




'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class    __lowercase						(  __magic_name__       ,							unittest.TestCase ):
   _a							=      KandinskyVaaInpaintPipeline
   _a							=      ["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
   _a							=      [
       """image_embeds""",
       """negative_image_embeds""",
       """image""",
       """mask_image""",
   ]
   _a							=      [
       """generator""",
       """height""",
       """width""",
       """latents""",
       """guidance_scale""",
       """num_inference_steps""",
       """return_dict""",
       """guidance_scale""",
       """num_images_per_prompt""",
       """output_type""",
       """return_dict""",
   ]
   _a							=      False







   @property
   def 		UpperCamelCase__			(	self      )				->    int:
     return 32







   @property
   def 		UpperCamelCase__			(	self      )				->    Any:
     return 32







   @property
   def 		UpperCamelCase__			(	self      )				->    int:
     return self.time_input_dim







   @property
   def 		UpperCamelCase__			(	self      )				->    int:
     return self.time_input_dim * 4







   @property
   def 		UpperCamelCase__			(	self      )				->    List[str]:
     return 100







   @property
   def 		UpperCamelCase__			(	self      )				->    int:
     torch.manual_seed(0      )

     __a			=					{
         'in_channels': 9,
         # Out channels is double in channels because predicts mean and variance
         'out_channels': 8,
         'addition_embed_type': 'image',
         'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
         'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
         'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
         'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
         'layers_per_block': 1,
         'encoder_hid_dim': self.text_embedder_hidden_size,
         'encoder_hid_dim_type': 'image_proj',
         'cross_attention_dim': self.cross_attention_dim,
         'attention_head_dim': 4,
         'resnet_time_scale_shift': 'scale_shift',
         'class_embed_type': None,
     }

     __a			=					UNetaDConditionModel(**UpperCamelCase      )
     return model







   @property
   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     return {
         "block_out_channels": [32, 64],
         "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": [
             "AttnUpDecoderBlock2D",
             "UpDecoderBlock2D",
         ],
         "vq_embed_dim": 4,
     }







   @property
   def 		UpperCamelCase__			(	self      )				->    str:
     torch.manual_seed(0      )
     __a			=					VQModel(**self.dummy_movq_kwargs      )
     return model







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     __a			=					self.dummy_unet
     __a			=					self.dummy_movq

     __a			=					DDIMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='linear'      ,							beta_start=0.00_085      ,							beta_end=0.012      ,							clip_sample=UpperCamelCase      ,							set_alpha_to_one=UpperCamelCase      ,							steps_offset=1      ,							prediction_type='epsilon'      ,							thresholding=UpperCamelCase      ,							)

     __a			=					{
         'unet': unet,
         'scheduler': scheduler,
         'movq': movq,
     }

     return components







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=0      )				->    str:
     __a			=					floats_tensor((1, self.text_embedder_hidden_size)      ,							rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
     __a			=					floats_tensor((1, self.text_embedder_hidden_size)      ,							rng=random.Random(seed + 1      )      ).to(
         UpperCamelCase      )
     # create init_image
     __a			=					floats_tensor((1, 3, 64, 64)      ,							rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
     __a			=					image.cpu().permute(0      ,							2      ,							3      ,							1      )[0]
     __a			=					Image.fromarray(np.uinta(UpperCamelCase      )      ).convert('RGB'      ).resize((256, 256)      )
     # create mask
     __a			=					np.ones((64, 64)      ,							dtype=np.floataa      )
     __a			=					0

     if str(UpperCamelCase      ).startswith('mps'      ):
       __a			=					torch.manual_seed(UpperCamelCase      )
     else:
       __a			=					torch.Generator(device=UpperCamelCase      ).manual_seed(UpperCamelCase      )
     __a			=					{
         'image': init_image,
         'mask_image': mask,
         'image_embeds': image_embeds,
         'negative_image_embeds': negative_image_embeds,
         'generator': generator,
         'height': 64,
         'width': 64,
         'num_inference_steps': 2,
         'guidance_scale': 4.0,
         'output_type': 'np',
     }
     return inputs







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					'cpu'

     __a			=					self.get_dummy_components()

     __a			=					self.pipeline_class(**UpperCamelCase      )
     __a			=					pipe.to(UpperCamelCase      )

     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					pipe(**self.get_dummy_inputs(UpperCamelCase      )      )
     __a			=					output.images

     __a			=					pipe(
         **self.get_dummy_inputs(UpperCamelCase      )      ,							return_dict=UpperCamelCase      ,							)[0]

     __a			=					image[0, -3:, -3:, -1]
     __a			=					image_from_tuple[0, -3:, -3:, -1]

     print(f"image.shape {image.shape}"      )

     assert image.shape == (1, 64, 64, 3)

     __a			=					np.array(
         [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]      )

     assert (
         np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
     ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
     ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3      )





@slow
@require_torch_gpu
class    __lowercase						(  unittest.TestCase ):







   def 		UpperCamelCase__			(	self      )				->    Dict:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'      )

     __a			=					load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'      )
     __a			=					np.ones((768, 768)      ,							dtype=np.floataa      )
     __a			=					0

     __a			=					'a hat'

     __a			=					KandinskyVaaPriorPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-2-prior'      ,							torch_dtype=torch.floataa      )
     pipe_prior.to(UpperCamelCase      )

     __a			=					KandinskyVaaInpaintPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-2-decoder-inpaint'      ,							torch_dtype=torch.floataa      )
     __a			=					pipeline.to(UpperCamelCase      )
     pipeline.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					torch.Generator(device='cpu'      ).manual_seed(0      )
     __a	,       __a			=					pipe_prior(
         UpperCamelCase      ,							generator=UpperCamelCase      ,							num_inference_steps=5      ,							negative_prompt=''      ,							).to_tuple()

     __a			=					pipeline(
         image=UpperCamelCase      ,							mask_image=UpperCamelCase      ,							image_embeds=UpperCamelCase      ,							negative_image_embeds=UpperCamelCase      ,							generator=UpperCamelCase      ,							num_inference_steps=100      ,							height=768      ,							width=768      ,							output_type='np'      ,							)

     __a			=					output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(UpperCamelCase      ,							UpperCamelCase      )









'''simple docstring'''


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


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      "▁"

UpperCAmelCase_ 		=      {"vocab_file": "sentencepiece.bpe.model"}

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

UpperCAmelCase_ 		=      {
    "facebook/nllb-200-distilled-600M": 10_24,
}

# fmt: off
UpperCAmelCase_ 		=      ["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    __lowercase						(  __magic_name__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      ["""input_ids""", """attention_mask"""]

   _a							=      []
   _a							=      []







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

     __a			=					{} if sp_model_kwargs is None else sp_model_kwargs
     __a			=					legacy_behaviour

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

     __a			=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.Load(str(UpperCamelCase      )      )
     __a			=					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
     __a			=					{'<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
     __a			=					1

     __a			=					len(self.sp_model      )
     __a			=					{
         code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase      )
     }
     __a			=					{v: k for k, v in self.lang_code_to_id.items()}
     __a			=					len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset

     self.fairseq_tokens_to_ids.update(self.lang_code_to_id      )
     __a			=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}
     __a			=					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]      )

     __a			=					src_lang if src_lang is not None else 'eng_Latn'
     __a			=					self.lang_code_to_id[self._src_lang]
     __a			=					tgt_lang
     self.set_src_lang_special_tokens(self._src_lang      )







   def __getstate__(	self      )				->    Union[str, Any]:
     __a			=					self.__dict__.copy()
     __a			=					None
     __a			=					self.sp_model.serialized_model_proto()
     return state







   def __setstate__(	self      ,							UpperCamelCase      )				->    str:
     __a			=					d

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

     __a			=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )







   @property
   def 		UpperCamelCase__			(	self      )				->    str:
     return len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset + 1  # Plus 1 for the mask token







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







   @src_lang.setter
   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    None:
     __a			=					new_src_lang
     self.set_src_lang_special_tokens(self._src_lang      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = False      )				->    List[int]:

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

     __a			=					[1] * len(self.prefix_tokens      )
     __a			=					[1] * len(self.suffix_tokens      )
     if token_ids_a is None:
       return prefix_ones + ([0] * len(UpperCamelCase      )) + suffix_ones
     return prefix_ones + ([0] * len(UpperCamelCase      )) + ([0] * len(UpperCamelCase      )) + suffix_ones







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = 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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     __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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )				->    Dict:
     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 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					{self.convert_ids_to_tokens(UpperCamelCase      ): i for i in range(self.vocab_size      )}
     vocab.update(self.added_tokens_encoder      )
     return vocab







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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Optional[Any]:
     if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
     __a			=					self.sp_model.PieceToId(UpperCamelCase      )

     # 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 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Any:
     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 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    str:
     __a			=					''.join(UpperCamelCase      ).replace(UpperCamelCase      ,							' '      ).strip()
     return out_string







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Tuple[str]:
     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      ) and os.path.isfile(self.vocab_file      ):
       copyfile(self.vocab_file      ,							UpperCamelCase      )
     elif not os.path.isfile(self.vocab_file      ):
       with open(UpperCamelCase      ,							'wb'      ) as fi:
         __a			=					self.sp_model.serialized_model_proto()
         fi.write(UpperCamelCase      )

     return (out_vocab_file,)







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = "eng_Latn"      ,							UpperCamelCase = None      ,							UpperCamelCase = "fra_Latn"      ,							**UpperCamelCase      ,							)				->    BatchEncoding:
     __a			=					src_lang
     __a			=					tgt_lang
     return super().prepare_seqaseq_batch(UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     return self.set_src_lang_special_tokens(self.src_lang      )







   def 		UpperCamelCase__			(	self      )				->    int:
     return self.set_tgt_lang_special_tokens(self.tgt_lang      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    None:
     __a			=					self.lang_code_to_id[src_lang]
     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]







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    None:
     __a			=					self.lang_code_to_id[lang]
     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]



