






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:
							SCREAMING_SNAKE_CASE__				     =			None


SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)


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

SCREAMING_SNAKE_CASE__				     =			{
    '''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'''
        ),
    },
}

SCREAMING_SNAKE_CASE__				     =			{
    '''facebook/nllb-large-en-ro''': 1024,
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# fmt: off
SCREAMING_SNAKE_CASE__				     =			['''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(       __lowerCamelCase		):
		__SCREAMING_SNAKE_CASE  :       Tuple							=							VOCAB_FILES_NAMES
		__SCREAMING_SNAKE_CASE  :       int							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__SCREAMING_SNAKE_CASE  :       List[Any]							=							PRETRAINED_VOCAB_FILES_MAP
		__SCREAMING_SNAKE_CASE  :       Optional[int]							=							['''input_ids''', '''attention_mask''']
		__SCREAMING_SNAKE_CASE  :       Union[str, Any]							=							NllbTokenizer

		__SCREAMING_SNAKE_CASE  :       List[int]							=							[]
		__SCREAMING_SNAKE_CASE  :       List[int]							=							[]

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



					'''simple docstring'''



					__a						:  Tuple									=     AddedToken(SCREAMING_SNAKE_CASE__    ,       lstrip=SCREAMING_SNAKE_CASE__    ,       rstrip=SCREAMING_SNAKE_CASE__    ) if isinstance(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ) else mask_token
					__a						:  Union[str, Any]									=     legacy_behaviour
					super().__init__(
					    vocab_file=SCREAMING_SNAKE_CASE__    ,       tokenizer_file=SCREAMING_SNAKE_CASE__    ,       bos_token=SCREAMING_SNAKE_CASE__    ,       eos_token=SCREAMING_SNAKE_CASE__    ,       sep_token=SCREAMING_SNAKE_CASE__    ,       cls_token=SCREAMING_SNAKE_CASE__    ,       unk_token=SCREAMING_SNAKE_CASE__    ,       pad_token=SCREAMING_SNAKE_CASE__    ,       mask_token=SCREAMING_SNAKE_CASE__    ,       src_lang=SCREAMING_SNAKE_CASE__    ,       tgt_lang=SCREAMING_SNAKE_CASE__    ,       additional_special_tokens=SCREAMING_SNAKE_CASE__    ,       legacy_behaviour=SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    ,       )

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

					__a						:  Union[str, Any]									=     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						:  List[Any]									=     {
					    lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    ) for lang_code in FAIRSEQ_LANGUAGE_CODES
					}

					__a						:  Any									=     src_lang if src_lang is not None else 'eng_Latn'
					__a						:  Union[str, Any]									=     self.convert_tokens_to_ids(self._src_lang    )
					__a						:  List[Any]									=     tgt_lang
					self.set_src_lang_special_tokens(self._src_lang    )

		@property
		def    __lowerCAmelCase  (					self		:    Tuple    ):



					'''simple docstring'''



					return self._src_lang

		@src_lang.setter
		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     new_src_lang
					self.set_src_lang_special_tokens(self._src_lang    )

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    List[int]    ,       SCREAMING_SNAKE_CASE__		:    Optional[List[int]] = None    ):



					'''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    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    List[int]    ,       SCREAMING_SNAKE_CASE__		:    Optional[List[int]] = None    ):



					'''simple docstring'''



					__a						:  Dict									=     [self.sep_token_id]
					__a						:  List[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    __lowerCAmelCase  (					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    Optional[str]    ,       SCREAMING_SNAKE_CASE__		:    Optional[str]    ,       **SCREAMING_SNAKE_CASE__		:    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						:  List[Any]									=     src_lang
					__a						:  Tuple									=     self(SCREAMING_SNAKE_CASE__    ,       add_special_tokens=SCREAMING_SNAKE_CASE__    ,       return_tensors=SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    )
					__a						:  int									=     self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    )
					__a						:  Optional[int]									=     tgt_lang_id
					return inputs

		def    __lowerCAmelCase  (					self		:    int    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    str = "eng_Latn"    ,       SCREAMING_SNAKE_CASE__		:    Optional[List[str]] = None    ,       SCREAMING_SNAKE_CASE__		:    str = "fra_Latn"    ,       **SCREAMING_SNAKE_CASE__		:    Optional[int]    ,       ):



					'''simple docstring'''



					__a						:  Optional[Any]									=     src_lang
					__a						:  Optional[int]									=     tgt_lang
					return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    List[Any]    ):



					'''simple docstring'''



					return self.set_src_lang_special_tokens(self.src_lang    )

		def    __lowerCAmelCase  (					self		:    Dict    ):



					'''simple docstring'''



					return self.set_tgt_lang_special_tokens(self.tgt_lang    )

		def    __lowerCAmelCase  (					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    int    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    )

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

					__a						:  str									=     self.convert_ids_to_tokens(self.prefix_tokens    )
					__a						:  int									=     self.convert_ids_to_tokens(self.suffix_tokens    )

					__a						:  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    __lowerCAmelCase  (					self		:    Any    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    )
					if self.legacy_behaviour:
								__a						:  Optional[Any]									=     []
								__a						:  int									=     [self.eos_token_id, self.cur_lang_code]
					else:
								__a						:  Optional[int]									=     [self.cur_lang_code]
								__a						:  List[str]									=     [self.eos_token_id]

					__a						:  Union[str, Any]									=     self.convert_ids_to_tokens(self.prefix_tokens    )
					__a						:  Dict									=     self.convert_ids_to_tokens(self.suffix_tokens    )

					__a						:  Dict									=     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    __lowerCAmelCase  (					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    Optional[str] = None    ):



					'''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(SCREAMING_SNAKE_CASE__    ):
								logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''    )
								return
					__a						:  Tuple									=     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__    ):
								copyfile(self.vocab_file    ,       SCREAMING_SNAKE_CASE__    )

					return (out_vocab_file,)







"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 						a__		( unittest.TestCase		):
   @property
   def      __magic_name__     (     self							):
          torch.manual_seed(0							)
          lowercase       :							str									=				UNetaDModel(
              block_out_channels=(32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=("DownBlock2D", "AttnDownBlock2D")      ,       up_block_types=("AttnUpBlock2D", "UpBlock2D")      ,       )
          return model

   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				self.dummy_uncond_unet
          lowercase       :							Dict									=				KarrasVeScheduler()

          lowercase       :							Any									=				KarrasVePipeline(unet=_a      ,       scheduler=_a							)
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Tuple									=				torch.manual_seed(0							)
          lowercase       :							Tuple									=				pipe(num_inference_steps=2      ,       generator=_a      ,       output_type="numpy"							).images

          lowercase       :							int									=				torch.manual_seed(0							)
          lowercase       :							Tuple									=				pipe(num_inference_steps=2      ,       generator=_a      ,       output_type="numpy"      ,       return_dict=_a							)[0]

          lowercase       :							Any									=				image[0, -3:, -3:, -1]
          lowercase       :							Dict									=				image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase       :							Tuple									=				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2


@slow
@require_torch
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				"google/ncsnpp-celebahq-256"
          lowercase       :							str									=				UNetaDModel.from_pretrained(_a							)
          lowercase       :							Optional[Any]									=				KarrasVeScheduler()

          lowercase       :							Union[str, Any]									=				KarrasVePipeline(unet=_a      ,       scheduler=_a							)
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Tuple									=				torch.manual_seed(0							)
          lowercase       :							Dict									=				pipe(num_inference_steps=20      ,       generator=_a      ,       output_type="numpy"							).images

          lowercase       :							Optional[Any]									=				image[0, -3:, -3:, -1]
          assert image.shape == (1, 256, 256, 3)
          lowercase       :							Optional[Any]									=				np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]							)

          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2


