
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
		import PIL

lowercase  :					Optional[Any]													=       logging.get_logger(__name__)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any])					->    int:






							'''simple docstring'''





							if isinstance(_lowerCamelCase						,  (list, tuple)) and isinstance(videos[0]						,  (list, tuple)) and is_valid_image(videos[0][0]):
														return videos

							elif isinstance(_lowerCamelCase						,  (list, tuple)) and is_valid_image(videos[0]):
														return [videos]

							elif is_valid_image(_lowerCamelCase):
														return [[videos]]

							raise ValueError(F'Could not make batched video from {videos}')




class  lowerCamelCase__							(				lowerCamelCase__):




	'''simple docstring'''




	_A								=     ['''pixel_values''']







	def __init__(					self		:str     ,   a		:bool = True     ,   a		:Dict[str, int] = None     ,   a		:PILImageResampling = PILImageResampling.BILINEAR     ,   a		:bool = True     ,   a		:Dict[str, int] = None     ,   a		:bool = True     ,   a		:Union[int, float] = 1 / 2_5_5     ,   a		:bool = True     ,   a		:bool = True     ,   a		:Optional[Union[float, List[float]]] = None     ,   a		:Optional[Union[float, List[float]]] = None     ,   **a		:Dict     ,   )		-> Any:
								super().__init__(**lowerCAmelCase__						)
								__UpperCamelCase	:   Dict			   =					size if size is not None else {"""shortest_edge""": 2_5_6}
								__UpperCamelCase	:   str			   =					get_size_dict(lowerCAmelCase__     ,   default_to_square=lowerCAmelCase__						)
								__UpperCamelCase	:   Any			   =					crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
								__UpperCamelCase	:   List[Any]			   =					get_size_dict(lowerCAmelCase__     ,   param_name="crop_size"						)

								__UpperCamelCase	:   Union[str, Any]			   =					do_resize
								__UpperCamelCase	:   Optional[Any]			   =					size
								__UpperCamelCase	:   Optional[int]			   =					do_center_crop
								__UpperCamelCase	:   List[str]			   =					crop_size
								__UpperCamelCase	:   Union[str, Any]			   =					resample
								__UpperCamelCase	:   Union[str, Any]			   =					do_rescale
								__UpperCamelCase	:   Any			   =					rescale_factor
								__UpperCamelCase	:   Any			   =					offset
								__UpperCamelCase	:   int			   =					do_normalize
								__UpperCamelCase	:   Any			   =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__UpperCamelCase	:   Any			   =					image_std if image_std is not None else IMAGENET_STANDARD_STD







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:np.ndarray     ,   a		:Dict[str, int]     ,   a		:PILImageResampling = PILImageResampling.BILINEAR     ,   a		:Optional[Union[str, ChannelDimension]] = None     ,   **a		:Any     ,   )		-> int:
								__UpperCamelCase	:   str			   =					get_size_dict(lowerCAmelCase__     ,   default_to_square=lowerCAmelCase__						)
								if "shortest_edge" in size:
															__UpperCamelCase	:   Tuple			   =					get_resize_output_image_size(lowerCAmelCase__     ,   size["shortest_edge"]     ,   default_to_square=lowerCAmelCase__						)
								elif "height" in size and "width" in size:
															__UpperCamelCase	:   str			   =					(size["""height"""], size["""width"""])
								else:
															raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'						)
								return resize(lowerCAmelCase__     ,   size=lowerCAmelCase__     ,   resample=lowerCAmelCase__     ,   data_format=lowerCAmelCase__     ,   **lowerCAmelCase__						)







	def    _lowerCamelCase				(					self		:Any     ,   a		:np.ndarray     ,   a		:Dict[str, int]     ,   a		:Optional[Union[str, ChannelDimension]] = None     ,   **a		:Dict     ,   )		-> int:
								__UpperCamelCase	:   Union[str, Any]			   =					get_size_dict(lowerCAmelCase__						)
								if "height" not in size or "width" not in size:
															raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'						)
								return center_crop(lowerCAmelCase__     ,   size=(size["height"], size["width"])     ,   data_format=lowerCAmelCase__     ,   **lowerCAmelCase__						)







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:np.ndarray     ,   a		:Union[int, float]     ,   a		:bool = True     ,   a		:Optional[Union[str, ChannelDimension]] = None     ,   **a		:List[Any]     ,   )		-> str:
								__UpperCamelCase	:   Union[str, Any]			   =					image.astype(np.floataa						)
								if offset:
															__UpperCamelCase	:   int			   =					image - (scale / 2)
								return rescale(lowerCAmelCase__     ,   scale=lowerCAmelCase__     ,   data_format=lowerCAmelCase__     ,   **lowerCAmelCase__						)







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:np.ndarray     ,   a		:Union[float, List[float]]     ,   a		:Union[float, List[float]]     ,   a		:Optional[Union[str, ChannelDimension]] = None     ,   **a		:Union[str, Any]     ,   )		-> int:
								return normalize(lowerCAmelCase__     ,   mean=lowerCAmelCase__     ,   std=lowerCAmelCase__     ,   data_format=lowerCAmelCase__     ,   **lowerCAmelCase__						)







	def    _lowerCamelCase				(					self		:Dict     ,   a		:ImageInput     ,   a		:bool = None     ,   a		:Dict[str, int] = None     ,   a		:PILImageResampling = None     ,   a		:bool = None     ,   a		:Dict[str, int] = None     ,   a		:bool = None     ,   a		:float = None     ,   a		:bool = None     ,   a		:bool = None     ,   a		:Optional[Union[float, List[float]]] = None     ,   a		:Optional[Union[float, List[float]]] = None     ,   a		:Optional[ChannelDimension] = ChannelDimension.FIRST     ,   )		-> Any:






								if do_resize and size is None or resample is None:
															raise ValueError("Size and resample must be specified if do_resize is True."						)

								if do_center_crop and crop_size is None:
															raise ValueError("Crop size must be specified if do_center_crop is True."						)

								if do_rescale and rescale_factor is None:
															raise ValueError("Rescale factor must be specified if do_rescale is True."						)

								if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("Image mean and std must be specified if do_normalize is True."						)

								if offset and not do_rescale:
															raise ValueError("For offset, do_rescale must also be set to True."						)

								# All transformations expect numpy arrays.
								__UpperCamelCase	:   Union[str, Any]			   =					to_numpy_array(lowerCAmelCase__						)

								if do_resize:
															__UpperCamelCase	:   List[str]			   =					self.resize(image=lowerCAmelCase__     ,   size=lowerCAmelCase__     ,   resample=lowerCAmelCase__						)

								if do_center_crop:
															__UpperCamelCase	:   Optional[int]			   =					self.center_crop(lowerCAmelCase__     ,   size=lowerCAmelCase__						)

								if do_rescale:
															__UpperCamelCase	:   str			   =					self.rescale(image=lowerCAmelCase__     ,   scale=lowerCAmelCase__     ,   offset=lowerCAmelCase__						)

								if do_normalize:
															__UpperCamelCase	:   Optional[int]			   =					self.normalize(image=lowerCAmelCase__     ,   mean=lowerCAmelCase__     ,   std=lowerCAmelCase__						)

								__UpperCamelCase	:   Any			   =					to_channel_dimension_format(lowerCAmelCase__     ,   lowerCAmelCase__						)
								return image







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:ImageInput     ,   a		:bool = None     ,   a		:Dict[str, int] = None     ,   a		:PILImageResampling = None     ,   a		:bool = None     ,   a		:Dict[str, int] = None     ,   a		:bool = None     ,   a		:float = None     ,   a		:bool = None     ,   a		:bool = None     ,   a		:Optional[Union[float, List[float]]] = None     ,   a		:Optional[Union[float, List[float]]] = None     ,   a		:Optional[Union[str, TensorType]] = None     ,   a		:ChannelDimension = ChannelDimension.FIRST     ,   **a		:Tuple     ,   )		-> Optional[Any]:
								__UpperCamelCase	:   Tuple			   =					do_resize if do_resize is not None else self.do_resize
								__UpperCamelCase	:   Dict			   =					resample if resample is not None else self.resample
								__UpperCamelCase	:   Dict			   =					do_center_crop if do_center_crop is not None else self.do_center_crop
								__UpperCamelCase	:   Dict			   =					do_rescale if do_rescale is not None else self.do_rescale
								__UpperCamelCase	:   Dict			   =					rescale_factor if rescale_factor is not None else self.rescale_factor
								__UpperCamelCase	:   List[Any]			   =					offset if offset is not None else self.offset
								__UpperCamelCase	:   Union[str, Any]			   =					do_normalize if do_normalize is not None else self.do_normalize
								__UpperCamelCase	:   Union[str, Any]			   =					image_mean if image_mean is not None else self.image_mean
								__UpperCamelCase	:   Any			   =					image_std if image_std is not None else self.image_std

								__UpperCamelCase	:   Dict			   =					size if size is not None else self.size
								__UpperCamelCase	:   Optional[Any]			   =					get_size_dict(lowerCAmelCase__     ,   default_to_square=lowerCAmelCase__						)
								__UpperCamelCase	:   Any			   =					crop_size if crop_size is not None else self.crop_size
								__UpperCamelCase	:   Optional[Any]			   =					get_size_dict(lowerCAmelCase__     ,   param_name="crop_size"						)

								if not valid_images(lowerCAmelCase__						):
															raise ValueError(
															    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
															    "torch.Tensor, tf.Tensor or jax.ndarray."						)

								__UpperCamelCase	:   List[str]			   =					make_batched(lowerCAmelCase__						)

								__UpperCamelCase	:   Union[str, Any]			   =					[
								    [
								        self._preprocess_image(
								            image=lowerCAmelCase__     ,   do_resize=lowerCAmelCase__     ,   size=lowerCAmelCase__     ,   resample=lowerCAmelCase__     ,   do_center_crop=lowerCAmelCase__     ,   crop_size=lowerCAmelCase__     ,   do_rescale=lowerCAmelCase__     ,   rescale_factor=lowerCAmelCase__     ,   offset=lowerCAmelCase__     ,   do_normalize=lowerCAmelCase__     ,   image_mean=lowerCAmelCase__     ,   image_std=lowerCAmelCase__     ,   data_format=lowerCAmelCase__     ,   )
								        for img in video
								    ]
								    for video in videos
								]

								__UpperCamelCase	:   Dict			   =					{"""pixel_values""": videos}
								return BatchFeature(data=lowerCAmelCase__     ,   tensor_type=lowerCAmelCase__						)




'''simple docstring'''




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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__       :	Optional[int]    = logging.get_logger(__name__)


UpperCamelCase__       :	Optional[int]    = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase__       :	Optional[int]    = {
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
}

UpperCamelCase__       :	List[Any]    = {
    '''facebook/bart-base''': 10_24,
    '''facebook/bart-large''': 10_24,
    '''facebook/bart-large-mnli''': 10_24,
    '''facebook/bart-large-cnn''': 10_24,
    '''facebook/bart-large-xsum''': 10_24,
    '''yjernite/bart_eli5''': 10_24,
}


@lru_cache()
def 				lowerCAmelCase_   (	):
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		(
		    list(range(ord("""!"""					)       ,      ord("""~"""					) + 1					)					) + list(range(ord("""¡"""					)       ,      ord("""¬"""					) + 1					)					) + list(range(ord("""®"""					)       ,      ord("""ÿ"""					) + 1					)					)
		)
		__SCREAMING_SNAKE_CASE       :		int           =		bs[:]
		__SCREAMING_SNAKE_CASE       :		Any           =		0
		for b in range(2**8					):
				if b not in bs:
						bs.append(_lowerCamelCase					)
						cs.append(2**8 + n					)
						n += 1
		__SCREAMING_SNAKE_CASE       :		Dict           =		[chr(_lowerCamelCase					) for n in cs]
		return dict(zip(_lowerCamelCase       ,      _lowerCamelCase					)					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		List[str]           =		set()
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)					)
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		char
		return pairs






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		List[str]			    =		VOCAB_FILES_NAMES
			_A      :		Tuple			    =		PRETRAINED_VOCAB_FILES_MAP
			_A      :		Optional[int]			    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_A      :		int			    =		['''input_ids''', '''attention_mask''']

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		AddedToken(lowerCAmelCase__							,     lstrip=lowerCAmelCase__							,     rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else bos_token
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		AddedToken(lowerCAmelCase__							,     lstrip=lowerCAmelCase__							,     rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else eos_token
					__SCREAMING_SNAKE_CASE       :		Tuple           =		AddedToken(lowerCAmelCase__							,     lstrip=lowerCAmelCase__							,     rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else sep_token
					__SCREAMING_SNAKE_CASE       :		Tuple           =		AddedToken(lowerCAmelCase__							,     lstrip=lowerCAmelCase__							,     rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else cls_token
					__SCREAMING_SNAKE_CASE       :		Dict           =		AddedToken(lowerCAmelCase__							,     lstrip=lowerCAmelCase__							,     rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else unk_token
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		AddedToken(lowerCAmelCase__							,     lstrip=lowerCAmelCase__							,     rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else pad_token

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

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

					with open(lowerCAmelCase__							,     encoding="""utf-8"""						) as vocab_handle:
							__SCREAMING_SNAKE_CASE       :		Dict           =		json.load(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		{v: k for k, v in self.encoder.items()}
					__SCREAMING_SNAKE_CASE       :		Dict           =		errors  # how to handle errors in decoding
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		bytes_to_unicode()
					__SCREAMING_SNAKE_CASE       :		Tuple           =		{v: k for k, v in self.byte_encoder.items()}
					with open(lowerCAmelCase__							,     encoding="""utf-8"""						) as merges_handle:
							__SCREAMING_SNAKE_CASE       :		Tuple           =		merges_handle.read().split("""\n"""						)[1:-1]
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[tuple(merge.split()						) for merge in bpe_merges]
					__SCREAMING_SNAKE_CASE       :		str           =		dict(zip(lowerCAmelCase__							,     range(len(lowerCAmelCase__						)						)						)						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		{}
					__SCREAMING_SNAKE_CASE       :		Any           =		add_prefix_space

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

			@property
			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					return len(self.encoder						)

			def UpperCamelCase__					(							self     : List[str]						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : Any							,     lowerCAmelCase__     : Tuple						):





					"""simple docstring"""






					if token in self.cache:
							return self.cache[token]
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		tuple(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		get_pairs(lowerCAmelCase__						)

					if not pairs:
							return token

					while True:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		min(lowerCAmelCase__							,     key=lambda lowerCAmelCase__						: self.bpe_ranks.get(lowerCAmelCase__							,     float("""inf"""						)						)						)
							if bigram not in self.bpe_ranks:
									break
							__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		bigram
							__SCREAMING_SNAKE_CASE       :		int           =		[]
							__SCREAMING_SNAKE_CASE       :		List[str]           =		0
							while i < len(lowerCAmelCase__						):
									try:
											__SCREAMING_SNAKE_CASE       :		Any           =		word.index(lowerCAmelCase__							,     lowerCAmelCase__						)
									except ValueError:
											new_word.extend(word[i:]						)
											break
									else:
											new_word.extend(word[i:j]						)
											__SCREAMING_SNAKE_CASE       :		Dict           =		j

									if word[i] == first and i < len(lowerCAmelCase__						) - 1 and word[i + 1] == second:
											new_word.append(first + second						)
											i += 2
									else:
											new_word.append(word[i]						)
											i += 1
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		tuple(lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		new_word
							if len(lowerCAmelCase__						) == 1:
									break
							else:
									__SCREAMING_SNAKE_CASE       :		Tuple           =		get_pairs(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		""" """.join(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		word
					return word

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[]
					for token in re.findall(self.pat							,     lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		"""""".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(lowerCAmelCase__						).split(""" """						)						)
					return bpe_tokens

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : Tuple						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : Any							,     lowerCAmelCase__     : Tuple						):





					"""simple docstring"""






					return self.decoder.get(lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		"""""".join(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		bytearray([self.byte_decoder[c] for c in text]						).decode("""utf-8"""							,     errors=self.errors						)
					return text

			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Optional[str] = None						):





					"""simple docstring"""






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

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

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		0
					with open(lowerCAmelCase__							,     """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 lowerCAmelCase__						: 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!"""						)
											__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		token_index
									writer.write(""" """.join(lowerCAmelCase__						) + """\n"""						)
									index += 1

					return vocab_file, merge_file

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : List[int]							,     lowerCAmelCase__     : Optional[List[int]] = None						):





					"""simple docstring"""






					if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		[self.cls_token_id]
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep

			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : List[int]							,     lowerCAmelCase__     : Optional[List[int]] = None							,     lowerCAmelCase__     : bool = False						):





					"""simple docstring"""






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

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

			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : List[int]							,     lowerCAmelCase__     : Optional[List[int]] = None						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		[self.sep_token_id]
					__SCREAMING_SNAKE_CASE       :		Dict           =		[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     : Dict							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Dict=False							,     **lowerCAmelCase__     : List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		kwargs.pop("""add_prefix_space"""							,     self.add_prefix_space						)
					if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__						) > 0 and not text[0].isspace()):
							__SCREAMING_SNAKE_CASE       :		List[str]           =		""" """ + text
					return (text, kwargs)