






'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


UpperCamelCase     :   List[Any]	      =		logging.get_logger(__name__)

UpperCamelCase     :   str	      =		'▁'

UpperCamelCase     :   Union[str, Any]	      =		{'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

UpperCamelCase     :   Optional[Any]	      =		{
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

UpperCamelCase     :   str	      =		{'vinai/bartpho-syllable': 10_24}




class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





		_UpperCamelCase						=       VOCAB_FILES_NAMES
		_UpperCamelCase						=       PRETRAINED_VOCAB_FILES_MAP
		_UpperCamelCase						=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_UpperCamelCase						=       ['input_ids', 'attention_mask']






		def __init__(   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase="<s>" ,_lowerCAmelCase="</s>" ,_lowerCAmelCase="</s>" ,_lowerCAmelCase="<s>" ,_lowerCAmelCase="<unk>" ,_lowerCAmelCase="<pad>" ,_lowerCAmelCase="<mask>" ,_lowerCAmelCase = None ,**_lowerCAmelCase ,):
								# Mask token behave like a normal word, i.e. include the space before it
								lowerCamelCase__								=	AddedToken(_lowerCAmelCase ,lstrip=_lowerCAmelCase ,rstrip=_lowerCAmelCase							) if isinstance(_lowerCAmelCase ,_lowerCAmelCase							) else mask_token

								lowerCamelCase__								=	{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=_lowerCAmelCase ,eos_token=_lowerCAmelCase ,unk_token=_lowerCAmelCase ,sep_token=_lowerCAmelCase ,cls_token=_lowerCAmelCase ,pad_token=_lowerCAmelCase ,mask_token=_lowerCAmelCase ,sp_model_kwargs=self.sp_model_kwargs ,**_lowerCAmelCase ,)

								lowerCamelCase__								=	vocab_file
								lowerCamelCase__								=	monolingual_vocab_file
								lowerCamelCase__								=	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
								self.sp_model.Load(str(_lowerCAmelCase							)							)

								# Load the reduced vocab

								# Keep order of special tokens for backward compatibility
								lowerCamelCase__								=	{}
								lowerCamelCase__								=	0
								for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
														if str(_lowerCAmelCase							) not in self.fairseq_tokens_to_ids:
																				lowerCamelCase__								=	cnt
																				cnt += 1
								with open(_lowerCAmelCase ,"""r""" ,encoding="""utf-8"""							) as f:
														for line in f.readlines():
																				lowerCamelCase__								=	line.strip().split()[0]
																				lowerCamelCase__								=	len(self.fairseq_tokens_to_ids							)
								if str(_lowerCAmelCase							) not in self.fairseq_tokens_to_ids:
														lowerCamelCase__								=	len(self.fairseq_tokens_to_ids							)

								lowerCamelCase__								=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}






		def __getstate__(   self							):
								lowerCamelCase__								=	self.__dict__.copy()
								lowerCamelCase__								=	None
								lowerCamelCase__								=	self.sp_model.serialized_model_proto()
								return state






		def __setstate__(   self ,_lowerCAmelCase							):
								lowerCamelCase__								=	d

								# for backward compatibility
								if not hasattr(self ,"""sp_model_kwargs"""							):
														lowerCamelCase__								=	{}

								lowerCamelCase__								=	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
								self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase = None							):

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






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase = None ,_lowerCAmelCase = False							):

								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 ,_lowerCAmelCase ,_lowerCAmelCase = None							):
								lowerCamelCase__								=	[self.sep_token_id]
								lowerCamelCase__								=	[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]






		@property
		def       UpperCamelCase_       (   self							):
								return len(self.fairseq_ids_to_tokens							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	{self.convert_ids_to_tokens(_lowerCAmelCase							): i for i in range(self.vocab_size							)}
								vocab.update(self.added_tokens_encoder							)
								return vocab






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								return self.sp_model.encode(_lowerCAmelCase ,out_type=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								if token in self.fairseq_tokens_to_ids:
														return self.fairseq_tokens_to_ids[token]
								else:
														return self.unk_token_id






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								return self.fairseq_ids_to_tokens[index]






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								lowerCamelCase__								=	"""""".join(_lowerCAmelCase							).replace(_lowerCAmelCase ,""" """							).strip()
								return out_string






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase = None							):
								if not os.path.isdir(_lowerCAmelCase							):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
														return
								lowerCamelCase__								=	os.path.join(
								    _lowerCAmelCase ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
								lowerCamelCase__								=	os.path.join(
								    _lowerCAmelCase ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""] ,)

								if os.path.abspath(self.vocab_file							) != os.path.abspath(_lowerCAmelCase							) and os.path.isfile(self.vocab_file							):
														copyfile(self.vocab_file ,_lowerCAmelCase							)
								elif not os.path.isfile(self.vocab_file							):
														with open(_lowerCAmelCase ,"""wb"""							) as fi:
																				lowerCamelCase__								=	self.sp_model.serialized_model_proto()
																				fi.write(_lowerCAmelCase							)

								if os.path.abspath(self.monolingual_vocab_file							) != os.path.abspath(
								    _lowerCAmelCase							) and os.path.isfile(self.monolingual_vocab_file							):
														copyfile(self.monolingual_vocab_file ,_lowerCAmelCase							)
								elif not os.path.isfile(self.monolingual_vocab_file							):
														with open(_lowerCAmelCase ,"""w""" ,encoding="""utf-8"""							) as fp:
																				for token in self.fairseq_tokens_to_ids:
																										if token not in self.all_special_tokens:
																																fp.write(F'''{str(_lowerCAmelCase							)} \n'''							)

								return out_vocab_file, out_monolingual_vocab_file



"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_a								=						logging.get_logger(__name__)






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = ['pixel_values']



		def __init__(   self							,						__a = True							,						__a = None							,						__a = PILImageResampling.BICUBIC							,						__a = True							,						__a = True							,						__a = 1 / 2_55							,						__a = None							,						__a = True							,						__a = None							,						__a = None							,						**__a							,						)			->			None:






							'''simple docstring'''
							super().__init__(**__a)
							_UpperCamelCase									=    size if size is not None else {'''height''': 2_24, '''width''': 2_24}
							_UpperCamelCase									=    get_size_dict(__a)
							_UpperCamelCase									=    crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
							_UpperCamelCase									=    get_size_dict(__a							,						default_to_square=__a							,						param_name='''crop_size''')

							_UpperCamelCase									=    do_resize
							_UpperCamelCase									=    do_rescale
							_UpperCamelCase									=    do_normalize
							_UpperCamelCase									=    do_center_crop
							_UpperCamelCase									=    crop_size
							_UpperCamelCase									=    size
							_UpperCamelCase									=    resample
							_UpperCamelCase									=    rescale_factor
							_UpperCamelCase									=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							_UpperCamelCase									=    image_std if image_std is not None else IMAGENET_DEFAULT_STD



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = PILImageResampling.BILINEAR							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							_UpperCamelCase									=    get_size_dict(__a)

							if "shortest_edge" in size:
												_UpperCamelCase									=    get_resize_output_image_size(__a							,						size=size['''shortest_edge''']							,						default_to_square=__a)
												# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
							elif "height" in size and "width" in size:
												_UpperCamelCase									=    (size['''height'''], size['''width'''])
							else:
												raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''')
							return resize(__a							,						size=__a							,						resample=__a							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							_UpperCamelCase									=    get_size_dict(__a)
							if "height" not in size or "width" not in size:
												raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''')
							return center_crop(__a							,						size=(size['''height'''], size['''width'''])							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = None							,						**__a)			->			np.ndarray:






							'''simple docstring'''
							return rescale(__a							,						scale=__a							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							return normalize(__a							,						mean=__a							,						std=__a							,						data_format=__a							,						**__a)




		def 		UpperCAmelCase				(   self							,						__a							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = ChannelDimension.FIRST							,						**__a							,						)			->			BatchFeature:






							'''simple docstring'''
							_UpperCamelCase									=    do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase									=    do_rescale if do_rescale is not None else self.do_rescale
							_UpperCamelCase									=    do_normalize if do_normalize is not None else self.do_normalize
							_UpperCamelCase									=    do_center_crop if do_center_crop is not None else self.do_center_crop
							_UpperCamelCase									=    crop_size if crop_size is not None else self.crop_size
							_UpperCamelCase									=    get_size_dict(__a							,						param_name='''crop_size'''							,						default_to_square=__a)
							_UpperCamelCase									=    resample if resample is not None else self.resample
							_UpperCamelCase									=    rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCamelCase									=    image_mean if image_mean is not None else self.image_mean
							_UpperCamelCase									=    image_std if image_std is not None else self.image_std

							_UpperCamelCase									=    size if size is not None else self.size
							_UpperCamelCase									=    get_size_dict(__a)

							if not is_batched(__a):
												_UpperCamelCase									=    [images]

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

							if do_resize and size is None:
												raise ValueError('''Size 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.''')

							# All transformations expect numpy arrays.
							_UpperCamelCase									=    [to_numpy_array(__a) for image in images]

							if do_resize:
												_UpperCamelCase									=    [self.resize(image=__a							,						size=__a							,						resample=__a) for image in images]

							if do_center_crop:
												_UpperCamelCase									=    [self.center_crop(image=__a							,						size=__a) for image in images]

							if do_rescale:
												_UpperCamelCase									=    [self.rescale(image=__a							,						scale=__a) for image in images]

							if do_normalize:
												_UpperCamelCase									=    [self.normalize(image=__a							,						mean=__a							,						std=__a) for image in images]

							_UpperCamelCase									=    [to_channel_dimension_format(__a							,						__a) for image in images]

							_UpperCamelCase									=    {'''pixel_values''': images}
							return BatchFeature(data=__a							,						tensor_type=__a)






