
"""simple docstring"""





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


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


UpperCamelCase_       :    Union[str, Any]						      =			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
UpperCamelCase_       :    Tuple						      =			{
    '''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_       :    Tuple						      =			{
    '''allenai/led-base-16384''': 1_6384,
}
@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(__a					)
															cs.append(2**8 + n					)
															n += 1
					A_								=    [chr(__a					) for n in cs]
					return dict(zip(__a					, __a					)					)
def 				A_  (__a					):

					'''simple docstring'''





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




class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







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




			def __init__(					self    :       Dict							, _snake_case    :       str							, _snake_case    :       str							, _snake_case    :       int="replace"							, _snake_case    :       Optional[Any]="<s>"							, _snake_case    :       Dict="</s>"							, _snake_case    :       Any="</s>"							, _snake_case    :       str="<s>"							, _snake_case    :       str="<unk>"							, _snake_case    :       Optional[int]="<pad>"							, _snake_case    :       Dict="<mask>"							, _snake_case    :       List[Any]=False							, **_snake_case    :       Any							, )   ->			Any:







								"""simple docstring"""
								A_								=    AddedToken(_snake_case							, lstrip=_snake_case							, rstrip=_snake_case						) if isinstance(_snake_case							, _snake_case						) else bos_token
								A_								=    AddedToken(_snake_case							, lstrip=_snake_case							, rstrip=_snake_case						) if isinstance(_snake_case							, _snake_case						) else eos_token
								A_								=    AddedToken(_snake_case							, lstrip=_snake_case							, rstrip=_snake_case						) if isinstance(_snake_case							, _snake_case						) else sep_token
								A_								=    AddedToken(_snake_case							, lstrip=_snake_case							, rstrip=_snake_case						) if isinstance(_snake_case							, _snake_case						) else cls_token
								A_								=    AddedToken(_snake_case							, lstrip=_snake_case							, rstrip=_snake_case						) if isinstance(_snake_case							, _snake_case						) else unk_token
								A_								=    AddedToken(_snake_case							, lstrip=_snake_case							, rstrip=_snake_case						) if isinstance(_snake_case							, _snake_case						) else pad_token

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

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

								with open(_snake_case							, encoding="utf-8"						) as vocab_handle:
													A_								=    json.load(_snake_case						)
								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(_snake_case							, 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(_snake_case							, range(len(_snake_case						)						)						)						)
								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   lowerCamelCase__  (					self    :       List[str]						)   ->			List[str]:







								"""simple docstring"""
								return len(self.encoder						)




			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			Dict:







								"""simple docstring"""
								return dict(self.encoder							, **self.added_tokens_encoder						)




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       List[str]						)   ->			str:







								"""simple docstring"""
								if token in self.cache:
													return self.cache[token]
								A_								=    tuple(_snake_case						)
								A_								=    get_pairs(_snake_case						)

								if not pairs:
													return token

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

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




			def   lowerCamelCase__  (					self    :       str							, _snake_case    :       List[str]						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    []
								for token in re.findall(self.pat							, _snake_case						):
													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(_snake_case						).split(" "						)						)
								return bpe_tokens




			def   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       int						)   ->			List[Any]:







								"""simple docstring"""
								return self.encoder.get(_snake_case							, self.encoder.get(self.unk_token						)						)




			def   lowerCamelCase__  (					self    :       Any							, _snake_case    :       Optional[Any]						)   ->			Tuple:







								"""simple docstring"""
								return self.decoder.get(_snake_case						)




			def   lowerCamelCase__  (					self    :       str							, _snake_case    :       str						)   ->			List[str]:







								"""simple docstring"""
								A_								=    "".join(_snake_case						)
								A_								=    bytearray([self.byte_decoder[c] for c in text]						).decode("utf-8"							, errors=self.errors						)
								return text




			def   lowerCamelCase__  (					self    :       Optional[int]							, _snake_case    :       str							, _snake_case    :       Optional[str] = None						)   ->			Tuple[str]:







								"""simple docstring"""
								if not os.path.isdir(_snake_case						):
													logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
													return
								A_								=    os.path.join(
								    _snake_case							, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
								A_								=    os.path.join(
								    _snake_case							, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]						)

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

								A_								=    0
								with open(_snake_case							, "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 _snake_case						: 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(_snake_case						) + "\n"						)
																		index += 1

								return vocab_file, merge_file




			def   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       List[int]							, _snake_case    :       Optional[List[int]] = None						)   ->			List[int]:







								"""simple docstring"""
								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   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       List[int]							, _snake_case    :       Optional[List[int]] = None							, _snake_case    :       bool = False						)   ->			List[int]:







								"""simple docstring"""
								if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_snake_case							, token_ids_a=_snake_case							, already_has_special_tokens=_snake_case						)

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




			def   lowerCamelCase__  (					self    :       List[str]							, _snake_case    :       List[int]							, _snake_case    :       Optional[List[int]] = None						)   ->			List[int]:







								"""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]




			def   lowerCamelCase__  (					self    :       Any							, _snake_case    :       List[str]							, _snake_case    :       Any=False							, **_snake_case    :       Dict						)   ->			Any:







								"""simple docstring"""
								A_								=    kwargs.pop("add_prefix_space"							, self.add_prefix_space						)
								if (is_split_into_words or add_prefix_space) and (len(_snake_case						) > 0 and not text[0].isspace()):
													A_								=    " " + text
								return (text, kwargs)






			def   lowerCamelCase__  (					self    :       Union[str, 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:







								"""simple docstring"""
								A_								=    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:
													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(_snake_case						)

													if needs_to_be_padded:
																		A_								=    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`
																							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






"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_       :    Dict						      =			{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    int						      =			['''PLBartTokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    Optional[int]						      =			[
		    '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PLBartForCausalLM''',
		    '''PLBartForConditionalGeneration''',
		    '''PLBartForSequenceClassification''',
		    '''PLBartModel''',
		    '''PLBartPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_plbart import PLBartTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_plbart import (
				    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PLBartForCausalLM,
				    PLBartForConditionalGeneration,
				    PLBartForSequenceClassification,
				    PLBartModel,
				    PLBartPreTrainedModel,
				)


else:
		import sys

		UpperCamelCase_       :    Optional[int]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure)




