Any]=True     ,  **snake_case__     :     Optional[int]     ,  )      ->	List[Any]:
				super().__init__(
				    snake_case__     ,  snake_case__     ,  tokenizer_file=snake_case__     ,  errors=snake_case__     ,  bos_token=snake_case__     ,  eos_token=snake_case__     ,  sep_token=snake_case__     ,  cls_token=snake_case__     ,  unk_token=snake_case__     ,  pad_token=snake_case__     ,  mask_token=snake_case__     ,  add_prefix_space=snake_case__     ,  trim_offsets=snake_case__     ,  **snake_case__     ,  )

				_lowerCamelCase				   =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
				if pre_tok_state.get('add_prefix_space'     ,  snake_case__		) != add_prefix_space:
						_lowerCamelCase				   =  getattr(snake_case__     ,  pre_tok_state.pop('type'		)		)
						_lowerCamelCase				   =  add_prefix_space
						_lowerCamelCase				   =  pre_tok_class(**snake_case__		)

				_lowerCamelCase				   =  add_prefix_space

				# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
				_lowerCamelCase				   =  """post_processor"""
				_lowerCamelCase				   =  getattr(self.backend_tokenizer     ,  snake_case__     ,  snake_case__		)
				if tokenizer_component_instance:
						_lowerCamelCase				   =  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:
								_lowerCamelCase				   =  tuple(state['sep']		)
						if "cls" in state:
								_lowerCamelCase				   =  tuple(state['cls']		)

						_lowerCamelCase				   =  False

						if state.get('add_prefix_space'     ,  snake_case__		) != add_prefix_space:
								_lowerCamelCase				   =  add_prefix_space
								_lowerCamelCase				   =  True

						if state.get('trim_offsets'     ,  snake_case__		) != trim_offsets:
								_lowerCamelCase				   =  trim_offsets
								_lowerCamelCase				   =  True

						if changes_to_apply:
								_lowerCamelCase				   =  getattr(snake_case__     ,  state.pop('type'		)		)
								_lowerCamelCase				   =  component_class(**snake_case__		)
								setattr(self.backend_tokenizer     ,  snake_case__     ,  snake_case__		)
		@property
		# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
		def   _snake_case       (       self     :     List[str]		)      ->	str:
				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   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     int		)      ->	Dict:
				_lowerCamelCase				   =  AddedToken(snake_case__     ,  lstrip=snake_case__     ,  rstrip=snake_case__		) if isinstance(snake_case__     ,  snake_case__		) else value
				_lowerCamelCase				   =  value
		def   _snake_case       (       self     :     str     ,  *snake_case__     :     List[Any]     ,  **snake_case__     :     int		)      ->	BatchEncoding:
				_lowerCamelCase				   =  kwargs.get('is_split_into_words'     ,  snake_case__		)

				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(*snake_case__     ,  **snake_case__		)
		def   _snake_case       (       self     :     int     ,  *snake_case__     :     Optional[Any]     ,  **snake_case__     :     List[str]		)      ->	BatchEncoding:
				_lowerCamelCase				   =  kwargs.get('is_split_into_words'     ,  snake_case__		)

				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(*snake_case__     ,  **snake_case__		)
		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     str     ,  snake_case__     :     Optional[str] = None		)      ->	Tuple[str]:
				_lowerCamelCase				   =  self._tokenizer.model.save(snake_case__     ,  name=snake_case__		)
				return tuple(snake_case__		)
		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     Tuple     ,  snake_case__     :     Union[str, Any]=None		)      ->	List[str]:
				_lowerCamelCase				   =  [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   _snake_case       (       self     :     Tuple     ,  snake_case__     :     List[int]     ,  snake_case__     :     Optional[List[int]] = None		)      ->	List[int]:
				_lowerCamelCase				   =  [self.sep_token_id]
				_lowerCamelCase				   =  [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   _snake_case       (       self     :     Any     ,  snake_case__     :     Union[Dict[str, EncodedInput], BatchEncoding]     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     Optional[bool] = None     ,  )      ->	dict:
				_lowerCamelCase				   =  super()._pad(
				    encoded_inputs=snake_case__     ,  max_length=snake_case__     ,  padding_strategy=snake_case__     ,  pad_to_multiple_of=snake_case__     ,  return_attention_mask=snake_case__     ,  )

				# Load from model defaults
				if return_attention_mask is None:
						_lowerCamelCase				   =  """attention_mask""" in self.model_input_names

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

						if needs_to_be_padded:
								_lowerCamelCase				   =  len(snake_case__		) - 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`
										_lowerCamelCase				   =  (
										    encoded_inputs["""global_attention_mask"""] + [-1] * difference
										)
								elif self.padding_side == "left":
										_lowerCamelCase				   =  [-1] * difference + encoded_inputs[
										    """global_attention_mask"""
										]
								else:
										raise ValueError('Invalid padding strategy:' + str(self.padding_side		)		)

				return encoded_inputs







"""simple docstring"""







def      UpperCamelCase       ( _A  ,				_A )     ->       int:
				lowercase    :      int       = 1  # To kept the Calculated Value
				# Since C(n, k) = C(n, n-k)
				if k > (n - k):
								lowercase    :      List[Any]       = n - k
				# Calculate C(n,k)
				for i in range(_A ):
								result *= n - i
								result //= i + 1
				return result


def      UpperCamelCase       ( _A )     ->       int:
				return binomial_coefficient(2 * node_count  ,				_A ) // (node_count + 1)


def      UpperCamelCase       ( _A )     ->       int:
				if n < 0:
								raise ValueError("""factorial() not defined for negative values""" )
				lowercase    :      Union[str, Any]       = 1
				for i in range(1  ,				n + 1 ):
								result *= i
				return result


def      UpperCamelCase       ( _A )     ->       int:
				return catalan_number(_A ) * factorial(_A )


if __name__ == "__main__":
				_lowerCAmelCase            =	int(input('Enter the number of nodes: ').strip() or 0)
				if node_count <= 0:
								raise ValueError('We need some nodes to work with.')
				print(
				    F'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
				    F'binary trees and {catalan_number(node_count)} binary search trees.'
				)
