




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:
 _lowerCamelCase			:	List[str]							 =     None


_lowerCamelCase			:	Any							 =     logging.get_logger(__name__)


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

_lowerCamelCase			:	Union[str, 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"""
        ),
    },
}

_lowerCamelCase			:	List[Any]							 =     {
    """facebook/nllb-large-en-ro""": 1024,
    """facebook/nllb-200-distilled-600M""": 1024,
}

# fmt: off
_lowerCamelCase			:	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        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   VOCAB_FILES_NAMES
    UpperCAmelCase__													=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__													=   PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__													=   ['''input_ids''', '''attention_mask''']
    UpperCAmelCase__													=   NllbTokenizer

    UpperCAmelCase__													=   []
    UpperCAmelCase__													=   []


    def __init__(    self   :			Dict      ,				UpperCAmelCase__   :			int=None      ,				UpperCAmelCase__   :			Tuple=None      ,				UpperCAmelCase__   :			Any="<s>"      ,				UpperCAmelCase__   :			Optional[Any]="</s>"      ,				UpperCAmelCase__   :			Dict="</s>"      ,				UpperCAmelCase__   :			Union[str, Any]="<s>"      ,				UpperCAmelCase__   :			Union[str, Any]="<unk>"      ,				UpperCAmelCase__   :			Optional[int]="<pad>"      ,				UpperCAmelCase__   :			List[str]="<mask>"      ,				UpperCAmelCase__   :			List[Any]=None      ,				UpperCAmelCase__   :			List[Any]=None      ,				UpperCAmelCase__   :			Tuple=None      ,				UpperCAmelCase__   :			Union[str, Any]=False      ,				**UpperCAmelCase__   :			Dict      ,				)		->Dict:





           '''simple docstring'''

           A__   				=					AddedToken(UpperCAmelCase__      ,				lstrip=UpperCAmelCase__      ,				rstrip=UpperCAmelCase__) if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__) else mask_token
           A__   				=					legacy_behaviour
           super().__init__(
               vocab_file=UpperCAmelCase__      ,				tokenizer_file=UpperCAmelCase__      ,				bos_token=UpperCAmelCase__      ,				eos_token=UpperCAmelCase__      ,				sep_token=UpperCAmelCase__      ,				cls_token=UpperCAmelCase__      ,				unk_token=UpperCAmelCase__      ,				pad_token=UpperCAmelCase__      ,				mask_token=UpperCAmelCase__      ,				src_lang=UpperCAmelCase__      ,				tgt_lang=UpperCAmelCase__      ,				additional_special_tokens=UpperCAmelCase__      ,				legacy_behaviour=UpperCAmelCase__      ,				**UpperCAmelCase__      ,				)

           A__   				=					vocab_file
           A__   				=					False if not self.vocab_file else True

           A__   				=					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})
           A__   				=					{
               lang_code: self.convert_tokens_to_ids(UpperCAmelCase__) for lang_code in FAIRSEQ_LANGUAGE_CODES
           }

           A__   				=					src_lang if src_lang is not None else '''eng_Latn'''
           A__   				=					self.convert_tokens_to_ids(self._src_lang)
           A__   				=					tgt_lang
           self.set_src_lang_special_tokens(self._src_lang)


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->str:





           '''simple docstring'''

           return self._src_lang


    @src_lang.setter
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			str)		->None:





           '''simple docstring'''

           A__   				=					new_src_lang
           self.set_src_lang_special_tokens(self._src_lang)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			List[int]      ,				UpperCAmelCase__   :			Optional[List[int]] = None)		->List[int]:





           '''simple docstring'''

           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  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			List[int]      ,				UpperCAmelCase__   :			Optional[List[int]] = None)		->List[int]:





           '''simple docstring'''

           A__   				=					[self.sep_token_id]
           A__   				=					[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  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Optional[str]      ,				UpperCAmelCase__   :			Optional[str]      ,				**UpperCAmelCase__   :			List[Any])		->List[Any]:





           '''simple docstring'''

           if src_lang is None or tgt_lang is None:
                  raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''')
           A__   				=					src_lang
           A__   				=					self(UpperCAmelCase__      ,				add_special_tokens=UpperCAmelCase__      ,				return_tensors=UpperCAmelCase__      ,				**UpperCAmelCase__)
           A__   				=					self.convert_tokens_to_ids(UpperCAmelCase__)
           A__   				=					tgt_lang_id
           return inputs


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			str = "eng_Latn"      ,				UpperCAmelCase__   :			Optional[List[str]] = None      ,				UpperCAmelCase__   :			str = "fra_Latn"      ,				**UpperCAmelCase__   :			List[str]      ,				)		->BatchEncoding:





           '''simple docstring'''

           A__   				=					src_lang
           A__   				=					tgt_lang
           return super().prepare_seqaseq_batch(UpperCAmelCase__      ,				UpperCAmelCase__      ,				**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->Optional[Any]:





           '''simple docstring'''

           return self.set_src_lang_special_tokens(self.src_lang)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->List[Any]:





           '''simple docstring'''

           return self.set_tgt_lang_special_tokens(self.tgt_lang)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			List[Any])		->None:





           '''simple docstring'''

           A__   				=					self.convert_tokens_to_ids(UpperCAmelCase__)

           if self.legacy_behaviour:
                  A__   				=					[]
                  A__   				=					[self.eos_token_id, self.cur_lang_code]
           else:
                  A__   				=					[self.cur_lang_code]
                  A__   				=					[self.eos_token_id]

           A__   				=					self.convert_ids_to_tokens(self.prefix_tokens)
           A__   				=					self.convert_ids_to_tokens(self.suffix_tokens)

           A__   				=					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  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			str)		->None:





           '''simple docstring'''

           A__   				=					self.convert_tokens_to_ids(UpperCAmelCase__)
           if self.legacy_behaviour:
                  A__   				=					[]
                  A__   				=					[self.eos_token_id, self.cur_lang_code]
           else:
                  A__   				=					[self.cur_lang_code]
                  A__   				=					[self.eos_token_id]

           A__   				=					self.convert_ids_to_tokens(self.prefix_tokens)
           A__   				=					self.convert_ids_to_tokens(self.suffix_tokens)

           A__   				=					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  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Optional[str] = None)		->Tuple[str]:





           '''simple docstring'''

           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(UpperCAmelCase__):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""")
                  return
           A__   				=					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__):
                  copyfile(self.vocab_file      ,				UpperCAmelCase__)

           return (out_vocab_file,)








from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCamelCase			:	str							 =     logging.get_logger(__name__)


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   ['''pixel_values''']


    def __init__(    self   :			List[Any]      ,				UpperCAmelCase__   :			bool = True      ,				UpperCAmelCase__   :			int = 32      ,				UpperCAmelCase__   :			Dict=PILImageResampling.BILINEAR      ,				UpperCAmelCase__   :			bool = True      ,				**UpperCAmelCase__   :			Optional[int]      ,				)		->None:





           '''simple docstring'''

           A__   				=					do_resize
           A__   				=					do_rescale
           A__   				=					size_divisor
           A__   				=					resample
           super().__init__(**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Optional[ChannelDimension] = None      ,				**UpperCAmelCase__   :			Dict)		->np.ndarray:





           '''simple docstring'''

           A__				,							A__   				=					get_image_size(UpperCAmelCase__)
           # Rounds the height and width down to the closest multiple of size_divisor
           A__   				=					height // size_divisor * size_divisor
           A__   				=					width // size_divisor * size_divisor
           A__   				=					resize(UpperCAmelCase__      ,				(new_h, new_w)      ,				resample=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)
           return image


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			float      ,				UpperCAmelCase__   :			Optional[ChannelDimension] = None      ,				**UpperCAmelCase__   :			List[str])		->np.ndarray:





           '''simple docstring'''

           return rescale(image=UpperCAmelCase__      ,				scale=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)





    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]      ,				UpperCAmelCase__   :			Optional[bool] = None      ,				UpperCAmelCase__   :			Optional[int] = None      ,				UpperCAmelCase__   :			Dict=None      ,				UpperCAmelCase__   :			Optional[bool] = None      ,				UpperCAmelCase__   :			Optional[Union[TensorType, str]] = None      ,				UpperCAmelCase__   :			ChannelDimension = ChannelDimension.FIRST      ,				**UpperCAmelCase__   :			List[str]      ,				)		->BatchFeature:





           '''simple docstring'''

           A__   				=					do_resize if do_resize is not None else self.do_resize
           A__   				=					do_rescale if do_rescale is not None else self.do_rescale
           A__   				=					size_divisor if size_divisor is not None else self.size_divisor
           A__   				=					resample if resample is not None else self.resample

           if do_resize and size_divisor is None:
                  raise ValueError('''size_divisor is required for resizing''')

           A__   				=					make_list_of_images(UpperCAmelCase__)

           if not valid_images(UpperCAmelCase__):
                  raise ValueError('''Invalid image(s)''')

           # All transformations expect numpy arrays.
           A__   				=					[to_numpy_array(UpperCAmelCase__) for img in images]

           if do_resize:
                  A__   				=					[self.resize(UpperCAmelCase__      ,				size_divisor=UpperCAmelCase__      ,				resample=UpperCAmelCase__) for image in images]

           if do_rescale:
                  A__   				=					[self.rescale(UpperCAmelCase__      ,				scale=1 / 255) for image in images]

           A__   				=					[to_channel_dimension_format(UpperCAmelCase__      ,				UpperCAmelCase__) for image in images]

           A__   				=					{'''pixel_values''': images}
           return BatchFeature(data=UpperCAmelCase__      ,				tensor_type=UpperCAmelCase__)


