



'''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   :			int					  =    None


a   :			int					  =    logging.get_logger(__name__)


a   :			List[Any]					  =    {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

a   :			List[Any]					  =    {
    """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   :			Tuple					  =    {
    """facebook/nllb-large-en-ro""": 1_0_2_4,
    """facebook/nllb-200-distilled-600M""": 1_0_2_4,
}

# fmt: off
a   :			str					  =    ["""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 			UpperCamelCase_ (							__magic_name__			):
	lowercase          = VOCAB_FILES_NAMES
	lowercase          = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase          = PRETRAINED_VOCAB_FILES_MAP
	lowercase          = ['input_ids', 'attention_mask']
	lowercase          = NllbTokenizer

	lowercase          = []
	lowercase          = []





	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		,		)			->			Optional[int]:
								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase     :		Optional[Any]							     =		AddedToken(A		,		lstrip=A		,		rstrip=A    ) if isinstance(A		,		A    ) else mask_token
								UpperCAmelCase     :		Union[str, Any]							     =		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		,		)

								UpperCAmelCase     :		List[str]							     =		vocab_file
								UpperCAmelCase     :		Union[str, Any]							     =		False if not self.vocab_file else True

								UpperCAmelCase     :		Optional[int]							     =		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}    )
								UpperCAmelCase     :		Union[str, Any]							     =		{
								    lang_code: self.convert_tokens_to_ids(A    ) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

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





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





	@src_lang.setter
	def  _lowercase(  self		,		A    )			->			None:
								UpperCAmelCase     :		Any							     =		new_src_lang
								self.set_src_lang_special_tokens(self._src_lang    )





	def  _lowercase(  self		,		A		,		A = 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  _lowercase(  self		,		A		,		A = None    )			->			List[int]:
								UpperCAmelCase     :		Optional[int]							     =		[self.sep_token_id]
								UpperCAmelCase     :		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  _lowercase(  self		,		A		,		A		,		A		,		A		,		**A    )			->			Union[str, Any]:
								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     :		List[str]							     =		src_lang
								UpperCAmelCase     :		Any							     =		self(A		,		add_special_tokens=A		,		return_tensors=A		,		**A    )
								UpperCAmelCase     :		Any							     =		self.convert_tokens_to_ids(A    )
								UpperCAmelCase     :		Optional[Any]							     =		tgt_lang_id
								return inputs





	def  _lowercase(  self		,		A		,		A = "eng_Latn"		,		A = None		,		A = "fra_Latn"		,		**A		,		)			->			BatchEncoding:
								UpperCAmelCase     :		Optional[Any]							     =		src_lang
								UpperCAmelCase     :		List[str]							     =		tgt_lang
								return super().prepare_seqaseq_batch(A		,		A		,		**A    )





	def  _lowercase(  self    )			->			Tuple:
								return self.set_src_lang_special_tokens(self.src_lang    )





	def  _lowercase(  self    )			->			List[Any]:
								return self.set_tgt_lang_special_tokens(self.tgt_lang    )





	def  _lowercase(  self		,		A    )			->			None:
								UpperCAmelCase     :		str							     =		self.convert_tokens_to_ids(A    )

								if self.legacy_behaviour:
															UpperCAmelCase     :		Optional[int]							     =		[]
															UpperCAmelCase     :		Tuple							     =		[self.eos_token_id, self.cur_lang_code]
								else:
															UpperCAmelCase     :		Dict							     =		[self.cur_lang_code]
															UpperCAmelCase     :		Optional[Any]							     =		[self.eos_token_id]

								UpperCAmelCase     :		str							     =		self.convert_ids_to_tokens(self.prefix_tokens    )
								UpperCAmelCase     :		Optional[int]							     =		self.convert_ids_to_tokens(self.suffix_tokens    )

								UpperCAmelCase     :		Optional[int]							     =		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  _lowercase(  self		,		A    )			->			None:
								UpperCAmelCase     :		Optional[Any]							     =		self.convert_tokens_to_ids(A    )
								if self.legacy_behaviour:
															UpperCAmelCase     :		int							     =		[]
															UpperCAmelCase     :		str							     =		[self.eos_token_id, self.cur_lang_code]
								else:
															UpperCAmelCase     :		List[str]							     =		[self.cur_lang_code]
															UpperCAmelCase     :		List[Any]							     =		[self.eos_token_id]

								UpperCAmelCase     :		Union[str, Any]							     =		self.convert_ids_to_tokens(self.prefix_tokens    )
								UpperCAmelCase     :		Tuple							     =		self.convert_ids_to_tokens(self.suffix_tokens    )

								UpperCAmelCase     :		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  _lowercase(  self		,		A		,		A = None    )			->			Tuple[str]:
								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
								UpperCAmelCase     :		Union[str, 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,)






'''simple docstring'''




from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
							import tensorflow as tf

							from transformers import TFBlipTextModel
							from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class 			UpperCamelCase_ :





	def __init__(  self		,		A		,		A=12		,		A=7		,		A=True		,		A=True		,		A=True		,		A=99		,		A=32		,		A=32		,		A=2		,		A=4		,		A=37		,		A=0.1		,		A=0.1		,		A=512		,		A=0.0_2		,		A=0		,		A=None		,		)			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		parent
								UpperCAmelCase     :		str							     =		batch_size
								UpperCAmelCase     :		Union[str, Any]							     =		seq_length
								UpperCAmelCase     :		Optional[Any]							     =		is_training
								UpperCAmelCase     :		int							     =		use_input_mask
								UpperCAmelCase     :		List[Any]							     =		use_labels
								UpperCAmelCase     :		Dict							     =		vocab_size
								UpperCAmelCase     :		str							     =		hidden_size
								UpperCAmelCase     :		List[Any]							     =		projection_dim
								UpperCAmelCase     :		Tuple							     =		num_hidden_layers
								UpperCAmelCase     :		Dict							     =		num_attention_heads
								UpperCAmelCase     :		Optional[Any]							     =		intermediate_size
								UpperCAmelCase     :		Any							     =		dropout
								UpperCAmelCase     :		List[Any]							     =		attention_dropout
								UpperCAmelCase     :		Optional[Any]							     =		max_position_embeddings
								UpperCAmelCase     :		Tuple							     =		initializer_range
								UpperCAmelCase     :		Optional[Any]							     =		scope
								UpperCAmelCase     :		Union[str, Any]							     =		bos_token_id





	def  _lowercase(  self    )			->			Tuple:
								UpperCAmelCase     :		Optional[int]							     =		ids_tensor([self.batch_size, self.seq_length]		,		self.vocab_size    )

								UpperCAmelCase     :		Union[str, Any]							     =		None
								if self.use_input_mask:
															UpperCAmelCase     :		str							     =		random_attention_mask([self.batch_size, self.seq_length]    )

								if input_mask is not None:
															UpperCAmelCase     :		Tuple							     =		input_mask.numpy()
															UpperCAmelCase     ,     UpperCAmelCase     :		int							     =		input_mask.shape
															UpperCAmelCase     :		Optional[int]							     =		np.random.randint(1		,		seq_length - 1		,		size=(batch_size,)    )
															for batch_idx, start_index in enumerate(A    ):
																						UpperCAmelCase     :		Tuple							     =		1
																						UpperCAmelCase     :		Optional[Any]							     =		0

								UpperCAmelCase     :		int							     =		self.get_config()

								return config, input_ids, tf.convert_to_tensor(A    )





	def  _lowercase(  self    )			->			int:
								return BlipTextConfig(
								    vocab_size=self.vocab_size		,		hidden_size=self.hidden_size		,		projection_dim=self.projection_dim		,		num_hidden_layers=self.num_hidden_layers		,		num_attention_heads=self.num_attention_heads		,		intermediate_size=self.intermediate_size		,		dropout=self.dropout		,		attention_dropout=self.attention_dropout		,		max_position_embeddings=self.max_position_embeddings		,		initializer_range=self.initializer_range		,		bos_token_id=self.bos_token_id		,		)





	def  _lowercase(  self		,		A		,		A		,		A    )			->			Union[str, Any]:
								UpperCAmelCase     :		int							     =		TFBlipTextModel(config=A    )
								UpperCAmelCase     :		Union[str, Any]							     =		model(A		,		attention_mask=A		,		training=A    )
								UpperCAmelCase     :		int							     =		model(A		,		training=A    )
								self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size)    )
								self.parent.assertEqual(result.pooler_output.shape		,		(self.batch_size, self.hidden_size)    )






	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Dict							     =		self.prepare_config_and_inputs()
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		config_and_inputs
								UpperCAmelCase     :		Optional[Any]							     =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_tf
class 			UpperCamelCase_ (							__magic_name__      ,     unittest.TestCase			):
	lowercase          = (TFBlipTextModel,) if is_tf_available() else ()
	lowercase          = False
	lowercase          = False
	lowercase          = False





	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		Union[str, Any]							     =		BlipTextModelTester(self    )
								UpperCAmelCase     :		List[str]							     =		ConfigTester(self		,		config_class=A		,		hidden_size=37    )





	def  _lowercase(  self    )			->			Tuple:
								self.config_tester.run_common_tests()





	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		List[Any]							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A    )





	def  _lowercase(  self    )			->			List[str]:
								pass





	def  _lowercase(  self    )			->			Optional[int]:
								pass





	@unittest.skip(reason="""Blip does not use inputs_embeds"""    )
	def  _lowercase(  self    )			->			Union[str, Any]:
								pass





	@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""    )
	def  _lowercase(  self    )			->			Optional[int]:
								pass





	@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""    )
	def  _lowercase(  self    )			->			Dict:
								pass





	@slow
	def  _lowercase(  self    )			->			Dict:
								for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase     :		Any							     =		TFBlipTextModel.from_pretrained(A    )
															self.assertIsNotNone(A    )






	def  _lowercase(  self		,		A=True    )			->			str:
								super().test_pt_tf_model_equivalence(allow_missing_keys=A    )

