


"""simple docstring"""


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()



class   _a      (      unittest.TestCase ):
    a_    :				Dict             =    StableDiffusionLDMaDPipeline
    a_    :				int             =    TEXT_TO_IMAGE_PARAMS
    a_    :				Union[str, Any]             =    TEXT_TO_IMAGE_BATCH_PARAMS
    a_    :				Optional[Any]             =    TEXT_TO_IMAGE_IMAGE_PARAMS
    def      _UpperCamelCase       ( self							:					List[Any]				):
          torch.manual_seed(0				)
          lowerCamelCase__           =		UNetaDConditionModel(
              block_out_channels=(32, 64)							,					layers_per_block=2							,					sample_size=32							,					in_channels=4							,					out_channels=4							,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,					up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,					cross_attention_dim=32							,					)
          lowerCamelCase__           =		DDIMScheduler(
              beta_start=0.0_00_85							,					beta_end=0.0_12							,					beta_schedule='scaled_linear'							,					clip_sample=SCREAMING_SNAKE_CASE__							,					set_alpha_to_one=SCREAMING_SNAKE_CASE__							,					)
          torch.manual_seed(0				)
          lowerCamelCase__           =		AutoencoderKL(
              block_out_channels=[32, 64]							,					in_channels=6							,					out_channels=6							,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,					latent_channels=4							,					)
          torch.manual_seed(0				)
          lowerCamelCase__           =		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=10_00							,					)
          lowerCamelCase__           =		CLIPTextModel(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

          lowerCamelCase__           =		{
              'unet': unet,
              'scheduler': scheduler,
              'vae': vae,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'safety_checker': None,
              'feature_extractor': None,
          }
          return components
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=0				):
          if str(SCREAMING_SNAKE_CASE__				).startswith('mps'				):
                lowerCamelCase__           =		torch.manual_seed(SCREAMING_SNAKE_CASE__				)
          else:
                lowerCamelCase__           =		torch.Generator(device=SCREAMING_SNAKE_CASE__				).manual_seed(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		{
              'prompt': 'A painting of a squirrel eating a burger',
              'generator': generator,
              'num_inference_steps': 2,
              'guidance_scale': 6.0,
              'output_type': 'numpy',
          }
          return inputs
    def      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__           =		'cpu'  # ensure determinism for the device-dependent torch.Generator

          lowerCamelCase__           =		self.get_dummy_components()
          lowerCamelCase__           =		StableDiffusionLDMaDPipeline(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		ldmad_pipe.to(SCREAMING_SNAKE_CASE__				)
          ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		ldmad_pipe(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__    ,  lowerCamelCase__           =		output.rgb, output.depth

          lowerCamelCase__           =		rgb[0, -3:, -3:, -1]
          lowerCamelCase__           =		depth[0, -3:, -1]

          assert rgb.shape == (1, 64, 64, 3)
          assert depth.shape == (1, 64, 64)

          lowerCamelCase__           =		np.array(
              [0.37_33_81_76, 0.7_02_47, 0.74_20_31_93, 0.51_64_36_04, 0.58_25_67_93, 0.60_93_21_36, 0.4_18_10_95, 0.48_35_58_77, 0.46_53_52_62]				)
          lowerCamelCase__           =		np.array([1_03.4_67_27, 85.81_20_04, 87.84_92_36]				)

          assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb				).max() < 1e-2
          assert np.abs(image_slice_depth.flatten() - expected_slice_depth				).max() < 1e-2
    def      _UpperCamelCase       ( self							:					List[Any]				):
          lowerCamelCase__           =		self.get_dummy_components()
          lowerCamelCase__           =		StableDiffusionLDMaDPipeline(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		ldmad_pipe.to(SCREAMING_SNAKE_CASE__				)
          ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		3 * [inputs['prompt']]

          # forward
          lowerCamelCase__           =		ldmad_pipe(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__    ,  lowerCamelCase__           =		output.rgb, output.depth
          lowerCamelCase__           =		rgb_slice_a[0, -3:, -3:, -1]
          lowerCamelCase__           =		depth_slice_a[0, -3:, -1]

          lowerCamelCase__           =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		3 * [inputs.pop('prompt'				)]

          lowerCamelCase__           =		ldmad_pipe.tokenizer(
              SCREAMING_SNAKE_CASE__							,					padding='max_length'							,					max_length=ldmad_pipe.tokenizer.model_max_length							,					truncation=SCREAMING_SNAKE_CASE__							,					return_tensors='pt'							,					)
          lowerCamelCase__           =		text_inputs['input_ids'].to(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		ldmad_pipe.text_encoder(SCREAMING_SNAKE_CASE__				)[0]

          lowerCamelCase__           =		prompt_embeds

          # forward
          lowerCamelCase__           =		ldmad_pipe(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__    ,  lowerCamelCase__           =		output.rgb, output.depth
          lowerCamelCase__           =		rgb_slice_a[0, -3:, -3:, -1]
          lowerCamelCase__           =		depth_slice_a[0, -3:, -1]

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


    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__           =		'cpu'  # ensure determinism for the device-dependent torch.Generator
          lowerCamelCase__           =		self.get_dummy_components()
          lowerCamelCase__           =		PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		StableDiffusionLDMaDPipeline(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		ldmad_pipe.to(SCREAMING_SNAKE_CASE__				)
          ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		'french fries'
          lowerCamelCase__           =		ldmad_pipe(**SCREAMING_SNAKE_CASE__							,					negative_prompt=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__    ,  lowerCamelCase__           =		output.rgb, output.depth
          lowerCamelCase__           =		rgb[0, -3:, -3:, -1]
          lowerCamelCase__           =		depth[0, -3:, -1]

          assert rgb.shape == (1, 64, 64, 3)
          assert depth.shape == (1, 64, 64)

          lowerCamelCase__           =		np.array(
              [0.3_70_44, 0.71_81_15_03, 0.7_22_32_51, 0.48_60_36_75, 0.5_63_83_91, 0.6_36_49_48, 0.42_83_37_04, 0.4_90_13_15, 0.47_92_62_17]				)
          lowerCamelCase__           =		np.array([1_07.8_47_38, 84.6_28_02, 89.96_21_35]				)
          assert np.abs(rgb_slice.flatten() - expected_slice_rgb				).max() < 1e-2
          assert np.abs(depth_slice.flatten() - expected_slice_depth				).max() < 1e-2





@slow
@require_torch_gpu
class   _a      (      unittest.TestCase ):
    def      _UpperCamelCase       ( self							:					Any				):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]="cpu"							,					SCREAMING_SNAKE_CASE__							:					int=torch.floataa							,					SCREAMING_SNAKE_CASE__							:					Tuple=0				):
          lowerCamelCase__           =		torch.Generator(device=SCREAMING_SNAKE_CASE__				).manual_seed(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		np.random.RandomState(SCREAMING_SNAKE_CASE__				).standard_normal((1, 4, 64, 64)				)
          lowerCamelCase__           =		torch.from_numpy(SCREAMING_SNAKE_CASE__				).to(device=SCREAMING_SNAKE_CASE__							,					dtype=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		{
              'prompt': 'a photograph of an astronaut riding a horse',
              'latents': latents,
              'generator': generator,
              'num_inference_steps': 3,
              'guidance_scale': 7.5,
              'output_type': 'numpy',
          }
          return inputs


    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'				)
          lowerCamelCase__           =		ldmad_pipe.to(SCREAMING_SNAKE_CASE__				)
          ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.get_inputs(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		ldmad_pipe(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__    ,  lowerCamelCase__           =		output.rgb, output.depth
          lowerCamelCase__           =		rgb[0, -3:, -3:, -1].flatten()
          lowerCamelCase__           =		rgb[0, -3:, -1].flatten()

          assert rgb.shape == (1, 5_12, 5_12, 3)
          assert depth.shape == (1, 5_12, 5_12)

          lowerCamelCase__           =		np.array(
              [0.53_80_54_65, 0.56_70_73_05, 0.5_48_65_15, 0.57_01_22_36, 0.5_81_45_11, 0.56_25_34_87, 0.54_84_30_14, 0.55_09_22_63, 0.6_45_97_06]				)
          lowerCamelCase__           =		np.array(
              [0.9_26_37_81, 0.6_67_86_72, 0.5_48_65_15, 0.92_20_21_45, 0.67_83_11_35, 0.56_25_34_87, 0.9_24_16_94, 0.7_55_14_78, 0.6_45_97_06]				)
          assert np.abs(rgb_slice - expected_slice_rgb				).max() < 3e-3
          assert np.abs(depth_slice - expected_slice_depth				).max() < 3e-3





@nightly
@require_torch_gpu
class   _a      (      unittest.TestCase ):
    def      _UpperCamelCase       ( self							:					Any				):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]="cpu"							,					SCREAMING_SNAKE_CASE__							:					Dict=torch.floataa							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=0				):
          lowerCamelCase__           =		torch.Generator(device=SCREAMING_SNAKE_CASE__				).manual_seed(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		np.random.RandomState(SCREAMING_SNAKE_CASE__				).standard_normal((1, 4, 64, 64)				)
          lowerCamelCase__           =		torch.from_numpy(SCREAMING_SNAKE_CASE__				).to(device=SCREAMING_SNAKE_CASE__							,					dtype=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		{
              'prompt': 'a photograph of an astronaut riding a horse',
              'latents': latents,
              'generator': generator,
              'num_inference_steps': 50,
              'guidance_scale': 7.5,
              'output_type': 'numpy',
          }
          return inputs
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__           =		StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'				).to(SCREAMING_SNAKE_CASE__				)
          ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.get_inputs(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		ldmad_pipe(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__    ,  lowerCamelCase__           =		output.rgb, output.depth

          lowerCamelCase__           =		0.49_55_86
          lowerCamelCase__           =		0.33_79_55_15
          lowerCamelCase__           =		1_12.4_85_18
          lowerCamelCase__           =		98.48_97_46
          assert np.abs(expected_rgb_mean - rgb.mean()				) < 1e-3
          assert np.abs(expected_rgb_std - rgb.std()				) < 1e-3
          assert np.abs(expected_depth_mean - depth.mean()				) < 1e-3
          assert np.abs(expected_depth_std - depth.std()				) < 1e-3


    def      _UpperCamelCase       ( self							:					Any				):
          lowerCamelCase__           =		StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c'				).to(SCREAMING_SNAKE_CASE__				)
          ldmad_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.get_inputs(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		ldmad_pipe(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__    ,  lowerCamelCase__           =		output.rgb, output.depth

          lowerCamelCase__           =		0.4_19_41_27
          lowerCamelCase__           =		0.35_37_55_86
          lowerCamelCase__           =		0.5_63_85_02
          lowerCamelCase__           =		0.34_68_61_03

          assert rgb.shape == (1, 5_12, 5_12, 3)
          assert depth.shape == (1, 5_12, 5_12, 1)
          assert np.abs(expected_rgb_mean - rgb.mean()				) < 1e-3
          assert np.abs(expected_rgb_std - rgb.std()				) < 1e-3
          assert np.abs(expected_depth_mean - depth.mean()				) < 1e-3
          assert np.abs(expected_depth_std - depth.std()				) < 1e-3







"""simple docstring"""


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:
   _snake_case          =							None


_snake_case          =							logging.get_logger(__name__)


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

_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"
        ),
    },
}

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

# fmt: off
_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   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Any             =    VOCAB_FILES_NAMES
    a_    :				Optional[Any]             =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a_    :				Optional[Any]             =    PRETRAINED_VOCAB_FILES_MAP
    a_    :				List[str]             =    ['input_ids', 'attention_mask']
    a_    :				Union[str, Any]             =    NllbTokenizer

    a_    :				List[int]             =    []
    a_    :				List[int]             =    []
    def __init__( self							:					int							,					SCREAMING_SNAKE_CASE__							:					List[Any]=None							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=None							,					SCREAMING_SNAKE_CASE__							:					List[Any]="<s>"							,					SCREAMING_SNAKE_CASE__							:					List[str]="</s>"							,					SCREAMING_SNAKE_CASE__							:					Any="</s>"							,					SCREAMING_SNAKE_CASE__							:					List[str]="<s>"							,					SCREAMING_SNAKE_CASE__							:					Tuple="<unk>"							,					SCREAMING_SNAKE_CASE__							:					Optional[int]="<pad>"							,					SCREAMING_SNAKE_CASE__							:					Any="<mask>"							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=None							,					SCREAMING_SNAKE_CASE__							:					int=None							,					SCREAMING_SNAKE_CASE__							:					Dict=None							,					SCREAMING_SNAKE_CASE__							:					Tuple=False							,					**SCREAMING_SNAKE_CASE__							:					str							,					):
          # Mask token behave like a normal word, i.e. include the space before it
          lowerCamelCase__           =		AddedToken(SCREAMING_SNAKE_CASE__							,					lstrip=SCREAMING_SNAKE_CASE__							,					rstrip=SCREAMING_SNAKE_CASE__				) if isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) else mask_token
          lowerCamelCase__           =		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__							,					)

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

          lowerCamelCase__           =		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}				)
          lowerCamelCase__           =		{
              lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__				) for lang_code in FAIRSEQ_LANGUAGE_CODES
          }

          lowerCamelCase__           =		src_lang if src_lang is not None else 'eng_Latn'
          lowerCamelCase__           =		self.convert_tokens_to_ids(self._src_lang				)
          lowerCamelCase__           =		tgt_lang
          self.set_src_lang_special_tokens(self._src_lang				)
    @property
    def      _UpperCamelCase       ( self							:					str				):
          return self._src_lang
    @src_lang.setter
    def      _UpperCamelCase       ( self							:					int							,					SCREAMING_SNAKE_CASE__							:					str				):
          lowerCamelCase__           =		new_src_lang
          self.set_src_lang_special_tokens(self._src_lang				)
    def      _UpperCamelCase       ( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					List[int]							,					SCREAMING_SNAKE_CASE__							:					Optional[List[int]] = 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							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					List[int]							,					SCREAMING_SNAKE_CASE__							:					Optional[List[int]] = None				):
          lowerCamelCase__           =		[self.sep_token_id]
          lowerCamelCase__           =		[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							:					str							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[str]							,					SCREAMING_SNAKE_CASE__							:					Optional[str]							,					**SCREAMING_SNAKE_CASE__							:					Optional[int]				):
          if src_lang is None or tgt_lang is None:
                raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'				)
          lowerCamelCase__           =		src_lang
          lowerCamelCase__           =		self(SCREAMING_SNAKE_CASE__							,					add_special_tokens=SCREAMING_SNAKE_CASE__							,					return_tensors=SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		tgt_lang_id
          return inputs
    def      _UpperCamelCase       ( self							:					str							,					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__							:					Dict							,					):
          lowerCamelCase__           =		src_lang
          lowerCamelCase__           =		tgt_lang
          return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					List[str]				):
          return self.set_src_lang_special_tokens(self.src_lang				)
    def      _UpperCamelCase       ( self							:					List[Any]				):
          return self.set_tgt_lang_special_tokens(self.tgt_lang				)
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]				):
          lowerCamelCase__           =		self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__				)

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

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

          lowerCamelCase__           =		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							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					str				):
          lowerCamelCase__           =		self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__				)
          if self.legacy_behaviour:
                lowerCamelCase__           =		[]
                lowerCamelCase__           =		[self.eos_token_id, self.cur_lang_code]
          else:
                lowerCamelCase__           =		[self.cur_lang_code]
                lowerCamelCase__           =		[self.eos_token_id]

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

          lowerCamelCase__           =		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							:					Dict							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[str] = 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(SCREAMING_SNAKE_CASE__				):
                logger.error(F'Vocabulary path ({save_directory}) should be a directory.'				)
                return
          lowerCamelCase__           =		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,)



