"""simple docstring"""




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		):






     def    __A  (		self  ):
       _lowerCAmelCase						:	int        =					tempfile.mkdtemp()

       _lowerCAmelCase						:	int        =					BlipImageProcessor()
       _lowerCAmelCase						:	Optional[Any]        =					GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""  )

       _lowerCAmelCase						:	Tuple        =					BlipaProcessor(a__    ,					a__  )

       processor.save_pretrained(self.tmpdirname  )






     def    __A  (		self    ,					**a__  ):
       return AutoProcessor.from_pretrained(self.tmpdirname    ,					**a__  ).tokenizer






     def    __A  (		self    ,					**a__  ):
       return AutoProcessor.from_pretrained(self.tmpdirname    ,					**a__  ).image_processor






     def    __A  (		self  ):
       shutil.rmtree(self.tmpdirname  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					[np.random.randint(255    ,					size=(3, 30, 400)    ,					dtype=np.uinta  )]

       _lowerCAmelCase						:	Union[str, Any]        =					[Image.fromarray(np.moveaxis(a__    ,					0    ,					-1  )  ) for x in image_inputs]

       return image_inputs






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					BlipaProcessor(tokenizer=self.get_tokenizer()    ,					image_processor=self.get_image_processor()  )
       processor.save_pretrained(self.tmpdirname  )

       _lowerCAmelCase						:	Any        =					self.get_tokenizer(bos_token="""(BOS)"""    ,					eos_token="""(EOS)"""  )
       _lowerCAmelCase						:	List[str]        =					self.get_image_processor(do_normalize=a__    ,					padding_value=1.0  )

       _lowerCAmelCase						:	str        =					BlipaProcessor.from_pretrained(
           self.tmpdirname    ,					bos_token="""(BOS)"""    ,					eos_token="""(EOS)"""    ,					do_normalize=a__    ,					padding_value=1.0  )

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

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






     def    __A  (		self  ):
       _lowerCAmelCase						:	int        =					self.get_image_processor()
       _lowerCAmelCase						:	int        =					self.get_tokenizer()

       _lowerCAmelCase						:	Tuple        =					BlipaProcessor(tokenizer=a__    ,					image_processor=a__  )

       _lowerCAmelCase						:	Tuple        =					self.prepare_image_inputs()

       _lowerCAmelCase						:	Union[str, Any]        =					image_processor(a__    ,					return_tensors="""np"""  )
       _lowerCAmelCase						:	Tuple        =					processor(images=a__    ,					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  ):
       _lowerCAmelCase						:	Any        =					self.get_image_processor()
       _lowerCAmelCase						:	Optional[Any]        =					self.get_tokenizer()

       _lowerCAmelCase						:	Any        =					BlipaProcessor(tokenizer=a__    ,					image_processor=a__  )

       _lowerCAmelCase						:	Optional[int]        =					'lower newer'

       _lowerCAmelCase						:	str        =					processor(text=a__  )

       _lowerCAmelCase						:	List[str]        =					tokenizer(a__    ,					return_token_type_ids=a__  )

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






     def    __A  (		self  ):
       _lowerCAmelCase						:	Union[str, Any]        =					self.get_image_processor()
       _lowerCAmelCase						:	Dict        =					self.get_tokenizer()

       _lowerCAmelCase						:	Optional[int]        =					BlipaProcessor(tokenizer=a__    ,					image_processor=a__  )

       _lowerCAmelCase						:	Dict        =					'lower newer'
       _lowerCAmelCase						:	List[Any]        =					self.prepare_image_inputs()

       _lowerCAmelCase						:	Dict        =					processor(text=a__    ,					images=a__  )

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

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






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[Any]        =					self.get_image_processor()
       _lowerCAmelCase						:	Optional[int]        =					self.get_tokenizer()

       _lowerCAmelCase						:	List[str]        =					BlipaProcessor(tokenizer=a__    ,					image_processor=a__  )

       _lowerCAmelCase						:	List[str]        =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       _lowerCAmelCase						:	Tuple        =					processor.batch_decode(a__  )
       _lowerCAmelCase						:	str        =					tokenizer.batch_decode(a__  )

       self.assertListEqual(a__    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Union[str, Any]        =					self.get_image_processor()
       _lowerCAmelCase						:	Optional[Any]        =					self.get_tokenizer()

       _lowerCAmelCase						:	Dict        =					BlipaProcessor(tokenizer=a__    ,					image_processor=a__  )

       _lowerCAmelCase						:	Tuple        =					'lower newer'
       _lowerCAmelCase						:	int        =					self.prepare_image_inputs()

       _lowerCAmelCase						:	str        =					processor(text=a__    ,					images=a__  )

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





"""simple docstring"""




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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_nllb import NllbTokenizer
else:
     _a				:		Tuple	   =       None


_a				:		str	   =       logging.get_logger(__name__)


_a				:		str	   =       {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_a				:		Tuple	   =       {
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
        ),
    },
}

_a				:		int	   =       {
    'facebook/nllb-large-en-ro': 1_024,
    'facebook/nllb-200-distilled-600M': 1_024,
}

# fmt: off
_a				:		Union[str, Any]	   =       ['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							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Union[str, Any]		 =		VOCAB_FILES_NAMES
     _UpperCamelCase							:      Union[str, Any]		 =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:      Dict		 =		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:      str		 =		["input_ids", "attention_mask"]
     _UpperCamelCase							:      Tuple		 =		NllbTokenizer

     _UpperCamelCase							:      List[int]		 =		[]
     _UpperCamelCase							:      List[int]		 =		[]






     def __init__(		self    ,					a__=None    ,					a__=None    ,					a__="<s>"    ,					a__="</s>"    ,					a__="</s>"    ,					a__="<s>"    ,					a__="<unk>"    ,					a__="<pad>"    ,					a__="<mask>"    ,					a__=None    ,					a__=None    ,					a__=None    ,					a__=False    ,					**a__    ,					):
       # Mask token behave like a normal word, i.e. include the space before it
       _lowerCAmelCase						:	Optional[int]        =					AddedToken(a__    ,					lstrip=a__    ,					rstrip=a__  ) if isinstance(a__    ,					a__  ) else mask_token
       _lowerCAmelCase						:	int        =					legacy_behaviour
       super().__init__(
           vocab_file=a__    ,					tokenizer_file=a__    ,					bos_token=a__    ,					eos_token=a__    ,					sep_token=a__    ,					cls_token=a__    ,					unk_token=a__    ,					pad_token=a__    ,					mask_token=a__    ,					src_lang=a__    ,					tgt_lang=a__    ,					additional_special_tokens=a__    ,					legacy_behaviour=a__    ,					**a__    ,					)

       _lowerCAmelCase						:	List[Any]        =					vocab_file
       _lowerCAmelCase						:	List[str]        =					False if not self.vocab_file else True

       _lowerCAmelCase						:	List[str]        =					FAIRSEQ_LANGUAGE_CODES.copy()

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

       self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}  )
       _lowerCAmelCase						:	Tuple        =					{
           lang_code: self.convert_tokens_to_ids(a__  ) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       _lowerCAmelCase						:	List[str]        =					src_lang if src_lang is not None else """eng_Latn"""
       _lowerCAmelCase						:	int        =					self.convert_tokens_to_ids(self._src_lang  )
       _lowerCAmelCase						:	str        =					tgt_lang
       self.set_src_lang_special_tokens(self._src_lang  )






     @property
     def    __A  (		self  ):
       return self._src_lang






     @src_lang.setter
     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	List[Any]        =					new_src_lang
       self.set_src_lang_special_tokens(self._src_lang  )






     def    __A  (		self    ,					a__    ,					a__ = None  ):
       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    ,					a__    ,					a__ = None  ):
       _lowerCAmelCase						:	Tuple        =					[self.sep_token_id]
       _lowerCAmelCase						:	int        =					[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    ,					a__    ,					a__    ,					a__    ,					a__    ,					**a__  ):
       if src_lang is None or tgt_lang is None:
         raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""  )
       _lowerCAmelCase						:	str        =					src_lang
       _lowerCAmelCase						:	List[Any]        =					self(a__    ,					add_special_tokens=a__    ,					return_tensors=a__    ,					**a__  )
       _lowerCAmelCase						:	Dict        =					self.convert_tokens_to_ids(a__  )
       _lowerCAmelCase						:	Dict        =					tgt_lang_id
       return inputs






     def    __A  (		self    ,					a__    ,					a__ = "eng_Latn"    ,					a__ = None    ,					a__ = "fra_Latn"    ,					**a__    ,					):
       _lowerCAmelCase						:	List[Any]        =					src_lang
       _lowerCAmelCase						:	Optional[int]        =					tgt_lang
       return super().prepare_seqaseq_batch(a__    ,					a__    ,					**a__  )






     def    __A  (		self  ):
       return self.set_src_lang_special_tokens(self.src_lang  )






     def    __A  (		self  ):
       return self.set_tgt_lang_special_tokens(self.tgt_lang  )






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.convert_tokens_to_ids(a__  )

       if self.legacy_behaviour:
         _lowerCAmelCase						:	Optional[int]        =					[]
         _lowerCAmelCase						:	Union[str, Any]        =					[self.eos_token_id, self.cur_lang_code]
       else:
         _lowerCAmelCase						:	str        =					[self.cur_lang_code]
         _lowerCAmelCase						:	List[Any]        =					[self.eos_token_id]

       _lowerCAmelCase						:	List[Any]        =					self.convert_ids_to_tokens(self.prefix_tokens  )
       _lowerCAmelCase						:	Any        =					self.convert_ids_to_tokens(self.suffix_tokens  )

       _lowerCAmelCase						:	Optional[Any]        =					processors.TemplateProcessing(
           single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,					pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,					self.prefix_tokens + self.suffix_tokens  )  )    ,					)






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Any        =					self.convert_tokens_to_ids(a__  )
       if self.legacy_behaviour:
         _lowerCAmelCase						:	Optional[int]        =					[]
         _lowerCAmelCase						:	str        =					[self.eos_token_id, self.cur_lang_code]
       else:
         _lowerCAmelCase						:	int        =					[self.cur_lang_code]
         _lowerCAmelCase						:	Optional[int]        =					[self.eos_token_id]

       _lowerCAmelCase						:	Union[str, Any]        =					self.convert_ids_to_tokens(self.prefix_tokens  )
       _lowerCAmelCase						:	int        =					self.convert_ids_to_tokens(self.suffix_tokens  )

       _lowerCAmelCase						:	str        =					processors.TemplateProcessing(
           single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,					pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,					self.prefix_tokens + self.suffix_tokens  )  )    ,					)






     def    __A  (		self    ,					a__    ,					a__ = None  ):
       if not self.can_save_slow_tokenizer:
         raise ValueError(
             """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
             """tokenizer."""  )

       if not os.path.isdir(a__  ):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory."  )
         return
       _lowerCAmelCase						:	Optional[Any]        =					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__  ):
         copyfile(self.vocab_file    ,					a__  )

       return (out_vocab_file,)




