






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


_a      	=  logging.get_logger(__name__)

_a      	=  '''▁'''

_a      	=  {'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

_a      	=  {
    '''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''',
    },
}

_a      	=  {'''vinai/bartpho-syllable''': 1024}





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






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

	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase="<s>"       ,    UpperCAmelCase="</s>"       ,    UpperCAmelCase="</s>"       ,    UpperCAmelCase="<s>"       ,    UpperCAmelCase="<unk>"       ,    UpperCAmelCase="<pad>"       ,    UpperCAmelCase="<mask>"       ,    UpperCAmelCase = None       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			AddedToken(UpperCAmelCase       ,    lstrip=UpperCAmelCase       ,    rstrip=UpperCAmelCase    ) if isinstance(UpperCAmelCase       ,    UpperCAmelCase    ) else mask_token

							_UpperCAmelCase									=			{} if sp_model_kwargs is None else sp_model_kwargs

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

							_UpperCAmelCase									=			vocab_file
							_UpperCAmelCase									=			monolingual_vocab_file
							_UpperCAmelCase									=			spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(str(UpperCAmelCase    )    )

							# Load the reduced vocab

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

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

	def __getstate__(							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.__dict__.copy()
							_UpperCAmelCase									=			None
							_UpperCAmelCase									=			self.sp_model.serialized_model_proto()
							return state

	def __setstate__(							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			d

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

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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None    ):
							"""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        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    UpperCAmelCase = False    ):
							"""simple docstring"""







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

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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None    ):
							"""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]

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return len(self.fairseq_ids_to_tokens    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			{self.convert_ids_to_tokens(UpperCAmelCase    ): i for i in range(self.vocab_size    )}
							vocab.update(self.added_tokens_encoder    )
							return vocab

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return self.sp_model.encode(UpperCAmelCase       ,    out_type=UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
							else:
													return self.unk_token_id

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return self.fairseq_ids_to_tokens[index]

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			''.join(UpperCAmelCase    ).replace(UpperCAmelCase       ,    ' '    ).strip()
							return out_string

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None    ):
							"""simple docstring"""







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

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

							if os.path.abspath(self.monolingual_vocab_file    ) != os.path.abspath(
							    UpperCAmelCase    ) and os.path.isfile(self.monolingual_vocab_file    ):
													copyfile(self.monolingual_vocab_file       ,    UpperCAmelCase    )
							elif not os.path.isfile(self.monolingual_vocab_file    ):
													with open(UpperCAmelCase       ,    '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(UpperCAmelCase    )} \n"""    )

							return out_vocab_file, out_monolingual_vocab_file








from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      ["image_processor", "tokenizer"]
	UpperCamelCase__								=      "Pix2StructImageProcessor"
	UpperCamelCase__								=      ("T5Tokenizer", "T5TokenizerFast")

	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			False
							super().__init__(UpperCAmelCase       ,    UpperCAmelCase    )

	def __call__(							self       ,    UpperCAmelCase=None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = True       ,    UpperCAmelCase = False       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = 2048       ,    UpperCAmelCase = 0       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = True       ,    UpperCAmelCase = None       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							if images is None and text is None:
													raise ValueError('You have to specify either images or text.'    )

							# Get only text
							if images is None and not self.image_processor.is_vqa:
													_UpperCAmelCase									=			self.tokenizer
													_UpperCAmelCase									=			self.tokenizer(
													    text=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    padding=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    max_length=UpperCAmelCase       ,    stride=UpperCAmelCase       ,    pad_to_multiple_of=UpperCAmelCase       ,    return_attention_mask=UpperCAmelCase       ,    return_overflowing_tokens=UpperCAmelCase       ,    return_special_tokens_mask=UpperCAmelCase       ,    return_offsets_mapping=UpperCAmelCase       ,    return_token_type_ids=UpperCAmelCase       ,    return_length=UpperCAmelCase       ,    verbose=UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    **UpperCAmelCase       ,    )
													return text_encoding

							if not self.image_processor.is_vqa:
													# add pixel_values
													_UpperCAmelCase									=			self.image_processor(
													    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    max_patches=UpperCAmelCase       ,    **UpperCAmelCase    )
							else:
													# add pixel_values and bbox
													_UpperCAmelCase									=			self.image_processor(
													    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    max_patches=UpperCAmelCase       ,    header_text=UpperCAmelCase       ,    **UpperCAmelCase    )

							if text is not None and not self.image_processor.is_vqa:
													_UpperCAmelCase									=			self.tokenizer(
													    text=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    padding=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    max_length=UpperCAmelCase       ,    stride=UpperCAmelCase       ,    pad_to_multiple_of=UpperCAmelCase       ,    return_attention_mask=UpperCAmelCase       ,    return_overflowing_tokens=UpperCAmelCase       ,    return_special_tokens_mask=UpperCAmelCase       ,    return_offsets_mapping=UpperCAmelCase       ,    return_token_type_ids=UpperCAmelCase       ,    return_length=UpperCAmelCase       ,    verbose=UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    **UpperCAmelCase       ,    )

													if "attention_mask" in text_encoding:
																			_UpperCAmelCase									=			text_encoding.pop('attention_mask'    )
													if "input_ids" in text_encoding:
																			_UpperCAmelCase									=			text_encoding.pop('input_ids'    )
							else:
													_UpperCAmelCase									=			None

							if text_encoding is not None:
													encoding_image_processor.update(UpperCAmelCase    )

							return encoding_image_processor

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.tokenizer.batch_decode(*UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.tokenizer.decode(*UpperCAmelCase       ,    **UpperCAmelCase    )

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.tokenizer.model_input_names
							_UpperCAmelCase									=			self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )
