



import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__snake_case     :Dict			       =logging.get_logger(__name__)


__snake_case     :Dict			       ={"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all LED models at https://huggingface.co/models?filter=LED
__snake_case     :List[str]			       ={
    "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     :Optional[Any]			       ={
    "allenai/led-base-16384": 16384,
}






@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def  lowerCamelCase_	(      )       ->							Optional[Any]:

							'''simple docstring'''






							A      						=    (
							    list(range(ord('!'				)			,	ord('~'				) + 1				)				) + list(range(ord('¡'				)			,	ord('¬'				) + 1				)				) + list(range(ord('®'				)			,	ord('ÿ'				) + 1				)				)
							)
							A      						=    bs[:]
							A      						=    0
							for b in range(2**8				):
														if b not in bs:
																					bs.append(__lowercase				)
																					cs.append(2**8 + n				)
																					n += 1
							A      						=    [chr(__lowercase				) for n in cs]
							return dict(zip(__lowercase			,	__lowercase				)				)






def  lowerCamelCase_	(      lowerCAmelCase__	:	Union[str, Any]				)       ->							Union[str, Any]:

							'''simple docstring'''






							A      						=    set()
							A      						=    word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)				)
														A      						=    char
							return pairs



class        lowerCAmelCase__					(  lowercase_       ):
				A_   :     str								=     VOCAB_FILES_NAMES
				A_   :     Optional[Any]								=     PRETRAINED_VOCAB_FILES_MAP
				A_   :     Optional[Any]								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A_   :     List[str]								=     ['input_ids', 'attention_mask']


				def __init__(							self      :  Optional[Any]						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  Optional[Any]="replace"						, __UpperCamelCase      :  Tuple="<s>"						, __UpperCamelCase      :  Union[str, Any]="</s>"						, __UpperCamelCase      :  Optional[int]="</s>"						, __UpperCamelCase      :  List[Any]="<s>"						, __UpperCamelCase      :  List[str]="<unk>"						, __UpperCamelCase      :  str="<pad>"						, __UpperCamelCase      :  int="<mask>"						, __UpperCamelCase      :  Union[str, Any]=False						, **__UpperCamelCase      :  List[str]						, )		->     Any:
											A      						=    AddedToken(UpperCamelCase_						, lstrip=UpperCamelCase_						, rstrip=UpperCamelCase_       ) if isinstance(UpperCamelCase_						, UpperCamelCase_       ) else bos_token
											A      						=    AddedToken(UpperCamelCase_						, lstrip=UpperCamelCase_						, rstrip=UpperCamelCase_       ) if isinstance(UpperCamelCase_						, UpperCamelCase_       ) else eos_token
											A      						=    AddedToken(UpperCamelCase_						, lstrip=UpperCamelCase_						, rstrip=UpperCamelCase_       ) if isinstance(UpperCamelCase_						, UpperCamelCase_       ) else sep_token
											A      						=    AddedToken(UpperCamelCase_						, lstrip=UpperCamelCase_						, rstrip=UpperCamelCase_       ) if isinstance(UpperCamelCase_						, UpperCamelCase_       ) else cls_token
											A      						=    AddedToken(UpperCamelCase_						, lstrip=UpperCamelCase_						, rstrip=UpperCamelCase_       ) if isinstance(UpperCamelCase_						, UpperCamelCase_       ) else unk_token
											A      						=    AddedToken(UpperCamelCase_						, lstrip=UpperCamelCase_						, rstrip=UpperCamelCase_       ) if isinstance(UpperCamelCase_						, UpperCamelCase_       ) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											A      						=    AddedToken(UpperCamelCase_						, lstrip=UpperCamelCase_						, rstrip=UpperCamelCase_       ) if isinstance(UpperCamelCase_						, UpperCamelCase_       ) else mask_token

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

											with open(UpperCamelCase_						, encoding='utf-8'       ) as vocab_handle:
																		A      						=    json.load(UpperCamelCase_       )
											A      						=    {v: k for k, v in self.encoder.items()}
											A      						=    errors  # how to handle errors in decoding
											A      						=    bytes_to_unicode()
											A      						=    {v: k for k, v in self.byte_encoder.items()}
											with open(UpperCamelCase_						, encoding='utf-8'       ) as merges_handle:
																		A      						=    merges_handle.read().split('\n'       )[1:-1]
											A      						=    [tuple(merge.split()       ) for merge in bpe_merges]
											A      						=    dict(zip(UpperCamelCase_						, range(len(UpperCamelCase_       )       )       )       )
											A      						=    {}
											A      						=    add_prefix_space

											# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
											A      						=    re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'       )


				@property
				# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
				def __UpperCamelCase						(							self      :  Tuple       )		->     Union[str, Any]:
											return len(self.encoder       )


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     int:
											return dict(self.encoder						, **self.added_tokens_encoder       )


				def __UpperCamelCase						(							self      :  Dict						, __UpperCamelCase      :  Any       )		->     Optional[int]:



											if token in self.cache:
																		return self.cache[token]
											A      						=    tuple(UpperCamelCase_       )
											A      						=    get_pairs(UpperCamelCase_       )

											if not pairs:
																		return token

											while True:
																		A      						=    min(UpperCamelCase_						, key=lambda __UpperCamelCase       : self.bpe_ranks.get(UpperCamelCase_						, float('inf'       )       )       )
																		if bigram not in self.bpe_ranks:
																									break
																		A						,   A      						=    bigram
																		A      						=    []
																		A      						=    0
																		while i < len(UpperCamelCase_       ):
																									try:
																																A      						=    word.index(UpperCamelCase_						, UpperCamelCase_       )
																									except ValueError:
																																new_word.extend(word[i:]       )
																																break
																									else:
																																new_word.extend(word[i:j]       )
																																A      						=    j

																									if word[i] == first and i < len(UpperCamelCase_       ) - 1 and word[i + 1] == second:
																																new_word.append(first + second       )
																																i += 2
																									else:
																																new_word.append(word[i]       )
																																i += 1
																		A      						=    tuple(UpperCamelCase_       )
																		A      						=    new_word
																		if len(UpperCamelCase_       ) == 1:
																									break
																		else:
																									A      						=    get_pairs(UpperCamelCase_       )
											A      						=    ' '.join(UpperCamelCase_       )
											A      						=    word
											return word


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  str       )		->     Optional[Any]:
											A      						=    []
											for token in re.findall(self.pat						, UpperCamelCase_       ):
																		A      						=    ''.join(
																		    self.byte_encoder[b] for b in token.encode('utf-8'       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																		bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase_       ).split(' '       )       )
											return bpe_tokens


				def __UpperCamelCase						(							self      :  Any						, __UpperCamelCase      :  Any       )		->     Union[str, Any]:
											return self.encoder.get(UpperCamelCase_						, self.encoder.get(self.unk_token       )       )


				def __UpperCamelCase						(							self      :  List[Any]						, __UpperCamelCase      :  str       )		->     List[str]:
											return self.decoder.get(UpperCamelCase_       )


				def __UpperCamelCase						(							self      :  Tuple						, __UpperCamelCase      :  List[Any]       )		->     str:
											A      						=    ''.join(UpperCamelCase_       )
											A      						=    bytearray([self.byte_decoder[c] for c in text]       ).decode('utf-8'						, errors=self.errors       )
											return text


				def __UpperCamelCase						(							self      :  str						, __UpperCamelCase      :  str						, __UpperCamelCase      :  Optional[str] = None       )		->     Tuple:



											if not os.path.isdir(UpperCamelCase_       ):
																		logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
																		return
											A      						=    os.path.join(
											    UpperCamelCase_						, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )
											A      						=    os.path.join(
											    UpperCamelCase_						, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']       )

											with open(UpperCamelCase_						, 'w'						, encoding='utf-8'       ) as f:
																		f.write(json.dumps(self.encoder						, indent=2						, sort_keys=UpperCamelCase_						, ensure_ascii=UpperCamelCase_       ) + '\n'       )

											A      						=    0
											with open(UpperCamelCase_						, 'w'						, encoding='utf-8'       ) as writer:
																		writer.write('#version: 0.2\n'       )
																		for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						, key=lambda __UpperCamelCase       : kv[1]       ):
																									if index != token_index:
																																logger.warning(
																																    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																																    ' Please check that the tokenizer is not corrupted!'       )
																																A      						=    token_index
																									writer.write(' '.join(UpperCamelCase_       ) + '\n'       )
																									index += 1

											return vocab_file, merge_file


				def __UpperCamelCase						(							self      :  Dict						, __UpperCamelCase      :  List[int]						, __UpperCamelCase      :  Optional[List[int]] = None       )		->     Union[str, Any]:



											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											A      						=    [self.cls_token_id]
											A      						=    [self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep


				def __UpperCamelCase						(							self      :  List[Any]						, __UpperCamelCase      :  List[int]						, __UpperCamelCase      :  Optional[List[int]] = None						, __UpperCamelCase      :  bool = False       )		->     List[str]:



											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=UpperCamelCase_						, token_ids_a=UpperCamelCase_						, already_has_special_tokens=UpperCamelCase_       )

											if token_ids_a is None:
																		return [1] + ([0] * len(UpperCamelCase_       )) + [1]
											return [1] + ([0] * len(UpperCamelCase_       )) + [1, 1] + ([0] * len(UpperCamelCase_       )) + [1]


				def __UpperCamelCase						(							self      :  Optional[int]						, __UpperCamelCase      :  List[int]						, __UpperCamelCase      :  Optional[List[int]] = None       )		->     str:
											A      						=    [self.sep_token_id]
											A      						=    [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 __UpperCamelCase						(							self      :  List[Any]						, __UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  Union[str, Any]=False						, **__UpperCamelCase      :  Optional[int]       )		->     Optional[Any]:
											A      						=    kwargs.pop('add_prefix_space'						, self.add_prefix_space       )
											if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase_       ) > 0 and not text[0].isspace()):
																		A      						=    ' ' + text
											return (text, kwargs)


				def __UpperCamelCase						(							self      :  Any						, __UpperCamelCase      :  Union[Dict[str, EncodedInput], BatchEncoding]						, __UpperCamelCase      :  Optional[int] = None						, __UpperCamelCase      :  PaddingStrategy = PaddingStrategy.DO_NOT_PAD						, __UpperCamelCase      :  Optional[int] = None						, __UpperCamelCase      :  Optional[bool] = None						, )		->     Dict:
											A      						=    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:
																		A      						=    'attention_mask' in self.model_input_names

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

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

											return encoded_inputs







import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__a   :							Union[str, Any]						= logging.get_logger(__name__)

__a   :							List[str]						= {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__a   :							Dict						= {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

__a   :							List[str]						= {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE     =					PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE     =					["input_ids", "attention_mask"]
     SCREAMING_SNAKE_CASE     =					RobertaTokenizer




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


         """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_			,						)

         __A       =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
         if pre_tok_state.get("""add_prefix_space"""			,						UpperCamelCase_					) != add_prefix_space:
             __A       =						getattr(UpperCamelCase_			,						pre_tok_state.pop("""type"""					)					)
             __A       =						add_prefix_space
             __A       =						pre_tok_class(**UpperCamelCase_					)

         __A       =						add_prefix_space

         __A       =						"""post_processor"""
         __A       =						getattr(self.backend_tokenizer			,						UpperCamelCase_			,						UpperCamelCase_					)
         if tokenizer_component_instance:
             __A       =						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(state["""sep"""]					)
             if "cls" in state:
                 __A       =						tuple(state["""cls"""]					)

             __A       =						False

             if state.get("""add_prefix_space"""			,						UpperCamelCase_					) != add_prefix_space:
                 __A       =						add_prefix_space
                 __A       =						True

             if state.get("""trim_offsets"""			,						UpperCamelCase_					) != trim_offsets:
                 __A       =						trim_offsets
                 __A       =						True

             if changes_to_apply:
                 __A       =						getattr(UpperCamelCase_			,						state.pop("""type"""					)					)
                 __A       =						component_class(**UpperCamelCase_					)
                 setattr(self.backend_tokenizer			,						UpperCamelCase_			,						UpperCamelCase_					)




     @property
     def 		lowerCAmelCase_  (					self							:      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 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						AddedToken(UpperCamelCase_			,						lstrip=UpperCamelCase_			,						rstrip=UpperCamelCase_					) if isinstance(UpperCamelCase_			,						UpperCamelCase_					) else value
         __A       =						value




     def 		lowerCAmelCase_  (					self							:      Tuple			,						*UpperCamelCase_							:      Optional[Any]			,						**UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						kwargs.get("""is_split_into_words"""			,						UpperCamelCase_					)
         assert self.add_prefix_space or not is_split_into_words, (
             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 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						*UpperCamelCase_							:      Optional[int]			,						**UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						kwargs.get("""is_split_into_words"""			,						UpperCamelCase_					)

         assert self.add_prefix_space or not is_split_into_words, (
             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 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[str] = None					):


         """simple docstring"""



         __A       =						self._tokenizer.model.save(UpperCamelCase_			,						name=UpperCamelCase_					)
         return tuple(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      int=None					):


         """simple docstring"""



         __A       =						[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							:      int			,						UpperCamelCase_							:      List[int]			,						UpperCamelCase_							:      Optional[List[int]] = None					):


         """simple docstring"""



         __A       =						[self.sep_token_id]
         __A       =						[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]




