






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 						snake_case  (  __SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE       ,			unittest.TestCase	):
    __magic_name__   					=					StableDiffusionInpaintPipeline
    __magic_name__   					=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    __magic_name__   					=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    __magic_name__   					=					frozenset(
        []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    __magic_name__   					=					frozenset([]	)





    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      torch.manual_seed(0      )
      a					:      List[Any]    =       UNetaDConditionModel(
          block_out_channels=(3_2, 6_4)    ,		layers_per_block=2    ,		sample_size=3_2    ,		in_channels=9    ,		out_channels=4    ,		down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,		up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,		cross_attention_dim=3_2    ,		attention_head_dim=(2, 4)    ,		use_linear_projection=_SCREAMING_SNAKE_CASE    ,		)
      a					:      Optional[Any]    =       PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE      )
      torch.manual_seed(0      )
      a					:      Dict    =       AutoencoderKL(
          block_out_channels=[3_2, 6_4]    ,		in_channels=3    ,		out_channels=3    ,		down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,		latent_channels=4    ,		sample_size=1_2_8    ,		)
      torch.manual_seed(0      )
      a					:      Tuple    =       CLIPTextConfig(
          bos_token_id=0    ,		eos_token_id=2    ,		hidden_size=3_2    ,		intermediate_size=3_7    ,		layer_norm_eps=1E-05    ,		num_attention_heads=4    ,		num_hidden_layers=5    ,		pad_token_id=1    ,		vocab_size=1_0_0_0    ,		hidden_act='gelu'    ,		projection_dim=5_1_2    ,		)
      a					:      List[Any]    =       CLIPTextModel(_SCREAMING_SNAKE_CASE      )
      a					:      Any    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

      a					:      Tuple    =       {
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "safety_checker": None,
          "feature_extractor": None,
      }
      return components





    def     lowerCamelCase__				(							self     : str    ,		A     : List[Any]    ,		A     : Union[str, Any]=0      ):


      '''simple docstring'''




      a					:      str    =       floats_tensor((1, 3, 3_2, 3_2)    ,		rng=random.Random(_SCREAMING_SNAKE_CASE      )      ).to(_SCREAMING_SNAKE_CASE      )
      a					:      Any    =       image.cpu().permute(0    ,		2    ,		3    ,		1      )[0]
      a					:      List[Any]    =       Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE      )      ).convert('RGB'      ).resize((6_4, 6_4)      )
      a					:      Union[str, Any]    =       Image.fromarray(np.uinta(image + 4      )      ).convert('RGB'      ).resize((6_4, 6_4)      )
      if str(_SCREAMING_SNAKE_CASE      ).startswith('mps'      ):
        a					:      Dict    =       torch.manual_seed(_SCREAMING_SNAKE_CASE      )
      else:
        a					:      Tuple    =       torch.Generator(device=_SCREAMING_SNAKE_CASE      ).manual_seed(_SCREAMING_SNAKE_CASE      )
      a					:      List[Any]    =       {
          "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     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




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

      a					:      List[str]    =       self.get_dummy_inputs(_SCREAMING_SNAKE_CASE      )
      a					:      int    =       sd_pipe(**_SCREAMING_SNAKE_CASE      ).images
      a					:      str    =       image[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)
      a					:      str    =       np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]      )

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





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      super().test_inference_batch_single_identical(expected_max_diff=3E-3      )






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





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


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      Optional[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					:      Dict    =       load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
          '/yellow_cat_sitting_on_a_park_bench.npy'      )

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

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

      a					:      Dict    =       torch.manual_seed(0      )
      a					:      Any    =       pipe(
          prompt=_SCREAMING_SNAKE_CASE    ,		image=_SCREAMING_SNAKE_CASE    ,		mask_image=_SCREAMING_SNAKE_CASE    ,		generator=_SCREAMING_SNAKE_CASE    ,		output_type='np'    ,		)
      a					:      Union[str, Any]    =       output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 3)
      assert np.abs(expected_image - image      ).max() < 9E-3





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      a					:      int    =       load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/sd2-inpaint/init_image.png'      )
      a					:      Dict    =       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					:      List[str]    =       "stabilityai/stable-diffusion-2-inpainting"
      a					:      List[Any]    =       StableDiffusionInpaintPipeline.from_pretrained(
          _SCREAMING_SNAKE_CASE    ,		torch_dtype=torch.floataa    ,		safety_checker=_SCREAMING_SNAKE_CASE    ,		)
      pipe.to(_SCREAMING_SNAKE_CASE      )
      pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE      )
      pipe.enable_attention_slicing()

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

      a					:      List[Any]    =       torch.manual_seed(0      )
      a					:      str    =       pipe(
          prompt=_SCREAMING_SNAKE_CASE    ,		image=_SCREAMING_SNAKE_CASE    ,		mask_image=_SCREAMING_SNAKE_CASE    ,		generator=_SCREAMING_SNAKE_CASE    ,		output_type='np'    ,		)
      a					:      Optional[int]    =       output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 3)
      assert np.abs(expected_image - image      ).max() < 5E-1





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      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[Any]    =       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					:      int    =       PNDMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE    ,		subfolder='scheduler'      )
      a					:      Optional[int]    =       StableDiffusionInpaintPipeline.from_pretrained(
          _SCREAMING_SNAKE_CASE    ,		safety_checker=_SCREAMING_SNAKE_CASE    ,		scheduler=_SCREAMING_SNAKE_CASE    ,		torch_dtype=torch.floataa    ,		)
      pipe.to(_SCREAMING_SNAKE_CASE      )
      pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE      )
      pipe.enable_attention_slicing(1      )
      pipe.enable_sequential_cpu_offload()

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

      a					:      Union[str, Any]    =       torch.manual_seed(0      )
      a					:      int    =       pipe(
          prompt=_SCREAMING_SNAKE_CASE    ,		image=_SCREAMING_SNAKE_CASE    ,		mask_image=_SCREAMING_SNAKE_CASE    ,		generator=_SCREAMING_SNAKE_CASE    ,		num_inference_steps=2    ,		output_type='np'    ,		)

      a					:      Any    =       torch.cuda.max_memory_allocated()
      # make sure that less than 2.65 GB is allocated
      assert mem_bytes < 2.65 * 1_0**9












"""simple docstring"""


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase    :   List[str]   			=						logging.get_logger(__name__)

_UpperCamelCase    :   Optional[Any]   			=						'▁'

_UpperCamelCase    :   List[Any]   			=						{'vocab_file': 'sentencepiece.bpe.model'}

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

_UpperCamelCase    :   List[str]   			=						{
    'facebook/nllb-200-distilled-600M': 1024,
}

# fmt: off
_UpperCamelCase    :   List[str]   			=						['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 						snake_case  (  UpperCAmelCase	):
    __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__   					=					[]
    __magic_name__   					=					[]





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


      '''simple docstring'''




      a					:      int    =       AddedToken(A    ,		lstrip=A    ,		rstrip=A      ) if isinstance(A    ,		A      ) else mask_token

      a					:      Tuple    =       {} if sp_model_kwargs is None else sp_model_kwargs
      a					:      Optional[Any]    =       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					:      int    =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(str(A      )      )
      a					:      Optional[int]    =       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					:      List[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					:      Any    =       1

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





    def __getstate__(							self     : List[str]      ):


      '''simple docstring'''




      a					:      Dict    =       self.__dict__.copy()
      a					:      int    =       None
      a					:      List[str]    =       self.sp_model.serialized_model_proto()
      return state





    def __setstate__(							self     : str    ,		A     : Any      ):


      '''simple docstring'''




      a					:      str    =       d

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

      a					:      Union[str, Any]    =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )





    @property
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''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     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      return self._src_lang





    @src_lang.setter
    def     lowerCamelCase__				(							self     : Dict    ,		A     : str      ):


      '''simple docstring'''




      a					:      Any    =       new_src_lang
      self.set_src_lang_special_tokens(self._src_lang      )





    def     lowerCamelCase__				(							self     : Optional[int]    ,		A     : List[int]    ,		A     : Optional[List[int]] = None    ,		A     : bool = False      ):


      '''simple docstring'''




      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					:      Tuple    =       [1] * len(self.prefix_tokens      )
      a					:      int    =       [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     lowerCamelCase__				(							self     : Any    ,		A     : List[int]    ,		A     : Optional[List[int]] = None      ):


      '''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     lowerCamelCase__				(							self     : Optional[int]    ,		A     : List[int]    ,		A     : Optional[List[int]] = None      ):


      '''simple docstring'''




      a					:      List[str]    =       [self.sep_token_id]
      a					:      List[str]    =       [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     lowerCamelCase__				(							self     : List[str]    ,		A     : Optional[int]    ,		A     : str    ,		A     : Optional[str]    ,		A     : Optional[str]    ,		**A     : str      ):


      '''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'      )
      a					:      Any    =       src_lang
      a					:      Any    =       self(A    ,		add_special_tokens=A    ,		return_tensors=A    ,		**A      )
      a					:      Tuple    =       self.convert_tokens_to_ids(A      )
      a					:      Optional[Any]    =       tgt_lang_id
      return inputs





    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       {self.convert_ids_to_tokens(A      ): i for i in range(self.vocab_size      )}
      vocab.update(self.added_tokens_encoder      )
      return vocab





    def     lowerCamelCase__				(							self     : Any    ,		A     : str      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : Union[str, Any]    ,		A     : Tuple      ):


      '''simple docstring'''




      if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
      a					:      int    =       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     lowerCamelCase__				(							self     : Tuple    ,		A     : List[str]      ):


      '''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     lowerCamelCase__				(							self     : List[str]    ,		A     : Dict      ):


      '''simple docstring'''




      a					:      List[str]    =       ''.join(A      ).replace(A    ,		' '      ).strip()
      return out_string





    def     lowerCamelCase__				(							self     : Any    ,		A     : str    ,		A     : Optional[str] = None      ):


      '''simple docstring'''




      if not os.path.isdir(A      ):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
        return
      a					:      Optional[int]    =       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					:      Tuple    =       self.sp_model.serialized_model_proto()
          fi.write(A      )

      return (out_vocab_file,)





    def     lowerCamelCase__				(							self     : Any    ,		A     : List[str]    ,		A     : str = "eng_Latn"    ,		A     : Optional[List[str]] = None    ,		A     : str = "fra_Latn"    ,		**A     : Optional[int]    ,		):


      '''simple docstring'''




      a					:      Union[str, Any]    =       src_lang
      a					:      Optional[int]    =       tgt_lang
      return super().prepare_seqaseq_batch(A    ,		A    ,		**A      )





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      return self.set_src_lang_special_tokens(self.src_lang      )





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


      '''simple docstring'''




      return self.set_tgt_lang_special_tokens(self.tgt_lang      )





    def     lowerCamelCase__				(							self     : Union[str, Any]    ,		A     : Dict      ):


      '''simple docstring'''




      a					:      Optional[int]    =       self.lang_code_to_id[src_lang]
      if self.legacy_behaviour:
        a					:      List[Any]    =       []
        a					:      Optional[int]    =       [self.eos_token_id, self.cur_lang_code]
      else:
        a					:      Union[str, Any]    =       [self.cur_lang_code]
        a					:      List[str]    =       [self.eos_token_id]





    def     lowerCamelCase__				(							self     : Optional[Any]    ,		A     : str      ):


      '''simple docstring'''




      a					:      Tuple    =       self.lang_code_to_id[lang]
      if self.legacy_behaviour:
        a					:      List[str]    =       []
        a					:      Union[str, Any]    =       [self.eos_token_id, self.cur_lang_code]
      else:
        a					:      Union[str, Any]    =       [self.cur_lang_code]
        a					:      List[str]    =       [self.eos_token_id]




