





'''simple docstring'''


import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort






class    UpperCamelCase__	(		_a						,unittest.TestCase  ):
	lowerCAmelCase__       :      Optional[int]										=    "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"







	def     __a (					self      : str			,			lowerCamelCase      : Optional[int]=0		):




				'''simple docstring'''
				a__	 =			np.random.RandomState(lowerCamelCase__		)
				a__	 =			{
				    "prompt": "A painting of a squirrel eating a burger",
				    "generator": generator,
				    "num_inference_steps": 2,
				    "guidance_scale": 7.5,
				    "output_type": "numpy",
				}
				return inputs







	def     __a (					self      : str		):




				'''simple docstring'''
				a__	 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,			provider="CPUExecutionProvider"		)
				pipe.set_progress_bar_config(disable=lowerCamelCase__		)

				a__	 =			self.get_dummy_inputs()
				a__	 =			pipe(**lowerCamelCase__		).images
				a__	 =			image[0, -3:, -3:, -1]

				assert image.shape == (1, 1_2_8, 1_2_8, 3)
				a__	 =			np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455]		)

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







	def     __a (					self      : Optional[Any]		):




				'''simple docstring'''
				a__	 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,			provider="CPUExecutionProvider"		)
				a__	 =			PNDMScheduler.from_config(pipe.scheduler.config			,			skip_prk_steps=lowerCamelCase__		)
				pipe.set_progress_bar_config(disable=lowerCamelCase__		)

				a__	 =			self.get_dummy_inputs()
				a__	 =			pipe(**lowerCamelCase__		).images
				a__	 =			image[0, -3:, -3:, -1]

				assert image.shape == (1, 1_2_8, 1_2_8, 3)
				a__	 =			np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330]		)

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







	def     __a (					self      : Optional[Any]		):




				'''simple docstring'''
				a__	 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,			provider="CPUExecutionProvider"		)
				a__	 =			LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
				pipe.set_progress_bar_config(disable=lowerCamelCase__		)

				a__	 =			self.get_dummy_inputs()
				a__	 =			pipe(**lowerCamelCase__		).images
				a__	 =			image[0, -3:, -3:, -1]

				assert image.shape == (1, 1_2_8, 1_2_8, 3)
				a__	 =			np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]		)

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







	def     __a (					self      : str		):




				'''simple docstring'''
				a__	 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,			provider="CPUExecutionProvider"		)
				a__	 =			EulerDiscreteScheduler.from_config(pipe.scheduler.config		)
				pipe.set_progress_bar_config(disable=lowerCamelCase__		)

				a__	 =			self.get_dummy_inputs()
				a__	 =			pipe(**lowerCamelCase__		).images
				a__	 =			image[0, -3:, -3:, -1]

				assert image.shape == (1, 1_2_8, 1_2_8, 3)
				a__	 =			np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]		)

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







	def     __a (					self      : Optional[int]		):




				'''simple docstring'''
				a__	 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,			provider="CPUExecutionProvider"		)
				a__	 =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config		)
				pipe.set_progress_bar_config(disable=lowerCamelCase__		)

				a__	 =			self.get_dummy_inputs()
				a__	 =			pipe(**lowerCamelCase__		).images
				a__	 =			image[0, -3:, -3:, -1]

				assert image.shape == (1, 1_2_8, 1_2_8, 3)
				a__	 =			np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271]		)

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







	def     __a (					self      : Any		):




				'''simple docstring'''
				a__	 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,			provider="CPUExecutionProvider"		)
				a__	 =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
				pipe.set_progress_bar_config(disable=lowerCamelCase__		)

				a__	 =			self.get_dummy_inputs()
				a__	 =			pipe(**lowerCamelCase__		).images
				a__	 =			image[0, -3:, -3:, -1]

				assert image.shape == (1, 1_2_8, 1_2_8, 3)
				a__	 =			np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200]		)

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







	def     __a (					self      : Union[str, Any]		):




				'''simple docstring'''
				a__	 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,			provider="CPUExecutionProvider"		)
				pipe.set_progress_bar_config(disable=lowerCamelCase__		)

				a__	 =			self.get_dummy_inputs()
				a__	 =			3 * [inputs["prompt"]]

				# forward
				a__	 =			pipe(**lowerCamelCase__		)
				a__	 =			output.images[0, -3:, -3:, -1]

				a__	 =			self.get_dummy_inputs()
				a__	 =			3 * [inputs.pop("prompt"		)]

				a__	 =			pipe.tokenizer(
				    lowerCamelCase__			,			padding="max_length"			,			max_length=pipe.tokenizer.model_max_length			,			truncation=lowerCamelCase__			,			return_tensors="np"			,			)
				a__	 =			text_inputs["input_ids"]

				a__	 =			pipe.text_encoder(input_ids=text_inputs.astype(np.intaa		)		)[0]

				a__	 =			prompt_embeds

				# forward
				a__	 =			pipe(**lowerCamelCase__		)
				a__	 =			output.images[0, -3:, -3:, -1]

				assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()		).max() < 1e-4







	def     __a (					self      : Union[str, Any]		):




				'''simple docstring'''
				a__	 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,			provider="CPUExecutionProvider"		)
				pipe.set_progress_bar_config(disable=lowerCamelCase__		)

				a__	 =			self.get_dummy_inputs()
				a__	 =			3 * ["this is a negative prompt"]
				a__	 =			negative_prompt
				a__	 =			3 * [inputs["prompt"]]

				# forward
				a__	 =			pipe(**lowerCamelCase__		)
				a__	 =			output.images[0, -3:, -3:, -1]

				a__	 =			self.get_dummy_inputs()
				a__	 =			3 * [inputs.pop("prompt"		)]

				a__	 =			[]
				for p in [prompt, negative_prompt]:
							a__	 =			pipe.tokenizer(
							    lowerCamelCase__			,			padding="max_length"			,			max_length=pipe.tokenizer.model_max_length			,			truncation=lowerCamelCase__			,			return_tensors="np"			,			)
							a__	 =			text_inputs["input_ids"]

							embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa		)		)[0]		)

				a__	 =			embeds

				# forward
				a__	 =			pipe(**lowerCamelCase__		)
				a__	 =			output.images[0, -3:, -3:, -1]

				assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()		).max() < 1e-4






@nightly
@require_onnxruntime
@require_torch_gpu
class    UpperCamelCase__	(		unittest.TestCase  ):







	@property
	def     __a (					self      : Any		):




				'''simple docstring'''
				return (
				    "CUDAExecutionProvider",
				    {
				        "gpu_mem_limit": "15000000000",  # 15GB
				        "arena_extend_strategy": "kSameAsRequested",
				    },
				)







	@property
	def     __a (					self      : Union[str, Any]		):




				'''simple docstring'''
				a__	 =			ort.SessionOptions()
				a__	 =			False
				return options







	def     __a (					self      : Optional[int]		):




				'''simple docstring'''
				# using the PNDM scheduler by default
				a__	 =			OnnxStableDiffusionPipeline.from_pretrained(
				    "CompVis/stable-diffusion-v1-4"			,			revision="onnx"			,			safety_checker=lowerCamelCase__			,			feature_extractor=lowerCamelCase__			,			provider=self.gpu_provider			,			sess_options=self.gpu_options			,			)
				sd_pipe.set_progress_bar_config(disable=lowerCamelCase__		)

				a__	 =			"A painting of a squirrel eating a burger"
				np.random.seed(0		)
				a__	 =			sd_pipe([prompt]			,			guidance_scale=6.0			,			num_inference_steps=1_0			,			output_type="np"		)
				a__	 =			output.images

				a__	 =			image[0, -3:, -3:, -1]

				assert image.shape == (1, 5_1_2, 5_1_2, 3)
				a__	 =			np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3







	def     __a (					self      : Tuple		):




				'''simple docstring'''
				a__	 =			DDIMScheduler.from_pretrained(
				    "runwayml/stable-diffusion-v1-5"			,			subfolder="scheduler"			,			revision="onnx"		)
				a__	 =			OnnxStableDiffusionPipeline.from_pretrained(
				    "runwayml/stable-diffusion-v1-5"			,			revision="onnx"			,			scheduler=lowerCamelCase__			,			safety_checker=lowerCamelCase__			,			feature_extractor=lowerCamelCase__			,			provider=self.gpu_provider			,			sess_options=self.gpu_options			,			)
				sd_pipe.set_progress_bar_config(disable=lowerCamelCase__		)

				a__	 =			"open neural network exchange"
				a__	 =			np.random.RandomState(0		)
				a__	 =			sd_pipe([prompt]			,			guidance_scale=7.5			,			num_inference_steps=1_0			,			generator=lowerCamelCase__			,			output_type="np"		)
				a__	 =			output.images
				a__	 =			image[0, -3:, -3:, -1]

				assert image.shape == (1, 5_1_2, 5_1_2, 3)
				a__	 =			np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3







	def     __a (					self      : List[Any]		):




				'''simple docstring'''
				a__	 =			LMSDiscreteScheduler.from_pretrained(
				    "runwayml/stable-diffusion-v1-5"			,			subfolder="scheduler"			,			revision="onnx"		)
				a__	 =			OnnxStableDiffusionPipeline.from_pretrained(
				    "runwayml/stable-diffusion-v1-5"			,			revision="onnx"			,			scheduler=lowerCamelCase__			,			safety_checker=lowerCamelCase__			,			feature_extractor=lowerCamelCase__			,			provider=self.gpu_provider			,			sess_options=self.gpu_options			,			)
				sd_pipe.set_progress_bar_config(disable=lowerCamelCase__		)

				a__	 =			"open neural network exchange"
				a__	 =			np.random.RandomState(0		)
				a__	 =			sd_pipe([prompt]			,			guidance_scale=7.5			,			num_inference_steps=1_0			,			generator=lowerCamelCase__			,			output_type="np"		)
				a__	 =			output.images
				a__	 =			image[0, -3:, -3:, -1]

				assert image.shape == (1, 5_1_2, 5_1_2, 3)
				a__	 =			np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3







	def     __a (					self      : Any		):




				'''simple docstring'''
				a__	 =			0

				def test_callback_fn(lowerCamelCase      : Any			,			lowerCamelCase      : Optional[int]			,			lowerCamelCase      : List[str]		) -> None:
							a__	 =			True
							nonlocal number_of_steps
							number_of_steps += 1
							if step == 0:
										assert latents.shape == (1, 4, 6_4, 6_4)
										a__	 =			latents[0, -3:, -3:, -1]
										a__	 =			np.array(
										    [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]		)

										assert np.abs(latents_slice.flatten() - expected_slice		).max() < 1e-3
							elif step == 5:
										assert latents.shape == (1, 4, 6_4, 6_4)
										a__	 =			latents[0, -3:, -3:, -1]
										a__	 =			np.array(
										    [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]		)

										assert np.abs(latents_slice.flatten() - expected_slice		).max() < 1e-3

				a__	 =			False

				a__	 =			OnnxStableDiffusionPipeline.from_pretrained(
				    "runwayml/stable-diffusion-v1-5"			,			revision="onnx"			,			safety_checker=lowerCamelCase__			,			feature_extractor=lowerCamelCase__			,			provider=self.gpu_provider			,			sess_options=self.gpu_options			,			)
				pipe.set_progress_bar_config(disable=lowerCamelCase__		)

				a__	 =			"Andromeda galaxy in a bottle"

				a__	 =			np.random.RandomState(0		)
				pipe(
				    prompt=lowerCamelCase__			,			num_inference_steps=5			,			guidance_scale=7.5			,			generator=lowerCamelCase__			,			callback=lowerCamelCase__			,			callback_steps=1			,			)
				assert test_callback_fn.has_been_called
				assert number_of_steps == 6







	def     __a (					self      : Tuple		):




				'''simple docstring'''
				a__	 =			OnnxStableDiffusionPipeline.from_pretrained(
				    "runwayml/stable-diffusion-v1-5"			,			revision="onnx"			,			safety_checker=lowerCamelCase__			,			feature_extractor=lowerCamelCase__			,			provider=self.gpu_provider			,			sess_options=self.gpu_options			,			)
				assert isinstance(lowerCamelCase__			,			lowerCamelCase__		)
				assert pipe.safety_checker is None

				a__	 =			pipe("example prompt"			,			num_inference_steps=2		).images[0]
				assert image is not None

				# check that there's no error when saving a pipeline with one of the models being None
				with tempfile.TemporaryDirectory() as tmpdirname:
							pipe.save_pretrained(lowerCamelCase__		)
							a__	 =			OnnxStableDiffusionPipeline.from_pretrained(lowerCamelCase__		)

				# sanity check that the pipeline still works
				assert pipe.safety_checker is None
				a__	 =			pipe("example prompt"			,			num_inference_steps=2		).images[0]
				assert image is not None






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  :      Any			 =None


_lowerCAmelCase  :      Union[str, Any]			 =logging.get_logger(__name__)


_lowerCAmelCase  :      Dict			 ={"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase  :      Optional[int]			 ={
    """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""": 10_24,
    """facebook/nllb-200-distilled-600M""": 10_24,
}

# fmt: off
_lowerCAmelCase  :      Union[str, 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     (				_a		):






					'''simple docstring'''



					__magic_name__        =   VOCAB_FILES_NAMES
					__magic_name__        =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__magic_name__        =   PRETRAINED_VOCAB_FILES_MAP
					__magic_name__        =   ["input_ids", "attention_mask"]
					__magic_name__        =   NllbTokenizer

					__magic_name__        =   []
					__magic_name__        =   []





					def __init__(				self				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__="<s>"				,      lowerCamelCase__="</s>"				,      lowerCamelCase__="</s>"				,      lowerCamelCase__="<s>"				,      lowerCamelCase__="<unk>"				,      lowerCamelCase__="<pad>"				,      lowerCamelCase__="<mask>"				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=False				,      **lowerCamelCase__				,      ):
						# Mask token behave like a normal word, i.e. include the space before it
						UpperCAmelCase__:	int									= AddedToken(lowerCamelCase__				,      lstrip=lowerCamelCase__				,      rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__				,      lowerCamelCase__						) else mask_token
						UpperCAmelCase__:	Tuple									= legacy_behaviour
						super().__init__(
						    vocab_file=lowerCamelCase__				,      tokenizer_file=lowerCamelCase__				,      bos_token=lowerCamelCase__				,      eos_token=lowerCamelCase__				,      sep_token=lowerCamelCase__				,      cls_token=lowerCamelCase__				,      unk_token=lowerCamelCase__				,      pad_token=lowerCamelCase__				,      mask_token=lowerCamelCase__				,      src_lang=lowerCamelCase__				,      tgt_lang=lowerCamelCase__				,      additional_special_tokens=lowerCamelCase__				,      legacy_behaviour=lowerCamelCase__				,      **lowerCamelCase__				,      )

						UpperCAmelCase__:	Optional[int]									= vocab_file
						UpperCAmelCase__:	Tuple									= False if not self.vocab_file else True

						UpperCAmelCase__:	str									= 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__:	Optional[int]									= {
						    lang_code: self.convert_tokens_to_ids(lowerCamelCase__						) for lang_code in FAIRSEQ_LANGUAGE_CODES
						}

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





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





					@src_lang.setter
					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Any									= new_src_lang
						self.set_src_lang_special_tokens(self._src_lang						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = 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    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None						):
						UpperCAmelCase__:	List[Any]									= [self.sep_token_id]
						UpperCAmelCase__:	Tuple									= [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				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						):
						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__:	str									= src_lang
						UpperCAmelCase__:	Any									= self(lowerCamelCase__				,      add_special_tokens=lowerCamelCase__				,      return_tensors=lowerCamelCase__				,      **lowerCamelCase__						)
						UpperCAmelCase__:	List[Any]									= self.convert_tokens_to_ids(lowerCamelCase__						)
						UpperCAmelCase__:	int									= tgt_lang_id
						return inputs





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = "eng_Latn"				,      lowerCamelCase__ = None				,      lowerCamelCase__ = "fra_Latn"				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__:	Union[str, Any]									= src_lang
						UpperCAmelCase__:	Optional[int]									= tgt_lang
						return super().prepare_seqaseq_batch(lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						return self.set_src_lang_special_tokens(self.src_lang						)





					def    _UpperCAmelCase    (				self						):
						return self.set_tgt_lang_special_tokens(self.tgt_lang						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	int									= self.convert_tokens_to_ids(lowerCamelCase__						)

						if self.legacy_behaviour:
							UpperCAmelCase__:	List[str]									= []
							UpperCAmelCase__:	List[Any]									= [self.eos_token_id, self.cur_lang_code]
						else:
							UpperCAmelCase__:	Optional[Any]									= [self.cur_lang_code]
							UpperCAmelCase__:	List[Any]									= [self.eos_token_id]

						UpperCAmelCase__:	Optional[Any]									= self.convert_ids_to_tokens(self.prefix_tokens						)
						UpperCAmelCase__:	Optional[Any]									= self.convert_ids_to_tokens(self.suffix_tokens						)

						UpperCAmelCase__:	Any									= 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    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[int]									= self.convert_tokens_to_ids(lowerCamelCase__						)
						if self.legacy_behaviour:
							UpperCAmelCase__:	int									= []
							UpperCAmelCase__:	str									= [self.eos_token_id, self.cur_lang_code]
						else:
							UpperCAmelCase__:	Tuple									= [self.cur_lang_code]
							UpperCAmelCase__:	Tuple									= [self.eos_token_id]

						UpperCAmelCase__:	Union[str, Any]									= self.convert_ids_to_tokens(self.prefix_tokens						)
						UpperCAmelCase__:	Optional[int]									= self.convert_ids_to_tokens(self.suffix_tokens						)

						UpperCAmelCase__:	Optional[Any]									= 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    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None						):
						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(lowerCamelCase__						):
							logger.error(F"Vocabulary path ({save_directory}) should be a directory."						)
							return
						UpperCAmelCase__:	str									= os.path.join(
						    lowerCamelCase__				,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(lowerCamelCase__						):
							copyfile(self.vocab_file				,      lowerCamelCase__						)

						return (out_vocab_file,)