


import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu



class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: int		)       -> str:




              """simple docstring"""


              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()

       @property
       def lowercase (		self: Optional[int]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  1
              UpperCamelCase_          =  3
              UpperCamelCase_          =  (32, 32)

              UpperCamelCase_          =  floats_tensor((batch_size, num_channels) + sizes					,   rng=random.Random(0		)		).to(_SCREAMING_SNAKE_CASE		)
              return image

       @property
       def lowercase (		self: List[str]		)       -> Dict:




              """simple docstring"""


              torch.manual_seed(0		)
              UpperCamelCase_          =  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					,   )
              return model

       @property
       def lowercase (		self: List[Any]		)       -> int:




              """simple docstring"""


              torch.manual_seed(0		)
              UpperCamelCase_          =  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					,   )
              return model

       @property
       def lowercase (		self: Tuple		)       -> Dict:




              """simple docstring"""


              torch.manual_seed(0		)
              UpperCamelCase_          =  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					,   )
              return CLIPTextModel(_SCREAMING_SNAKE_CASE		)

       @property
       def lowercase (		self: int		)       -> List[Any]:




              """simple docstring"""


              def extract(*_SCREAMING_SNAKE_CASE: Dict					,   **_SCREAMING_SNAKE_CASE: Any		):



                     class 							_UpperCamelCase					:

                            def __init__(		self: List[Any]		)       -> List[Any]:




                                   """simple docstring"""


                                   UpperCamelCase_          =  torch.ones([0]		)



                            def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> Dict:




                                   """simple docstring"""


                                   self.pixel_values.to(_SCREAMING_SNAKE_CASE		)
                                   return self

                     return Out()

              return extract

       def lowercase (		self: Union[str, Any]		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  "cpu"  # ensure determinism for the device-dependent torch.Generator
              UpperCamelCase_          =  self.dummy_cond_unet
              UpperCamelCase_          =  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					,   )

              UpperCamelCase_          =  self.dummy_vae
              UpperCamelCase_          =  self.dummy_text_encoder
              UpperCamelCase_          =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

              # make sure here that pndm scheduler skips prk
              UpperCamelCase_          =  StableDiffusionPipeline(
                  unet=_SCREAMING_SNAKE_CASE					,   scheduler=_SCREAMING_SNAKE_CASE					,   vae=_SCREAMING_SNAKE_CASE					,   text_encoder=_SCREAMING_SNAKE_CASE					,   tokenizer=_SCREAMING_SNAKE_CASE					,   safety_checker=_SCREAMING_SNAKE_CASE					,   feature_extractor=self.dummy_extractor					,   )
              UpperCamelCase_          =  sd_pipe.to(_SCREAMING_SNAKE_CASE		)
              sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  "A painting of a squirrel eating a burger"

              UpperCamelCase_          =  torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(0		)
              UpperCamelCase_          =  sd_pipe([prompt]					,   generator=_SCREAMING_SNAKE_CASE					,   guidance_scale=6.0					,   num_inference_steps=2					,   output_type="np"		)
              UpperCamelCase_          =  output.images

              UpperCamelCase_          =  torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(0		)
              UpperCamelCase_          =  sd_pipe(
                  [prompt]					,   generator=_SCREAMING_SNAKE_CASE					,   guidance_scale=6.0					,   num_inference_steps=2					,   output_type="np"					,   return_dict=_SCREAMING_SNAKE_CASE					,   )[0]

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

              assert image.shape == (1, 64, 64, 3)
              UpperCamelCase_          =  np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64]		)

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

       def lowercase (		self: Any		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  "cpu"  # ensure determinism for the device-dependent torch.Generator
              UpperCamelCase_          =  self.dummy_cond_unet
              UpperCamelCase_          =  PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  self.dummy_vae
              UpperCamelCase_          =  self.dummy_text_encoder
              UpperCamelCase_          =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

              # make sure here that pndm scheduler skips prk
              UpperCamelCase_          =  StableDiffusionPipeline(
                  unet=_SCREAMING_SNAKE_CASE					,   scheduler=_SCREAMING_SNAKE_CASE					,   vae=_SCREAMING_SNAKE_CASE					,   text_encoder=_SCREAMING_SNAKE_CASE					,   tokenizer=_SCREAMING_SNAKE_CASE					,   safety_checker=_SCREAMING_SNAKE_CASE					,   feature_extractor=self.dummy_extractor					,   )
              UpperCamelCase_          =  sd_pipe.to(_SCREAMING_SNAKE_CASE		)
              sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  "A painting of a squirrel eating a burger"
              UpperCamelCase_          =  torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(0		)
              UpperCamelCase_          =  sd_pipe([prompt]					,   generator=_SCREAMING_SNAKE_CASE					,   guidance_scale=6.0					,   num_inference_steps=2					,   output_type="np"		)

              UpperCamelCase_          =  output.images

              UpperCamelCase_          =  torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(0		)
              UpperCamelCase_          =  sd_pipe(
                  [prompt]					,   generator=_SCREAMING_SNAKE_CASE					,   guidance_scale=6.0					,   num_inference_steps=2					,   output_type="np"					,   return_dict=_SCREAMING_SNAKE_CASE					,   )[0]

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

              assert image.shape == (1, 64, 64, 3)
              UpperCamelCase_          =  np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93]		)

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

       def lowercase (		self: Optional[int]		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  StableDiffusionPipeline.from_pretrained(
                  "hf-internal-testing/tiny-stable-diffusion-lms-pipe"					,   safety_checker=_SCREAMING_SNAKE_CASE		)
              assert isinstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              assert isinstance(pipe.scheduler					,   _SCREAMING_SNAKE_CASE		)
              assert pipe.safety_checker is None

              UpperCamelCase_          =  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(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE		)

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



       @unittest.skipIf(torch_device != "cuda"					,   "This test requires a GPU"		)
       def lowercase (		self: List[Any]		)       -> Dict:




              """simple docstring"""


              UpperCamelCase_          =  self.dummy_cond_unet
              UpperCamelCase_          =  PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  self.dummy_vae
              UpperCamelCase_          =  self.dummy_text_encoder
              UpperCamelCase_          =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

              # put models in fp16
              UpperCamelCase_          =  unet.half()
              UpperCamelCase_          =  vae.half()
              UpperCamelCase_          =  bert.half()

              # make sure here that pndm scheduler skips prk
              UpperCamelCase_          =  StableDiffusionPipeline(
                  unet=_SCREAMING_SNAKE_CASE					,   scheduler=_SCREAMING_SNAKE_CASE					,   vae=_SCREAMING_SNAKE_CASE					,   text_encoder=_SCREAMING_SNAKE_CASE					,   tokenizer=_SCREAMING_SNAKE_CASE					,   safety_checker=_SCREAMING_SNAKE_CASE					,   feature_extractor=self.dummy_extractor					,   )
              UpperCamelCase_          =  sd_pipe.to(_SCREAMING_SNAKE_CASE		)
              sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  "A painting of a squirrel eating a burger"
              UpperCamelCase_          =  sd_pipe([prompt]					,   num_inference_steps=2					,   output_type="np"		).images

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



@nightly
@require_torch_gpu
class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: List[Any]		)       -> Union[str, Any]:




              """simple docstring"""


              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()

       def lowercase (		self: Any		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"					,   safety_checker=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config		)
              UpperCamelCase_          =  sd_pipe.to(_SCREAMING_SNAKE_CASE		)
              sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  (
                  "portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
                  " coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
                  " anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
                  " children from bahnhof zoo, detailed "
              )
              UpperCamelCase_          =  4003660346
              UpperCamelCase_          =  7

              # without safety guidance (sld_guidance_scale = 0)
              UpperCamelCase_          =  torch.manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  sd_pipe(
                  [prompt]					,   generator=_SCREAMING_SNAKE_CASE					,   guidance_scale=_SCREAMING_SNAKE_CASE					,   num_inference_steps=50					,   output_type="np"					,   width=512					,   height=512					,   sld_guidance_scale=0					,   )

              UpperCamelCase_          =  output.images
              UpperCamelCase_          =  image[0, -3:, -3:, -1]
              UpperCamelCase_          =  [0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

              assert image.shape == (1, 512, 512, 3)

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

              # without safety guidance (strong configuration)
              UpperCamelCase_          =  torch.manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  sd_pipe(
                  [prompt]					,   generator=_SCREAMING_SNAKE_CASE					,   guidance_scale=_SCREAMING_SNAKE_CASE					,   num_inference_steps=50					,   output_type="np"					,   width=512					,   height=512					,   sld_guidance_scale=2000					,   sld_warmup_steps=7					,   sld_threshold=0.0_25					,   sld_momentum_scale=0.5					,   sld_mom_beta=0.7					,   )

              UpperCamelCase_          =  output.images
              UpperCamelCase_          =  image[0, -3:, -3:, -1]
              UpperCamelCase_          =  [0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

              assert image.shape == (1, 512, 512, 3)

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

       def lowercase (		self: Optional[int]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"					,   safety_checker=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config		)
              UpperCamelCase_          =  sd_pipe.to(_SCREAMING_SNAKE_CASE		)
              sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  "padme amidala taking a bath artwork, safe for work, no nudity"
              UpperCamelCase_          =  2734971755
              UpperCamelCase_          =  7

              UpperCamelCase_          =  torch.manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  sd_pipe(
                  [prompt]					,   generator=_SCREAMING_SNAKE_CASE					,   guidance_scale=_SCREAMING_SNAKE_CASE					,   num_inference_steps=50					,   output_type="np"					,   width=512					,   height=512					,   sld_guidance_scale=0					,   )

              UpperCamelCase_          =  output.images
              UpperCamelCase_          =  image[0, -3:, -3:, -1]
              UpperCamelCase_          =  [0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

              assert image.shape == (1, 512, 512, 3)

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

              UpperCamelCase_          =  torch.manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  sd_pipe(
                  [prompt]					,   generator=_SCREAMING_SNAKE_CASE					,   guidance_scale=_SCREAMING_SNAKE_CASE					,   num_inference_steps=50					,   output_type="np"					,   width=512					,   height=512					,   sld_guidance_scale=2000					,   sld_warmup_steps=7					,   sld_threshold=0.0_25					,   sld_momentum_scale=0.5					,   sld_mom_beta=0.7					,   )

              UpperCamelCase_          =  output.images
              UpperCamelCase_          =  image[0, -3:, -3:, -1]
              UpperCamelCase_          =  [0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

              assert image.shape == (1, 512, 512, 3)

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



       def lowercase (		self: List[Any]		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"		)
              UpperCamelCase_          =  sd_pipe.to(_SCREAMING_SNAKE_CASE		)
              sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  (
                  "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
                  " leyendecker"
              )
              UpperCamelCase_          =  1044355234
              UpperCamelCase_          =  12

              UpperCamelCase_          =  torch.manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  sd_pipe(
                  [prompt]					,   generator=_SCREAMING_SNAKE_CASE					,   guidance_scale=_SCREAMING_SNAKE_CASE					,   num_inference_steps=50					,   output_type="np"					,   width=512					,   height=512					,   sld_guidance_scale=0					,   )

              UpperCamelCase_          =  output.images
              UpperCamelCase_          =  image[0, -3:, -3:, -1]
              UpperCamelCase_          =  np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]		)

              assert image.shape == (1, 512, 512, 3)

              assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-7

              UpperCamelCase_          =  torch.manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  sd_pipe(
                  [prompt]					,   generator=_SCREAMING_SNAKE_CASE					,   guidance_scale=_SCREAMING_SNAKE_CASE					,   num_inference_steps=50					,   output_type="np"					,   width=512					,   height=512					,   sld_guidance_scale=2000					,   sld_warmup_steps=7					,   sld_threshold=0.0_25					,   sld_momentum_scale=0.5					,   sld_mom_beta=0.7					,   )

              UpperCamelCase_          =  output.images
              UpperCamelCase_          =  image[0, -3:, -3:, -1]
              UpperCamelCase_          =  np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61]		)
              assert image.shape == (1, 512, 512, 3)

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








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': 1_0_2_4,
}

# 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 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Dict         =   VOCAB_FILES_NAMES
       _UpperCamelCase				:					str         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCamelCase				:					Tuple         =   PRETRAINED_VOCAB_FILES_MAP
       _UpperCamelCase				:					Union[str, Any]         =   ['''input_ids''', '''attention_mask''']

       _UpperCamelCase				:					List[int]         =   []
       _UpperCamelCase				:					List[int]         =   []

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




              """simple docstring"""


              UpperCamelCase_          =  AddedToken(_SCREAMING_SNAKE_CASE					,   lstrip=_SCREAMING_SNAKE_CASE					,   rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		) else mask_token

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

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

              UpperCamelCase_          =  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
              self.sp_model.Load(str(_SCREAMING_SNAKE_CASE		)		)
              UpperCamelCase_          =  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
              UpperCamelCase_          =  {"<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
              UpperCamelCase_          =  1

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

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

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

       def __getstate__(		self: Any		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.__dict__.copy()
              UpperCamelCase_          =  None
              UpperCamelCase_          =  self.sp_model.serialized_model_proto()
              return state

       def __setstate__(		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  d

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

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

       @property
       def lowercase (		self: Union[str, Any]		)       -> Dict:




              """simple docstring"""


              return len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset + 1  # Plus 1 for the mask token

       @property
       def lowercase (		self: Union[str, Any]		)       -> str:




              """simple docstring"""


              return self._src_lang

       @src_lang.setter
       def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: str		)       -> None:




              """simple docstring"""


              UpperCamelCase_          =  new_src_lang
              self.set_src_lang_special_tokens(self._src_lang		)

       def lowercase (		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None					,   _SCREAMING_SNAKE_CASE: bool = False		)       -> List[int]:




              """simple docstring"""


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

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

       def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None		)       -> List[int]:




              """simple docstring"""


              if token_ids_a is None:
                     return self.prefix_tokens + token_ids_a + self.suffix_tokens
              # We don't expect to process pairs, but leave the pair logic for API consistency
              return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None		)       -> List[int]:




              """simple docstring"""


              UpperCamelCase_          =  [self.sep_token_id]
              UpperCamelCase_          =  [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 lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: Optional[int]					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: Optional[str]					,   _SCREAMING_SNAKE_CASE: Optional[str]					,   **_SCREAMING_SNAKE_CASE: Tuple		)       -> int:




              """simple docstring"""


              if src_lang is None or tgt_lang is None:
                     raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"		)
              UpperCamelCase_          =  src_lang
              UpperCamelCase_          =  self(_SCREAMING_SNAKE_CASE					,   add_special_tokens=_SCREAMING_SNAKE_CASE					,   return_tensors=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tgt_lang_id
              return inputs

       def lowercase (		self: Tuple		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
              vocab.update(self.added_tokens_encoder		)
              return vocab

       def lowercase (		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: str		)       -> List[str]:




              """simple docstring"""


              return self.sp_model.encode(_SCREAMING_SNAKE_CASE					,   out_type=_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Dict					,   _SCREAMING_SNAKE_CASE: str		)       -> Optional[int]:




              """simple docstring"""


              if token in self.fairseq_tokens_to_ids:
                     return self.fairseq_tokens_to_ids[token]
              UpperCamelCase_          =  self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE		)

              # 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 lowercase (		self: int					,   _SCREAMING_SNAKE_CASE: Union[str, Any]		)       -> Any:




              """simple docstring"""


              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 lowercase (		self: int					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  "".join(_SCREAMING_SNAKE_CASE		).replace(_SCREAMING_SNAKE_CASE					,   " "		).strip()
              return out_string

       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: Optional[str] = None		)       -> Tuple[str]:




              """simple docstring"""


              if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
                     logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
                     return
              UpperCamelCase_          =  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		) and os.path.isfile(self.vocab_file		):
                     copyfile(self.vocab_file					,   _SCREAMING_SNAKE_CASE		)
              elif not os.path.isfile(self.vocab_file		):
                     with open(_SCREAMING_SNAKE_CASE					,   "wb"		) as fi:
                            UpperCamelCase_          =  self.sp_model.serialized_model_proto()
                            fi.write(_SCREAMING_SNAKE_CASE		)

              return (out_vocab_file,)

       def lowercase (		self: Union[str, Any]					,   _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: List[str]					,   )       -> BatchEncoding:




              """simple docstring"""


              UpperCamelCase_          =  src_lang
              UpperCamelCase_          =  tgt_lang
              return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Any		)       -> Optional[int]:




              """simple docstring"""


              return self.set_src_lang_special_tokens(self.src_lang		)

       def lowercase (		self: Dict		)       -> Optional[int]:




              """simple docstring"""


              return self.set_tgt_lang_special_tokens(self.tgt_lang		)

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Any		)       -> None:




              """simple docstring"""


              UpperCamelCase_          =  self.lang_code_to_id[src_lang]
              if self.legacy_behaviour:
                     UpperCamelCase_          =  []
                     UpperCamelCase_          =  [self.eos_token_id, self.cur_lang_code]
              else:
                     UpperCamelCase_          =  [self.cur_lang_code]
                     UpperCamelCase_          =  [self.eos_token_id]



       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: str		)       -> None:




              """simple docstring"""


              UpperCamelCase_          =  self.lang_code_to_id[lang]
              if self.legacy_behaviour:
                     UpperCamelCase_          =  []
                     UpperCamelCase_          =  [self.eos_token_id, self.cur_lang_code]
              else:
                     UpperCamelCase_          =  [self.cur_lang_code]
                     UpperCamelCase_          =  [self.eos_token_id]

