
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


lowercase__							:							Any									=			logging.get_logger(__name__)


lowercase__							:							str									=			{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all LED models at https://huggingface.co/models?filter=LED
lowercase__							:							Any									=			{
    "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",
    },
}

lowercase__							:							Tuple									=			{
    "allenai/led-base-16384": 1_6_3_8_4,
}




@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 	A_ (							)      ->    Dict:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Optional[int] )      ->    Optional[int]:



						'''simple docstring'''

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






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""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     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_="replace"     ,	SCREAMING_SNAKE_CASE_="<s>"     ,	SCREAMING_SNAKE_CASE_="</s>"     ,	SCREAMING_SNAKE_CASE_="</s>"     ,	SCREAMING_SNAKE_CASE_="<s>"     ,	SCREAMING_SNAKE_CASE_="<unk>"     ,	SCREAMING_SNAKE_CASE_="<pad>"     ,	SCREAMING_SNAKE_CASE_="<mask>"     ,	SCREAMING_SNAKE_CASE_=False     ,	**SCREAMING_SNAKE_CASE_     ,	)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			AddedToken(SCREAMING_SNAKE_CASE_     ,	lstrip=SCREAMING_SNAKE_CASE_     ,	rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) else bos_token
													__UpperCamelCase       =			AddedToken(SCREAMING_SNAKE_CASE_     ,	lstrip=SCREAMING_SNAKE_CASE_     ,	rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) else eos_token
													__UpperCamelCase       =			AddedToken(SCREAMING_SNAKE_CASE_     ,	lstrip=SCREAMING_SNAKE_CASE_     ,	rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) else sep_token
													__UpperCamelCase       =			AddedToken(SCREAMING_SNAKE_CASE_     ,	lstrip=SCREAMING_SNAKE_CASE_     ,	rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) else cls_token
													__UpperCamelCase       =			AddedToken(SCREAMING_SNAKE_CASE_     ,	lstrip=SCREAMING_SNAKE_CASE_     ,	rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) else unk_token
													__UpperCamelCase       =			AddedToken(SCREAMING_SNAKE_CASE_     ,	lstrip=SCREAMING_SNAKE_CASE_     ,	rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) else pad_token

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

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

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

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													__UpperCamelCase       =			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			)->   Tuple:





													'''simple docstring'''






													return len(self.encoder			)



							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													return dict(self.encoder     ,	**self.added_tokens_encoder			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   Tuple:





													'''simple docstring'''






													if token in self.cache:
																			return self.cache[token]
													__UpperCamelCase       =			tuple(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			get_pairs(SCREAMING_SNAKE_CASE_			)

													if not pairs:
																			return token

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

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



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			[]
													for token in re.findall(self.pat     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			''''''.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(SCREAMING_SNAKE_CASE_			).split(''' '''			)			)
													return bpe_tokens



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   Tuple:





													'''simple docstring'''






													return self.encoder.get(SCREAMING_SNAKE_CASE_     ,	self.encoder.get(self.unk_token			)			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   Any:





													'''simple docstring'''






													return self.decoder.get(SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       =			''''''.join(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			bytearray([self.byte_decoder[c] for c in text]			).decode('''utf-8'''     ,	errors=self.errors			)
													return text



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None			)->   Tuple[str]:





													'''simple docstring'''






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

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

													__UpperCamelCase       =			0
													with open(SCREAMING_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 SCREAMING_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!'''			)
																															__UpperCamelCase       =			token_index
																									writer.write(''' '''.join(SCREAMING_SNAKE_CASE_			) + '''\n'''			)
																									index += 1

													return vocab_file, merge_file



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None			)->   List[int]:





													'''simple docstring'''






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



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = False			)->   List[int]:





													'''simple docstring'''






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

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



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None			)->   List[int]:





													'''simple docstring'''






													__UpperCamelCase       =			[self.sep_token_id]
													__UpperCamelCase       =			[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     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=False     ,	**SCREAMING_SNAKE_CASE_			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			kwargs.pop('''add_prefix_space'''     ,	self.add_prefix_space			)
													if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_			) > 0 and not text[0].isspace()):
																			__UpperCamelCase       =			''' ''' + text
													return (text, kwargs)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = PaddingStrategy.DO_NOT_PAD     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	)->   dict:





													'''simple docstring'''






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

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

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

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

													return encoded_inputs






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__							:							Dict									=			{"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			["ViTFeatureExtractor"]
				lowercase__							:							str									=			["ViTImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							List[str]									=			[
				    "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ViTForImageClassification",
				    "ViTForMaskedImageModeling",
				    "ViTModel",
				    "ViTPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			[
				    "TFViTForImageClassification",
				    "TFViTModel",
				    "TFViTPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							List[Any]									=			[
				    "FlaxViTForImageClassification",
				    "FlaxViTModel",
				    "FlaxViTPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_vit import ViTFeatureExtractor
								from .image_processing_vit import ViTImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vit import (
								    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ViTForImageClassification,
								    ViTForMaskedImageModeling,
								    ViTModel,
								    ViTPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
				import sys

				lowercase__							:							List[Any]									=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




