






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


__A      =				logging.get_logger(__name__)


__A      =				{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all LED models at https://huggingface.co/models?filter=LED
__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,
}




@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def      __A					(     ):

					'''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      __A					(     _lowercase ):

					'''simple docstring'''







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





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					VOCAB_FILES_NAMES
							A_						  =					PRETRAINED_VOCAB_FILES_MAP
							A_						  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							A_						  =					["input_ids", "attention_mask"]

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

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

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

												with open(__A		,     encoding='''utf-8'''   ) as vocab_handle:
																	_A								= json.load(__A   )
												_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(__A		,     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(__A		,     range(len(__A   )   )   )   )
												_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       __A (  self:   Union[str, Any]   )    ->				str:
												return len(self.encoder   )

							def       __A (  self:   List[Any]   )    ->				Tuple:
												return dict(self.encoder		,     **self.added_tokens_encoder   )

							def       __A (  self:   Union[str, Any]		,     __A:   Union[str, Any]   )    ->				Tuple:
												if token in self.cache:
																	return self.cache[token]
												_A								= tuple(__A   )
												_A								= get_pairs(__A   )

												if not pairs:
																	return token

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

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

							def       __A (  self:   Any		,     __A:   int   )    ->				Dict:
												_A								= []
												for token in re.findall(self.pat		,     __A   ):
																	_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(__A   ).split(''' '''   )   )
												return bpe_tokens

							def       __A (  self:   List[str]		,     __A:   str   )    ->				List[str]:
												return self.encoder.get(__A		,     self.encoder.get(self.unk_token   )   )

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

							def       __A (  self:   Union[str, Any]		,     __A:   str   )    ->				Optional[int]:
												_A								= ''''''.join(__A   )
												_A								= bytearray([self.byte_decoder[c] for c in text]   ).decode('''utf-8'''		,     errors=self.errors   )
												return text

							def       __A (  self:   Union[str, Any]		,     __A:   str		,     __A:   Optional[str] = None   )    ->				Tuple[str]:
												if not os.path.isdir(__A   ):
																	logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
																	return
												_A								= os.path.join(
												    __A		,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
												_A								= os.path.join(
												    __A		,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']   )

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

												_A								= 0
												with open(__A		,     '''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 __A   : 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(__A   ) + '''\n'''   )
																						index += 1

												return vocab_file, merge_file

							def       __A (  self:   List[Any]		,     __A:   List[int]		,     __A:   Optional[List[int]] = None   )    ->				List[int]:
												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       __A (  self:   str		,     __A:   List[int]		,     __A:   Optional[List[int]] = None		,     __A:   bool = False   )    ->				List[int]:
												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=__A		,     token_ids_a=__A		,     already_has_special_tokens=__A   )

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

							def       __A (  self:   Optional[Any]		,     __A:   List[int]		,     __A:   Optional[List[int]] = None   )    ->				List[int]:
												_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       __A (  self:   Dict		,     __A:   List[Any]		,     __A:   Any=False		,     **__A:   Optional[Any]   )    ->				Optional[int]:
												_A								= kwargs.pop('''add_prefix_space'''		,     self.add_prefix_space   )
												if (is_split_into_words or add_prefix_space) and (len(__A   ) > 0 and not text[0].isspace()):
																	_A								= ''' ''' + text
												return (text, kwargs)



							def       __A (  self:   List[Any]		,     __A:   Union[Dict[str, EncodedInput], BatchEncoding]		,     __A:   Optional[int] = None		,     __A:   PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,     __A:   Optional[int] = None		,     __A:   Optional[bool] = None		,     )    ->				dict:
												_A								= 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								= '''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(__A   )

																	if needs_to_be_padded:
																						_A								= 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								= (
																											    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











from __future__ import annotations




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):  # noqa: E741

					'''simple docstring'''







					while r - l > 1:
										_A								= (l + r) // 2
										if v[m] >= key:
															_A								= m
										else:
															_A								= m  # noqa: E741
					return r




def      __A					(     _lowercase ):

					'''simple docstring'''







					if len(_lowercase ) == 0:
										return 0

					_A								= [0] * len(_lowercase )
					_A								= 1

					_A								= v[0]

					for i in range(1					,			len(_lowercase ) ):
										if v[i] < tail[0]:
															_A								= v[i]
										elif v[i] > tail[length - 1]:
															_A								= v[i]
															length += 1
										else:
															_A								= v[i]

					return length


if __name__ == "__main__":
				import doctest

				doctest.testmod()



