

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": 16384,
}

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Tuple       				=  VOCAB_FILES_NAMES
	_lowercase  :   Any       				=  PRETRAINED_VOCAB_FILES_MAP
	_lowercase  :   str       				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowercase  :   Dict       				=  LEDTokenizer
	_lowercase  :   Tuple       				=  ["input_ids", "attention_mask"]







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

						'''simple docstring'''





						super().__init__(
						    __UpperCamelCase  , __UpperCamelCase  , tokenizer_file=__UpperCamelCase  , errors=__UpperCamelCase  , bos_token=__UpperCamelCase  , eos_token=__UpperCamelCase  , sep_token=__UpperCamelCase  , cls_token=__UpperCamelCase  , unk_token=__UpperCamelCase  , pad_token=__UpperCamelCase  , mask_token=__UpperCamelCase  , add_prefix_space=__UpperCamelCase  , trim_offsets=__UpperCamelCase  , **__UpperCamelCase  , )

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

						__magic_name__							=    add_prefix_space

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

											__magic_name__							=    False

											if state.get('add_prefix_space'  , __UpperCamelCase      ) != add_prefix_space:
																__magic_name__							=    add_prefix_space
																__magic_name__							=    True

											if state.get('trim_offsets'  , __UpperCamelCase      ) != trim_offsets:
																__magic_name__							=    trim_offsets
																__magic_name__							=    True

											if changes_to_apply:
																__magic_name__							=    getattr(__UpperCamelCase  , state.pop('type'      )      )
																__magic_name__							=    component_class(**__UpperCamelCase      )
																setattr(self.backend_tokenizer  , __UpperCamelCase  , __UpperCamelCase      )







	@property
	# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''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 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    AddedToken(__UpperCamelCase  , lstrip=__UpperCamelCase  , rstrip=__UpperCamelCase      ) if isinstance(__UpperCamelCase  , __UpperCamelCase      ) else value
						__magic_name__							=    value







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , *__UpperCamelCase: Any  , **__UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    kwargs.get('is_split_into_words'  , __UpperCamelCase      )

						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(*__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str  , *__UpperCamelCase: Tuple  , **__UpperCamelCase: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    kwargs.get('is_split_into_words'  , __UpperCamelCase      )

						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(*__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[str] = None      ):

						'''simple docstring'''





						__magic_name__							=    self._tokenizer.model.save(__UpperCamelCase  , name=__UpperCamelCase      )
						return tuple(__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str  , __UpperCamelCase: int  , __UpperCamelCase: Any=None      ):

						'''simple docstring'''





						__magic_name__							=    [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 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , __UpperCamelCase: List[int]  , __UpperCamelCase: Optional[List[int]] = None      ):

						'''simple docstring'''





						__magic_name__							=    [self.sep_token_id]
						__magic_name__							=    [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 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]  , __UpperCamelCase: Union[Dict[str, EncodedInput], BatchEncoding]  , __UpperCamelCase: Optional[int] = None  , __UpperCamelCase: PaddingStrategy = PaddingStrategy.DO_NOT_PAD  , __UpperCamelCase: Optional[int] = None  , __UpperCamelCase: Optional[bool] = None  , ):

						'''simple docstring'''





						__magic_name__							=    super()._pad(
						    encoded_inputs=__UpperCamelCase  , max_length=__UpperCamelCase  , padding_strategy=__UpperCamelCase  , pad_to_multiple_of=__UpperCamelCase  , return_attention_mask=__UpperCamelCase  , )

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

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

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

						return encoded_inputs







import os

A__						=							{"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}




def 					_lowercase     (				a_				:					str				)				->	int:




					'''simple docstring'''







					__magic_name__							=    0

					__magic_name__							=    0
					while index < len(a_				) - 1:
										__magic_name__							=    SYMBOLS[numerals[index]]
										__magic_name__							=    SYMBOLS[numerals[index + 1]]
										if current_value < next_value:
															total_value -= current_value
										else:
															total_value += current_value
										index += 1
					total_value += SYMBOLS[numerals[index]]

					return total_value




def 					_lowercase     (				a_				:					int				)				->	str:




					'''simple docstring'''







					__magic_name__							=    ''

					__magic_name__							=    num // 1_0_0_0
					numerals += m_count * "M"
					num %= 1_0_0_0

					__magic_name__							=    num // 1_0_0
					if c_count == 9:
										numerals += "CM"
										c_count -= 9
					elif c_count == 4:
										numerals += "CD"
										c_count -= 4
					if c_count >= 5:
										numerals += "D"
										c_count -= 5
					numerals += c_count * "C"
					num %= 1_0_0

					__magic_name__							=    num // 1_0
					if x_count == 9:
										numerals += "XC"
										x_count -= 9
					elif x_count == 4:
										numerals += "XL"
										x_count -= 4
					if x_count >= 5:
										numerals += "L"
										x_count -= 5
					numerals += x_count * "X"
					num %= 1_0

					if num == 9:
										numerals += "IX"
										num -= 9
					elif num == 4:
										numerals += "IV"
										num -= 4
					if num >= 5:
										numerals += "V"
										num -= 5
					numerals += num * "I"

					return numerals




def 					_lowercase     (				a_				:					str = "/p089_roman.txt"				)				->	int:




					'''simple docstring'''







					__magic_name__							=    0

					with open(os.path.dirname(a_				) + roman_numerals_filename				) as filea:
										__magic_name__							=    filea.readlines()

					for line in lines:
										__magic_name__							=    line.strip()
										__magic_name__							=    parse_roman_numerals(a_				)
										__magic_name__							=    generate_roman_numerals(a_				)
										savings += len(a_				) - len(a_				)

					return savings


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




