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

import sentencepiece as spm

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


__a          =			logging.get_logger(__name__)

__a          =			'▁'

__a          =			{'vocab_file': 'sentencepiece.bpe.model'}

__a          =			{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
        ),
    }
}

__a          =			{
    'facebook/nllb-200-distilled-600M': 1_024,
}

# fmt: off
__a          =			['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']

class   __a(					_a       ):






    """simple docstring"""





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

    lowerCAmelCase        =				[]
    lowerCAmelCase        =				[]





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE="<s>"       ,_SCREAMING_SNAKE_CASE="</s>"       ,_SCREAMING_SNAKE_CASE="</s>"       ,_SCREAMING_SNAKE_CASE="<s>"       ,_SCREAMING_SNAKE_CASE="<unk>"       ,_SCREAMING_SNAKE_CASE="<pad>"       ,_SCREAMING_SNAKE_CASE="<mask>"       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=False       ,**_SCREAMING_SNAKE_CASE       ,)			->		str:
         # Mask token behave like a normal word, i.e. include the space before it
         UpperCAmelCase_      :							List[Any]       =      AddedToken(_SCREAMING_SNAKE_CASE       ,lstrip=_SCREAMING_SNAKE_CASE       ,rstrip=_SCREAMING_SNAKE_CASE  ) if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) else mask_token

         UpperCAmelCase_      :							Union[str, Any]       =      {} if sp_model_kwargs is None else sp_model_kwargs
         UpperCAmelCase_      :							Optional[int]       =      legacy_behaviour

         super().__init__(
             bos_token=_SCREAMING_SNAKE_CASE       ,eos_token=_SCREAMING_SNAKE_CASE       ,unk_token=_SCREAMING_SNAKE_CASE       ,sep_token=_SCREAMING_SNAKE_CASE       ,cls_token=_SCREAMING_SNAKE_CASE       ,pad_token=_SCREAMING_SNAKE_CASE       ,mask_token=_SCREAMING_SNAKE_CASE       ,tokenizer_file=_SCREAMING_SNAKE_CASE       ,src_lang=_SCREAMING_SNAKE_CASE       ,tgt_lang=_SCREAMING_SNAKE_CASE       ,additional_special_tokens=_SCREAMING_SNAKE_CASE       ,sp_model_kwargs=self.sp_model_kwargs       ,legacy_behaviour=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE       ,)

         UpperCAmelCase_      :							Union[str, Any]       =      spm.SentencePieceProcessor(**self.sp_model_kwargs  )
         self.sp_model.Load(str(_SCREAMING_SNAKE_CASE  )  )
         UpperCAmelCase_      :							Tuple       =      vocab_file
         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
         # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
         # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

         # Mimic fairseq token-to-id alignment for the first 4 token
         UpperCAmelCase_      :							List[str]       =      {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         UpperCAmelCase_      :							Any       =      1

         UpperCAmelCase_      :							List[str]       =      len(self.sp_model  )
         UpperCAmelCase_      :							Optional[Any]       =      {
             code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_SCREAMING_SNAKE_CASE  )
         }
         UpperCAmelCase_      :							Optional[int]       =      {v: k for k, v in self.lang_code_to_id.items()}
         UpperCAmelCase_      :							Dict       =      len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset

         self.fairseq_tokens_to_ids.update(self.lang_code_to_id  )
         UpperCAmelCase_      :							Dict       =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
         UpperCAmelCase_      :							Optional[int]       =      list(self.lang_code_to_id.keys()  )

         if additional_special_tokens is not None:
              # Only add those special tokens if they are not already there.
              self._additional_special_tokens.extend(
                  [t for t in additional_special_tokens if t not in self._additional_special_tokens]  )

         UpperCAmelCase_      :							Optional[Any]       =      src_lang if src_lang is not None else '''eng_Latn'''
         UpperCAmelCase_      :							str       =      self.lang_code_to_id[self._src_lang]
         UpperCAmelCase_      :							List[Any]       =      tgt_lang
         self.set_src_lang_special_tokens(self._src_lang  )





    def __getstate__(				self  )			->		Tuple:
         UpperCAmelCase_      :							Optional[int]       =      self.__dict__.copy()
         UpperCAmelCase_      :							List[str]       =      None
         UpperCAmelCase_      :							Optional[Any]       =      self.sp_model.serialized_model_proto()
         return state





    def __setstate__(				self       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         UpperCAmelCase_      :							List[Any]       =      d

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

         UpperCAmelCase_      :							List[Any]       =      spm.SentencePieceProcessor(**self.sp_model_kwargs  )
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )





    @property
    def 							a__				(				self  )			->		List[Any]:
         return len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset + 1  # Plus 1 for the mask token





    @property
    def 							a__				(				self  )			->		str:
         return self._src_lang





    @src_lang.setter
    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		None:
         UpperCAmelCase_      :							str       =      new_src_lang
         self.set_src_lang_special_tokens(self._src_lang  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = False  )			->		List[int]:

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

         UpperCAmelCase_      :							List[str]       =      [1] * len(self.prefix_tokens  )
         UpperCAmelCase_      :							Optional[int]       =      [1] * len(self.suffix_tokens  )
         if token_ids_a is None:
              return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE  )) + suffix_ones
         return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE  )) + ([0] * len(_SCREAMING_SNAKE_CASE  )) + suffix_ones





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		List[int]:
         if token_ids_a is None:
              return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		List[int]:
         UpperCAmelCase_      :							Union[str, Any]       =      [self.sep_token_id]
         UpperCAmelCase_      :							Optional[Any]       =      [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 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		List[str]:
         if src_lang is None or tgt_lang is None:
              raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''  )
         UpperCAmelCase_      :							Dict       =      src_lang
         UpperCAmelCase_      :							List[str]       =      self(_SCREAMING_SNAKE_CASE       ,add_special_tokens=_SCREAMING_SNAKE_CASE       ,return_tensors=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Dict       =      self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      tgt_lang_id
         return inputs





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							Tuple       =      {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE  ): i for i in range(self.vocab_size  )}
         vocab.update(self.added_tokens_encoder  )
         return vocab





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		List[str]:
         return self.sp_model.encode(_SCREAMING_SNAKE_CASE       ,out_type=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Dict:
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         UpperCAmelCase_      :							Dict       =      self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE  )

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		str:
         if index in self.fairseq_ids_to_tokens:
              return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         UpperCAmelCase_      :							Dict       =      ''''''.join(_SCREAMING_SNAKE_CASE  ).replace(_SCREAMING_SNAKE_CASE       ,''' '''  ).strip()
         return out_string





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		Tuple[str]:
         if not os.path.isdir(_SCREAMING_SNAKE_CASE  ):
              logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
              return
         UpperCAmelCase_      :							Optional[int]       =      os.path.join(
             _SCREAMING_SNAKE_CASE       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

         if os.path.abspath(self.vocab_file  ) != os.path.abspath(_SCREAMING_SNAKE_CASE  ) and os.path.isfile(self.vocab_file  ):
              copyfile(self.vocab_file       ,_SCREAMING_SNAKE_CASE  )
         elif not os.path.isfile(self.vocab_file  ):
              with open(_SCREAMING_SNAKE_CASE       ,'''wb'''  ) as fi:
                   UpperCAmelCase_      :							Optional[Any]       =      self.sp_model.serialized_model_proto()
                   fi.write(_SCREAMING_SNAKE_CASE  )

         return (out_vocab_file,)





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = "eng_Latn"       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = "fra_Latn"       ,**_SCREAMING_SNAKE_CASE       ,)			->		BatchEncoding:
         UpperCAmelCase_      :							int       =      src_lang
         UpperCAmelCase_      :							List[Any]       =      tgt_lang
         return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Optional[Any]:
         return self.set_src_lang_special_tokens(self.src_lang  )





    def 							a__				(				self  )			->		List[str]:
         return self.set_tgt_lang_special_tokens(self.tgt_lang  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		None:
         UpperCAmelCase_      :							Any       =      self.lang_code_to_id[src_lang]
         if self.legacy_behaviour:
              UpperCAmelCase_      :							Tuple       =      []
              UpperCAmelCase_      :							List[str]       =      [self.eos_token_id, self.cur_lang_code]
         else:
              UpperCAmelCase_      :							Tuple       =      [self.cur_lang_code]
              UpperCAmelCase_      :							Tuple       =      [self.eos_token_id]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		None:
         UpperCAmelCase_      :							Union[str, Any]       =      self.lang_code_to_id[lang]
         if self.legacy_behaviour:
              UpperCAmelCase_      :							int       =      []
              UpperCAmelCase_      :							Optional[int]       =      [self.eos_token_id, self.cur_lang_code]
         else:
              UpperCAmelCase_      :							Any       =      [self.cur_lang_code]
              UpperCAmelCase_      :							List[Any]       =      [self.eos_token_id]





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast

@require_vision
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Optional[int]       =      tempfile.mkdtemp()

         UpperCAmelCase_      :							str       =      BlipImageProcessor()
         UpperCAmelCase_      :							Dict       =      GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''  )

         UpperCAmelCase_      :							Optional[Any]       =      BlipaProcessor(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         processor.save_pretrained(self.tmpdirname  )





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,**_SCREAMING_SNAKE_CASE  ).tokenizer





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		Dict:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,**_SCREAMING_SNAKE_CASE  ).image_processor





    def 							a__				(				self  )			->		List[Any]:
         shutil.rmtree(self.tmpdirname  )





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Tuple       =      [np.random.randint(255       ,size=(3, 30, 400)       ,dtype=np.uinta  )]

         UpperCAmelCase_      :							Optional[Any]       =      [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE       ,0       ,-1  )  ) for x in image_inputs]

         return image_inputs





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Dict       =      BlipaProcessor(tokenizer=self.get_tokenizer()       ,image_processor=self.get_image_processor()  )
         processor.save_pretrained(self.tmpdirname  )

         UpperCAmelCase_      :							str       =      self.get_tokenizer(bos_token='''(BOS)'''       ,eos_token='''(EOS)'''  )
         UpperCAmelCase_      :							int       =      self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE       ,padding_value=1.0  )

         UpperCAmelCase_      :							Union[str, Any]       =      BlipaProcessor.from_pretrained(
             self.tmpdirname       ,bos_token='''(BOS)'''       ,eos_token='''(EOS)'''       ,do_normalize=_SCREAMING_SNAKE_CASE       ,padding_value=1.0  )

         self.assertEqual(processor.tokenizer.get_vocab()       ,tokenizer_add_kwargs.get_vocab()  )
         self.assertIsInstance(processor.tokenizer       ,_SCREAMING_SNAKE_CASE  )

         self.assertEqual(processor.image_processor.to_json_string()       ,image_processor_add_kwargs.to_json_string()  )
         self.assertIsInstance(processor.image_processor       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Dict       =      self.get_image_processor()
         UpperCAmelCase_      :							Any       =      self.get_tokenizer()

         UpperCAmelCase_      :							str       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      self.prepare_image_inputs()

         UpperCAmelCase_      :							Optional[Any]       =      image_processor(_SCREAMING_SNAKE_CASE       ,return_tensors='''np'''  )
         UpperCAmelCase_      :							int       =      processor(images=_SCREAMING_SNAKE_CASE       ,return_tensors='''np'''  )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()       ,input_processor[key].sum()       ,delta=1e-2  )





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							str       =      self.get_image_processor()
         UpperCAmelCase_      :							List[Any]       =      self.get_tokenizer()

         UpperCAmelCase_      :							Any       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      '''lower newer'''

         UpperCAmelCase_      :							Optional[int]       =      processor(text=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      tokenizer(_SCREAMING_SNAKE_CASE       ,return_token_type_ids=_SCREAMING_SNAKE_CASE  )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]       ,encoded_processor[key]  )





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							str       =      self.get_image_processor()
         UpperCAmelCase_      :							List[Any]       =      self.get_tokenizer()

         UpperCAmelCase_      :							Tuple       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      '''lower newer'''
         UpperCAmelCase_      :							int       =      self.prepare_image_inputs()

         UpperCAmelCase_      :							List[str]       =      processor(text=_SCREAMING_SNAKE_CASE       ,images=_SCREAMING_SNAKE_CASE  )

         self.assertListEqual(list(inputs.keys()  )       ,['''pixel_values''', '''input_ids''', '''attention_mask''']  )

         # test if it raises when no input is passed
         with pytest.raises(_SCREAMING_SNAKE_CASE  ):
              processor()





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Tuple       =      self.get_image_processor()
         UpperCAmelCase_      :							Dict       =      self.get_tokenizer()

         UpperCAmelCase_      :							List[str]       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[Any]       =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         UpperCAmelCase_      :							List[str]       =      processor.batch_decode(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE  )

         self.assertListEqual(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							Union[str, Any]       =      self.get_image_processor()
         UpperCAmelCase_      :							int       =      self.get_tokenizer()

         UpperCAmelCase_      :							Any       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Dict       =      '''lower newer'''
         UpperCAmelCase_      :							Union[str, Any]       =      self.prepare_image_inputs()

         UpperCAmelCase_      :							Any       =      processor(text=_SCREAMING_SNAKE_CASE       ,images=_SCREAMING_SNAKE_CASE  )

         # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
         self.assertListEqual(list(inputs.keys()  )       ,['''pixel_values''', '''input_ids''', '''attention_mask''']  )