



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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE  					=    '▁'

SCREAMING_SNAKE_CASE  					=    {'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE  					=    {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

SCREAMING_SNAKE_CASE  					=    {
    'google/pegasus-xsum': 512,
}


SCREAMING_SNAKE_CASE  					=    logging.get_logger(__name__)



class 						lowerCamelCase   (    __snake_case	):







					'''simple docstring'''







					lowerCAmelCase_					:     Tuple					       =	VOCAB_FILES_NAMES

					lowerCAmelCase_					:     Optional[Any]					       =	VOCAB_FILES_NAMES
					lowerCAmelCase_					:     Union[str, Any]					       =	PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_					:     Any					       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_					:     int					       =	["input_ids", "attention_mask"]



					def __init__(      self     ,						lowerCAmelCase     ,						lowerCAmelCase="<pad>"     ,						lowerCAmelCase="</s>"     ,						lowerCAmelCase="<unk>"     ,						lowerCAmelCase="<mask_2>"     ,						lowerCAmelCase="<mask_1>"     ,						lowerCAmelCase=None     ,						lowerCAmelCase=103     ,						lowerCAmelCase = None     ,						**lowerCAmelCase     ,						):
							UpperCAmelCase_						       =					offset
							if additional_special_tokens is not None:
									if not isinstance(A__     ,						A__  ):
											raise TypeError(
											    f'''additional_special_tokens should be of type {type(A__  )}, but is'''
											    f''' {type(A__  )}'''  )

									UpperCAmelCase_						       =					(
									    ([mask_token_sent] + additional_special_tokens)
									    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
									    else additional_special_tokens
									)
									# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
									additional_special_tokens_extended += [
									    f'''<unk_{i}>''' for i in range(len(A__  )     ,						self.offset - 1  )
									]

									if len(set(A__  )  ) != len(A__  ):
											raise ValueError(
											    "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
											    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''  )
									UpperCAmelCase_						       =					additional_special_tokens_extended
							else:
									UpperCAmelCase_						       =					[mask_token_sent] if mask_token_sent is not None else []
									additional_special_tokens += [f'''<unk_{i}>''' for i in range(2     ,						self.offset  )]

							UpperCAmelCase_						       =					{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=A__     ,						unk_token=A__     ,						mask_token=A__     ,						pad_token=A__     ,						mask_token_sent=A__     ,						offset=A__     ,						additional_special_tokens=A__     ,						sp_model_kwargs=self.sp_model_kwargs     ,						**A__     ,						)
							UpperCAmelCase_						       =					mask_token_sent
							UpperCAmelCase_						       =					vocab_file
							UpperCAmelCase_						       =					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(A__  )

							# add special tokens to encoder dict
							UpperCAmelCase_						       =					{
							    0: self.pad_token,
							    1: self.eos_token,
							}

							if self.mask_token_sent is not None:
									self.encoder.update(
									    {
									        2: self.mask_token_sent,
									        3: self.mask_token,
									    }  )

							if self.offset > 0:
									# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
									# mask_token_sent is already added to list -> so start at 1
									self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1     ,						self.offset - 1  )}  )

							UpperCAmelCase_						       =					{v: k for k, v in self.encoder.items()}



					@property
					def  A__		(      self  ):
							return len(self.sp_model  ) + self.offset



					def  A__		(      self  ):
							UpperCAmelCase_						       =					{self.convert_ids_to_tokens(A__  ): i for i in range(self.vocab_size  )}
							vocab.update(self.added_tokens_encoder  )
							return vocab



					def __getstate__(      self  ):
							UpperCAmelCase_						       =					self.__dict__.copy()
							UpperCAmelCase_						       =					None
							return state



					def __setstate__(      self     ,						lowerCAmelCase  ):
							UpperCAmelCase_						       =					d

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

							UpperCAmelCase_						       =					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(self.vocab_file  )



					def  A__		(      self     ,						lowerCAmelCase  ):
							return self.sp_model.encode(A__     ,						out_type=A__  )



					def  A__		(      self     ,						lowerCAmelCase  ):
							if token in self.decoder:
									return self.decoder[token]
							elif token in self.added_tokens_decoder:
									return self.added_tokens_decoder[token]
							UpperCAmelCase_						       =					self.sp_model.piece_to_id(A__  )
							return sp_id + self.offset



					def  A__		(      self     ,						lowerCAmelCase  ):
							if index in self.encoder:
									return self.encoder[index]
							elif index in self.added_tokens_encoder:
									return self.added_tokens_encoder[index]
							else:
									UpperCAmelCase_						       =					self.sp_model.IdToPiece(index - self.offset  )
							return token



					def  A__		(      self     ,						lowerCAmelCase  ):
							UpperCAmelCase_						       =					[]
							UpperCAmelCase_						       =					''''''
							for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
											out_string += self.sp_model.decode(A__  ) + token
											UpperCAmelCase_						       =					[]
									else:
											current_sub_tokens.append(A__  )
							out_string += self.sp_model.decode(A__  )
							return out_string.strip()



					def  A__		(      self     ,						lowerCAmelCase=False  ):
							return 1



					def  A__		(      self     ,						lowerCAmelCase  ):
							UpperCAmelCase_						       =					set(self.all_special_ids  )  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special

							return [1 if x in all_special_ids else 0 for x in seq]



					def  A__		(      self     ,						lowerCAmelCase     ,						lowerCAmelCase = None     ,						lowerCAmelCase = False  ):
							if already_has_special_tokens:
									return self._special_token_mask(A__  )
							elif token_ids_a is None:
									return self._special_token_mask(A__  ) + [1]
							else:
									return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]



					def  A__		(      self     ,						lowerCAmelCase     ,						lowerCAmelCase=None  ):
							if token_ids_a is None:
									return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]



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

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

							return (out_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 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[Any]     =   ["image_processor", "tokenizer"]
		__snake_case      :       Tuple     =   "BridgeTowerImageProcessor"
		__snake_case      :       List[str]     =   ("RobertaTokenizer", "RobertaTokenizerFast")







		def __init__(					self       ,A__       ,A__		):
						super().__init__(A__       ,A__		)







		def __call__(					self       ,A__       ,A__ = None       ,A__ = True       ,A__ = False       ,A__ = None       ,A__ = None       ,A__ = 0       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = True       ,A__ = None       ,**A__       ,):
						_A	:				List[Any]   =      self.tokenizer(
						    text=A__       ,add_special_tokens=A__       ,padding=A__       ,truncation=A__       ,max_length=A__       ,stride=A__       ,pad_to_multiple_of=A__       ,return_token_type_ids=A__       ,return_attention_mask=A__       ,return_overflowing_tokens=A__       ,return_special_tokens_mask=A__       ,return_offsets_mapping=A__       ,return_length=A__       ,verbose=A__       ,return_tensors=A__       ,**A__       ,)
						# add pixel_values + pixel_mask
						_A	:				Optional[Any]   =      self.image_processor(
						    A__       ,return_tensors=A__       ,do_normalize=A__       ,do_center_crop=A__       ,**A__		)
						encoding.update(A__		)

						return encoding







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.batch_decode(*A__       ,**A__		)







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.decode(*A__       ,**A__		)







		@property
		def  A__      (					self		):
						_A	:				Union[str, Any]   =      self.tokenizer.model_input_names
						_A	:				str   =      self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)



