

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class     _a						(__magic_name__		,   __magic_name__		,   __magic_name__		,   unittest.TestCase				):


					'''simple docstring'''






					UpperCAmelCase__:   Any  							=       StableDiffusionInpaintPipeline
					UpperCAmelCase__:   Dict  							=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					UpperCAmelCase__:   int  							=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					UpperCAmelCase__:   Optional[int]  							=       frozenset(
					    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					UpperCAmelCase__:   Tuple  							=       frozenset([]				)






					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    Union[str, Any]        =		UNetaDConditionModel(
						    block_out_channels=(32, 64)    ,       layers_per_block=2    ,       sample_size=32    ,       in_channels=9    ,       out_channels=4    ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,       cross_attention_dim=32    ,       attention_head_dim=(2, 4)    ,       use_linear_projection=A__    ,       )
						A__							:    Any        =		PNDMScheduler(skip_prk_steps=A__			)
						torch.manual_seed(0			)
						A__							:    Any        =		AutoencoderKL(
						    block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,       latent_channels=4    ,       sample_size=128    ,       )
						torch.manual_seed(0			)
						A__							:    Tuple        =		CLIPTextConfig(
						    bos_token_id=0    ,       eos_token_id=2    ,       hidden_size=32    ,       intermediate_size=37    ,       layer_norm_eps=1e-05    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       pad_token_id=1    ,       vocab_size=1000    ,       hidden_act="""gelu"""    ,       projection_dim=512    ,       )
						A__							:    Dict        =		CLIPTextModel(A__			)
						A__							:    List[Any]        =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

						A__							:    Optional[Any]        =		{
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """vae""": vae,
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """safety_checker""": None,
						    """feature_extractor""": None,
						}
						return components






					def   __A			( self    ,       A__    ,       A__=0			):
						# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
						A__							:    List[str]        =		floats_tensor((1, 3, 32, 32)    ,       rng=random.Random(A__			)			).to(A__			)
						A__							:    Dict        =		image.cpu().permute(0    ,       2    ,       3    ,       1			)[0]
						A__							:    Union[str, Any]        =		Image.fromarray(np.uinta(A__			)			).convert("""RGB"""			).resize((64, 64)			)
						A__							:    Any        =		Image.fromarray(np.uinta(image + 4			)			).convert("""RGB"""			).resize((64, 64)			)
						if str(A__			).startswith("""mps"""			):
							A__							:    List[str]        =		torch.manual_seed(A__			)
						else:
							A__							:    int        =		torch.Generator(device=A__			).manual_seed(A__			)
						A__							:    Tuple        =		{
						    """prompt""": """A painting of a squirrel eating a burger""",
						    """image""": init_image,
						    """mask_image""": mask_image,
						    """generator""": generator,
						    """num_inference_steps""": 2,
						    """guidance_scale""": 6.0,
						    """output_type""": """numpy""",
						}
						return inputs






					def   __A			( self			):
						A__							:    str        =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
						A__							:    Optional[Any]        =		self.get_dummy_components()
						A__							:    List[Any]        =		StableDiffusionInpaintPipeline(**A__			)
						A__							:    Optional[Any]        =		sd_pipe.to(A__			)
						sd_pipe.set_progress_bar_config(disable=A__			)

						A__							:    Optional[Any]        =		self.get_dummy_inputs(A__			)
						A__							:    List[Any]        =		sd_pipe(**A__			).images
						A__							:    List[Any]        =		image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						A__							:    Optional[Any]        =		np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]			)

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






					def   __A			( self			):
						super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



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


					'''simple docstring'''






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






					def   __A			( self			):
						A__							:    Tuple        =		load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/sd2-inpaint/init_image.png"""			)
						A__							:    Any        =		load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
						A__							:    Union[str, Any]        =		load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
						    """/yellow_cat_sitting_on_a_park_bench.npy"""			)

						A__							:    int        =		"""stabilityai/stable-diffusion-2-inpainting"""
						A__							:    List[str]        =		StableDiffusionInpaintPipeline.from_pretrained(A__    ,       safety_checker=A__			)
						pipe.to(A__			)
						pipe.set_progress_bar_config(disable=A__			)
						pipe.enable_attention_slicing()

						A__							:    int        =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    Tuple        =		pipe(
						    prompt=A__    ,       image=A__    ,       mask_image=A__    ,       generator=A__    ,       output_type="""np"""    ,       )
						A__							:    int        =		output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image			).max() < 9e-3






					def   __A			( self			):
						A__							:    str        =		load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/sd2-inpaint/init_image.png"""			)
						A__							:    Union[str, Any]        =		load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
						A__							:    Optional[int]        =		load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
						    """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""			)

						A__							:    Optional[int]        =		"""stabilityai/stable-diffusion-2-inpainting"""
						A__							:    Any        =		StableDiffusionInpaintPipeline.from_pretrained(
						    A__    ,       torch_dtype=torch.floataa    ,       safety_checker=A__    ,       )
						pipe.to(A__			)
						pipe.set_progress_bar_config(disable=A__			)
						pipe.enable_attention_slicing()

						A__							:    List[str]        =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

						A__							:    List[str]        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		pipe(
						    prompt=A__    ,       image=A__    ,       mask_image=A__    ,       generator=A__    ,       output_type="""np"""    ,       )
						A__							:    Union[str, Any]        =		output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image			).max() < 5e-1






					def   __A			( self			):
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						A__							:    Any        =		load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/sd2-inpaint/init_image.png"""			)
						A__							:    List[str]        =		load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)

						A__							:    Union[str, Any]        =		"""stabilityai/stable-diffusion-2-inpainting"""
						A__							:    List[str]        =		PNDMScheduler.from_pretrained(A__    ,       subfolder="""scheduler"""			)
						A__							:    List[Any]        =		StableDiffusionInpaintPipeline.from_pretrained(
						    A__    ,       safety_checker=A__    ,       scheduler=A__    ,       torch_dtype=torch.floataa    ,       )
						pipe.to(A__			)
						pipe.set_progress_bar_config(disable=A__			)
						pipe.enable_attention_slicing(1			)
						pipe.enable_sequential_cpu_offload()

						A__							:    Union[str, Any]        =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

						A__							:    Any        =		torch.manual_seed(0			)
						A__							:    str        =		pipe(
						    prompt=A__    ,       image=A__    ,       mask_image=A__    ,       generator=A__    ,       num_inference_steps=2    ,       output_type="""np"""    ,       )

						A__							:    int        =		torch.cuda.max_memory_allocated()
						# make sure that less than 2.65 GB is allocated
						assert mem_bytes < 2.6_5 * 10**9






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


A_       : str         =			logging.get_logger(__name__)

A_       : Optional[Any]         =			'▁'

A_       : int         =			{'vocab_file': 'sentencepiece.bpe.model'}

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

A_       : Optional[int]         =			{
    'facebook/nllb-200-distilled-600M': 1024,
}

# fmt: off
A_       : Tuple         =			['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						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Optional[Any]  							=       VOCAB_FILES_NAMES
					UpperCAmelCase__:   Any  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:   Optional[Any]  							=       PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:   str  							=       ['''input_ids''', '''attention_mask''']

					UpperCAmelCase__:   List[int]  							=       []
					UpperCAmelCase__:   List[int]  							=       []






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

						A__							:    List[Any]        =		{} if sp_model_kwargs is None else sp_model_kwargs
						A__							:    List[str]        =		legacy_behaviour

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

						A__							:    List[Any]        =		spm.SentencePieceProcessor(**self.sp_model_kwargs			)
						self.sp_model.Load(str(A__			)			)
						A__							:    List[str]        =		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__							:    str        =		{"""<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__							:    str        =		1

						A__							:    Optional[int]        =		len(self.sp_model			)
						A__							:    List[Any]        =		{
						    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(A__			)
						}
						A__							:    Tuple        =		{v: k for k, v in self.lang_code_to_id.items()}
						A__							:    Dict        =		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__							:    Tuple        =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}
						A__							:    int        =		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__							:    int        =		src_lang if src_lang is not None else """eng_Latn"""
						A__							:    str        =		self.lang_code_to_id[self._src_lang]
						A__							:    Union[str, Any]        =		tgt_lang
						self.set_src_lang_special_tokens(self._src_lang			)






					def __getstate__( self			):
						A__							:    Tuple        =		self.__dict__.copy()
						A__							:    List[Any]        =		None
						A__							:    Tuple        =		self.sp_model.serialized_model_proto()
						return state






					def __setstate__( self    ,       A__			):
						A__							:    str        =		d

						# for backward compatibility
						if not hasattr(self    ,       """sp_model_kwargs"""			):
							A__							:    Any        =		{}

						A__							:    List[str]        =		spm.SentencePieceProcessor(**self.sp_model_kwargs			)
						self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)






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






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






					@src_lang.setter
					def   __A			( self    ,       A__			):
						A__							:    str        =		new_src_lang
						self.set_src_lang_special_tokens(self._src_lang			)






					def   __A			( self    ,       A__    ,       A__ = None    ,       A__ = False			):

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

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






					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			):
						A__							:    Dict        =		[self.sep_token_id]
						A__							:    Dict        =		[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"""			)
						A__							:    Optional[int]        =		src_lang
						A__							:    List[Any]        =		self(A__    ,       add_special_tokens=A__    ,       return_tensors=A__    ,       **A__			)
						A__							:    Optional[int]        =		self.convert_tokens_to_ids(A__			)
						A__							:    Optional[int]        =		tgt_lang_id
						return inputs






					def   __A			( self			):
						A__							:    List[str]        =		{self.convert_ids_to_tokens(A__			): i for i in range(self.vocab_size			)}
						vocab.update(self.added_tokens_encoder			)
						return vocab






					def   __A			( self    ,       A__			):
						return self.sp_model.encode(A__    ,       out_type=A__			)






					def   __A			( self    ,       A__			):
						if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
						A__							:    List[str]        =		self.sp_model.PieceToId(A__			)

						# 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   __A			( self    ,       A__			):
						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   __A			( self    ,       A__			):
						A__							:    Optional[Any]        =		"""""".join(A__			).replace(A__    ,       """ """			).strip()
						return out_string






					def   __A			( self    ,       A__    ,       A__ = None			):
						if not os.path.isdir(A__			):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
							return
						A__							:    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__			) and os.path.isfile(self.vocab_file			):
							copyfile(self.vocab_file    ,       A__			)
						elif not os.path.isfile(self.vocab_file			):
							with open(A__    ,       """wb"""			) as fi:
								A__							:    str        =		self.sp_model.serialized_model_proto()
								fi.write(A__			)

						return (out_vocab_file,)






					def   __A			( self    ,       A__    ,       A__ = "eng_Latn"    ,       A__ = None    ,       A__ = "fra_Latn"    ,       **A__    ,       ):
						A__							:    Any        =		src_lang
						A__							:    List[Any]        =		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__			):
						A__							:    List[str]        =		self.lang_code_to_id[src_lang]
						if self.legacy_behaviour:
							A__							:    Dict        =		[]
							A__							:    str        =		[self.eos_token_id, self.cur_lang_code]
						else:
							A__							:    List[str]        =		[self.cur_lang_code]
							A__							:    Optional[Any]        =		[self.eos_token_id]






					def   __A			( self    ,       A__			):
						A__							:    Union[str, Any]        =		self.lang_code_to_id[lang]
						if self.legacy_behaviour:
							A__							:    Union[str, Any]        =		[]
							A__							:    int        =		[self.eos_token_id, self.cur_lang_code]
						else:
							A__							:    Dict        =		[self.cur_lang_code]
							A__							:    str        =		[self.eos_token_id]



