
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


lowercase        =  '''▁'''

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

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

lowercase        =  {
    '''google/pegasus-xsum''': 5_1_2,
}


lowercase        =  logging.get_logger(__name__)





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES

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

					def __init__(	self					:     Tuple   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Optional[int]="<pad>"   ,					__UpperCamelCase					:     Tuple="</s>"   ,					__UpperCamelCase					:     List[str]="<unk>"   ,					__UpperCamelCase					:     Optional[int]="<mask_2>"   ,					__UpperCamelCase					:     Any="<mask_1>"   ,					__UpperCamelCase					:     List[Any]=None   ,					__UpperCamelCase					:     Optional[Any]=1_0_3   ,					__UpperCamelCase					:     Optional[Dict[str, Any]] = None   ,					**__UpperCamelCase					:     List[Any]   ,					):
												lowerCamelCase_							  =					offset
												if additional_special_tokens is not None:
																			if not isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																										raise TypeError(
																										    F'''additional_special_tokens should be of type {type(__UpperCamelCase   )}, but is'''
																										    F''' {type(__UpperCamelCase   )}'''   )

																			lowerCamelCase_							  =					(
																			    ([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(__UpperCamelCase   )   ,					self.offset - 1   )
																			]

																			if len(set(__UpperCamelCase   )   ) != len(__UpperCamelCase   ):
																										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}.'''   )
																			lowerCamelCase_							  =					additional_special_tokens_extended
												else:
																			lowerCamelCase_							  =					[mask_token_sent] if mask_token_sent is not None else []
																			additional_special_tokens += [F'''<unk_{i}>''' for i in range(2   ,					self.offset   )]

												lowerCamelCase_							  =					{} if sp_model_kwargs is None else sp_model_kwargs

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

												# add special tokens to encoder dict
												lowerCamelCase_							  =					{
												    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   )}   )

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

					@property
					def    lowercase__      (	self					:     Union[str, Any]   ):
												return len(self.sp_model   ) + self.offset

					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					{self.convert_ids_to_tokens(__UpperCamelCase   ): i for i in range(self.vocab_size   )}
												vocab.update(self.added_tokens_encoder   )
												return vocab

					def __getstate__(	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.__dict__.copy()
												lowerCamelCase_							  =					None
												return state

					def __setstate__(	self					:     Optional[int]   ,					__UpperCamelCase					:     Any   ):
												lowerCamelCase_							  =					d

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

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

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     str   ):
												return self.sp_model.encode(__UpperCamelCase   ,					out_type=__UpperCamelCase   )

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     str   ):
												if token in self.decoder:
																			return self.decoder[token]
												elif token in self.added_tokens_decoder:
																			return self.added_tokens_decoder[token]
												lowerCamelCase_							  =					self.sp_model.piece_to_id(__UpperCamelCase   )
												return sp_id + self.offset

					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     int   ):
												if index in self.encoder:
																			return self.encoder[index]
												elif index in self.added_tokens_encoder:
																			return self.added_tokens_encoder[index]
												else:
																			lowerCamelCase_							  =					self.sp_model.IdToPiece(index - self.offset   )
												return token

					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     Dict   ):
												lowerCamelCase_							  =					[]
												lowerCamelCase_							  =					""""""
												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(__UpperCamelCase   ) + token
																										lowerCamelCase_							  =					[]
																			else:
																										current_sub_tokens.append(__UpperCamelCase   )
												out_string += self.sp_model.decode(__UpperCamelCase   )
												return out_string.strip()

					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     Union[str, Any]=False   ):
												return 1

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Tuple   ):
												lowerCamelCase_							  =					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    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     List   ,					__UpperCamelCase					:     Optional[List] = None   ,					__UpperCamelCase					:     bool = False   ):
												if already_has_special_tokens:
																			return self._special_token_mask(__UpperCamelCase   )
												elif token_ids_a is None:
																			return self._special_token_mask(__UpperCamelCase   ) + [1]
												else:
																			return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Union[str, Any]=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    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[str] = None   ):
												if not os.path.isdir(__UpperCamelCase   ):
																			logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
																			return
												lowerCamelCase_							  =					os.path.join(
												    __UpperCamelCase   ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""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:
																										lowerCamelCase_							  =					self.sp_model.serialized_model_proto()
																										fi.write(__UpperCamelCase   )

												return (out_vocab_file,)







from typing import List, Optional, Union

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





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''image_processor''', '''tokenizer''']
					SCREAMING_SNAKE_CASE			    =    '''BlipImageProcessor'''
					SCREAMING_SNAKE_CASE			    =    '''AutoTokenizer'''

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					False
												super().__init__(__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					self.image_processor

					def __call__(	self					:     Any   ,					__UpperCamelCase					:     ImageInput = None   ,					__UpperCamelCase					:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Union[bool, str, PaddingStrategy] = False   ,					__UpperCamelCase					:     Union[bool, str, TruncationStrategy] = None   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     int = 0   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Optional[Union[str, TensorType]] = None   ,					**__UpperCamelCase					:     Optional[Any]   ,					):
												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:
																			lowerCamelCase_							  =					self.tokenizer
																			lowerCamelCase_							  =					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

												# add pixel_values
												lowerCamelCase_							  =					self.image_processor(__UpperCamelCase   ,					return_tensors=__UpperCamelCase   )

												if text is not None:
																			lowerCamelCase_							  =					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   ,					)
												else:
																			lowerCamelCase_							  =					None

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

												return encoding_image_processor

					def    lowercase__      (	self					:     Any   ,					*__UpperCamelCase					:     Union[str, Any]   ,					**__UpperCamelCase					:     List[Any]   ):
												return self.tokenizer.batch_decode(*__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ,					*__UpperCamelCase					:     List[Any]   ,					**__UpperCamelCase					:     int   ):
												return self.tokenizer.decode(*__UpperCamelCase   ,					**__UpperCamelCase   )



					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					self.tokenizer.model_input_names
												lowerCamelCase_							  =					self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )





