

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


UpperCAmelCase_							:					Any		 =   logging.get_logger(__name__)


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

UpperCAmelCase_							:					int		 =   {
    '''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''',
    },
}

UpperCAmelCase_							:					Dict		 =   {
    '''allenai/led-base-16384''': 16_384,
}






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   str     					=				VOCAB_FILES_NAMES
	_lowercase		:   Union[str, Any]     					=				PRETRAINED_VOCAB_FILES_MAP
	_lowercase		:   Optional[int]     					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowercase		:   Dict     					=				LEDTokenizer
	_lowercase		:   List[Any]     					=				['''input_ids''', '''attention_mask''']




	def __init__(     self						:     Optional[Any]					,      __A						:     Dict=None					,      __A						:     Tuple=None					,      __A						:     Optional[int]=None					,      __A						:     Any="replace"					,      __A						:     Any="<s>"					,      __A						:     Any="</s>"					,      __A						:     List[str]="</s>"					,      __A						:     List[Any]="<s>"					,      __A						:     Dict="<unk>"					,      __A						:     Optional[int]="<pad>"					,      __A						:     List[Any]="<mask>"					,      __A						:     List[Any]=False					,      __A						:     Tuple=True					,      **__A						:     Optional[Any]					,      ):
		super().__init__(
		    __A					,      __A					,      tokenizer_file=__A					,      errors=__A					,      bos_token=__A					,      eos_token=__A					,      sep_token=__A					,      cls_token=__A					,      unk_token=__A					,      pad_token=__A					,      mask_token=__A					,      add_prefix_space=__A					,      trim_offsets=__A					,      **__A					,      )

		__A :			List[Any]		    =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
		if pre_tok_state.get("""add_prefix_space"""					,      __A							) != add_prefix_space:
			__A :			int		    =			getattr(__A					,      pre_tok_state.pop("""type"""							)							)
			__A :			Optional[Any]		    =			add_prefix_space
			__A :			Optional[int]		    =			pre_tok_class(**__A							)

		__A :			Optional[Any]		    =			add_prefix_space

		# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
		__A :			Optional[int]		    =			"""post_processor"""
		__A :			Tuple		    =			getattr(self.backend_tokenizer					,      __A					,      __A							)
		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 :			Dict		    =			tuple(state["""sep"""]							)
			if "cls" in state:
				__A :			Optional[Any]		    =			tuple(state["""cls"""]							)

			__A :			Optional[Any]		    =			False

			if state.get("""add_prefix_space"""					,      __A							) != add_prefix_space:
				__A :			Tuple		    =			add_prefix_space
				__A :			Union[str, Any]		    =			True

			if state.get("""trim_offsets"""					,      __A							) != trim_offsets:
				__A :			int		    =			trim_offsets
				__A :			Optional[Any]		    =			True

			if changes_to_apply:
				__A :			Union[str, Any]		    =			getattr(__A					,      state.pop("""type"""							)							)
				__A :			Union[str, Any]		    =			component_class(**__A							)
				setattr(self.backend_tokenizer					,      __A					,      __A							)




	@property
	# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
	def        lowerCAmelCase_       (     self						:     List[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        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Tuple							):
		__A :			Optional[Any]		    =			AddedToken(__A					,      lstrip=__A					,      rstrip=__A							) if isinstance(__A					,      __A							) else value
		__A :			List[Any]		    =			value




	def        lowerCAmelCase_       (     self						:     int					,      *__A						:     Optional[int]					,      **__A						:     Optional[int]							):
		__A :			Any		    =			kwargs.get("""is_split_into_words"""					,      __A							)

		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(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     List[str]					,      *__A						:     Tuple					,      **__A						:     Optional[Any]							):
		__A :			Tuple		    =			kwargs.get("""is_split_into_words"""					,      __A							)

		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(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     str					,      __A						:     Optional[str] = None							):
		__A :			List[Any]		    =			self._tokenizer.model.save(__A					,      name=__A							)
		return tuple(__A							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     int					,      __A						:     Tuple=None							):
		__A :			Optional[int]		    =			[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        lowerCAmelCase_       (     self						:     str					,      __A						:     List[int]					,      __A						:     Optional[List[int]] = None							):
		__A :			Dict		    =			[self.sep_token_id]
		__A :			List[Any]		    =			[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        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     Union[Dict[str, EncodedInput], BatchEncoding]					,      __A						:     Optional[int] = None					,      __A						:     PaddingStrategy = PaddingStrategy.DO_NOT_PAD					,      __A						:     Optional[int] = None					,      __A						:     Optional[bool] = None					,      ):
		__A :			Optional[Any]		    =			super()._pad(
		    encoded_inputs=__A					,      max_length=__A					,      padding_strategy=__A					,      pad_to_multiple_of=__A					,      return_attention_mask=__A					,      )

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

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

			if needs_to_be_padded:
				__A :			Any		    =			len(__A							) - 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 :			int		    =			(
					    encoded_inputs["""global_attention_mask"""] + [-1] * difference
					)
				elif self.padding_side == "left":
					__A :			List[Any]		    =			[-1] * difference + encoded_inputs[
					    """global_attention_mask"""
					]
				else:
					raise ValueError("""Invalid padding strategy:""" + str(self.padding_side							)							)

		return encoded_inputs










"""simple docstring"""







# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


a_							=    re.compile(r"""^(?P<major>\d+)""" r"""\.(?P<minor>\d+)""" r"""\.(?P<patch>\d+)$""")







@total_ordering
@dataclass
class 					A_:







			"""simple docstring"""



			a_       :			str
			a_       :			Optional[str]												=			None
			a_       :			Optional[Union[str, int]]												=			None
			a_       :			Optional[Union[str, int]]												=			None
			a_       :			Optional[Union[str, int]]												=			None
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase					:					List[str]	       =    _str_to_version_tuple(self.version_str							)
			def __repr__(	self							):
						return F"{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"
			@property
			def    _lowerCAmelCase		(	self							):
						return self.major, self.minor, self.patch
			def    _lowerCAmelCase		(	self				,   A							):
						if isinstance(A				,   A							):
									return Version(A							)
						elif isinstance(A				,   A							):
									return other
						raise TypeError(F"{other} (type {type(A							)}) cannot be compared to version."							)
			def __eq__(	self				,   A							):
						try:
									_lowerCamelCase					:					Dict	       =    self._validate_operand(A							)
						except (TypeError, ValueError):
									return False
						else:
									return self.tuple == other.tuple
			def __lt__(	self				,   A							):
						_lowerCamelCase					:					Any	       =    self._validate_operand(A							)
						return self.tuple < other.tuple
			def __hash__(	self							):
						return hash(_version_tuple_to_str(self.tuple							)							)
			@classmethod
			def    _lowerCAmelCase		(	cls				,   A							):
						_lowerCamelCase					:					Optional[Any]	       =    {f.name for f in dataclasses.fields(cls							)}
						return cls(**{k: v for k, v in dic.items() if k in field_names}							)

			def    _lowerCAmelCase		(	self							):
						return self.version_str






def     UpperCAmelCase_					(				__a   :			Any						):




			'''simple docstring'''



			_lowerCamelCase					:					Union[str, Any]	       =    _VERSION_REG.match(__a						)
			if not res:
						raise ValueError(f"Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits."						)
			return tuple(int(__a						) for v in [res.group('major'						), res.group('minor'						), res.group('patch'						)]						)



def     UpperCAmelCase_					(				__a   :			List[str]						):




			'''simple docstring'''



			return ".".join(str(__a						) for v in version_tuple						)





