





"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase						,     _UpperCamelCase						,     _UpperCamelCase						,     unittest.TestCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[str]		   =    StableDiffusionImageVariationPipeline
  SCREAMING_SNAKE_CASE__							:str		   =    IMAGE_VARIATION_PARAMS
  SCREAMING_SNAKE_CASE__							:Optional[Any]		   =    IMAGE_VARIATION_BATCH_PARAMS
  SCREAMING_SNAKE_CASE__							:Optional[int]		   =    frozenset([]      )
  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  SCREAMING_SNAKE_CASE__							:str		   =    frozenset([]      )

  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple		)						-> Optional[Any]:
        torch.manual_seed(0		)
        _UpperCamelCase					:		Any  =				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    ,      )
        _UpperCamelCase					:		Any  =				PNDMScheduler(skip_prk_steps=__a		)
        torch.manual_seed(0		)
        _UpperCamelCase					:		Dict  =				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    ,      )
        torch.manual_seed(0		)
        _UpperCamelCase					:		Union[str, Any]  =				CLIPVisionConfig(
            hidden_size=32    ,      projection_dim=32    ,      intermediate_size=37    ,      layer_norm_eps=1e-0_5    ,      num_attention_heads=4    ,      num_hidden_layers=5    ,      image_size=32    ,      patch_size=4    ,      )
        _UpperCamelCase					:		Optional[int]  =				CLIPVisionModelWithProjection(__a		)
        _UpperCamelCase					:		Dict  =				CLIPImageProcessor(crop_size=32    ,      size=32		)

        _UpperCamelCase					:		List[str]  =				{
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "image_encoder": image_encoder,
            "feature_extractor": feature_extractor,
            "safety_checker": None,
        }
        return components

  def       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				Tuple    ,      __a      :				str=0		)						-> List[str]:
        _UpperCamelCase					:		str  =				floats_tensor((1, 3, 32, 32)    ,      rng=random.Random(__a		)		)
        _UpperCamelCase					:		str  =				image.cpu().permute(0    ,      2    ,      3    ,      1		)[0]
        _UpperCamelCase					:		Union[str, Any]  =				Image.fromarray(np.uinta(__a		)		).convert("RGB"		).resize((32, 32)		)
        if str(__a		).startswith("mps"		):
              _UpperCamelCase					:		int  =				torch.manual_seed(__a		)
        else:
              _UpperCamelCase					:		int  =				torch.Generator(device=__a		).manual_seed(__a		)
        _UpperCamelCase					:		Dict  =				{
            "image": image,
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "numpy",
        }
        return inputs

  def       __SCREAMING_SNAKE_CASE (						self      :				str		)						-> int:
        _UpperCamelCase					:		Any  =				"cpu"  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase					:		Dict  =				self.get_dummy_components()
        _UpperCamelCase					:		int  =				StableDiffusionImageVariationPipeline(**__a		)
        _UpperCamelCase					:		Tuple  =				sd_pipe.to(__a		)
        sd_pipe.set_progress_bar_config(disable=__a		)

        _UpperCamelCase					:		Union[str, Any]  =				self.get_dummy_inputs(__a		)
        _UpperCamelCase					:		Any  =				sd_pipe(**__a		).images
        _UpperCamelCase					:		Optional[Any]  =				image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        _UpperCamelCase					:		Any  =				np.array([0.52_39, 0.57_23, 0.47_96, 0.50_49, 0.55_50, 0.46_85, 0.53_29, 0.48_91, 0.49_21]		)

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

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> str:
        _UpperCamelCase					:		Optional[Any]  =				"cpu"  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase					:		List[str]  =				self.get_dummy_components()
        _UpperCamelCase					:		List[Any]  =				StableDiffusionImageVariationPipeline(**__a		)
        _UpperCamelCase					:		Dict  =				sd_pipe.to(__a		)
        sd_pipe.set_progress_bar_config(disable=__a		)

        _UpperCamelCase					:		Union[str, Any]  =				self.get_dummy_inputs(__a		)
        _UpperCamelCase					:		Tuple  =				2 * [inputs["image"]]
        _UpperCamelCase					:		int  =				sd_pipe(**__a		)

        _UpperCamelCase					:		Optional[Any]  =				output.images

        _UpperCamelCase					:		Optional[Any]  =				image[-1, -3:, -3:, -1]

        assert image.shape == (2, 64, 64, 3)
        _UpperCamelCase					:		Dict  =				np.array([0.68_92, 0.56_37, 0.58_36, 0.57_71, 0.62_54, 0.64_09, 0.55_80, 0.55_69, 0.52_89]		)

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


  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]		)						-> List[str]:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3		)





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







  '''simple docstring'''


  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]		)						-> Optional[Any]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]    ,      __a      :				Optional[Any]    ,      __a      :				Tuple="cpu"    ,      __a      :				List[str]=torch.floataa    ,      __a      :				Optional[int]=0		)						-> Optional[Any]:
        _UpperCamelCase					:		Any  =				torch.Generator(device=__a		).manual_seed(__a		)
        _UpperCamelCase					:		Union[str, Any]  =				load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_imgvar/input_image_vermeer.png"		)
        _UpperCamelCase					:		List[Any]  =				np.random.RandomState(__a		).standard_normal((1, 4, 64, 64)		)
        _UpperCamelCase					:		Optional[int]  =				torch.from_numpy(__a		).to(device=__a    ,      dtype=__a		)
        _UpperCamelCase					:		str  =				{
            "image": init_image,
            "latents": latents,
            "generator": generator,
            "num_inference_steps": 3,
            "guidance_scale": 7.5,
            "output_type": "numpy",
        }
        return inputs

  def       __SCREAMING_SNAKE_CASE (						self      :				Any		)						-> Optional[Any]:
        _UpperCamelCase					:		str  =				StableDiffusionImageVariationPipeline.from_pretrained(
            "lambdalabs/sd-image-variations-diffusers"    ,      safety_checker=__a		)
        _UpperCamelCase					:		Any  =				sd_pipe.to(__a		)
        sd_pipe.set_progress_bar_config(disable=__a		)

        _UpperCamelCase					:		Any  =				self.get_inputs(__a		)
        _UpperCamelCase					:		Any  =				sd_pipe(**__a		).images
        _UpperCamelCase					:		Optional[int]  =				image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
        _UpperCamelCase					:		List[str]  =				np.array([0.8_44_91, 0.9_07_89, 0.7_57_08, 0.7_87_34, 0.8_34_85, 0.7_00_99, 0.6_69_38, 0.6_87_27, 0.6_13_79]		)
        assert np.abs(image_slice - expected_slice		).max() < 6e-3

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> str:
        _UpperCamelCase					:		Optional[Any]  =				0

        def callback_fn(__a      :				int    ,      __a      :				int    ,      __a      :				torch.FloatTensor		) -> None:
              _UpperCamelCase					:		int  =				True
              nonlocal number_of_steps
              number_of_steps += 1
              if step == 1:
                    _UpperCamelCase					:		Union[str, Any]  =				latents.detach().cpu().numpy()
                    assert latents.shape == (1, 4, 64, 64)
                    _UpperCamelCase					:		Union[str, Any]  =				latents[0, -3:, -3:, -1]
                    _UpperCamelCase					:		Dict  =				np.array(
                        [-0.16_21, 0.28_37, -0.79_79, -0.12_21, -1.30_57, 0.76_81, -2.11_91, 0.04_64, 1.63_09]		)

                    assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5e-2
              elif step == 2:
                    _UpperCamelCase					:		str  =				latents.detach().cpu().numpy()
                    assert latents.shape == (1, 4, 64, 64)
                    _UpperCamelCase					:		List[str]  =				latents[0, -3:, -3:, -1]
                    _UpperCamelCase					:		Optional[Any]  =				np.array([0.62_99, 1.75_00, 1.19_92, -2.15_82, -1.89_94, 0.73_34, -0.70_90, 1.01_37, 1.52_73]		)

                    assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5e-2

        _UpperCamelCase					:		int  =				False

        _UpperCamelCase					:		Any  =				StableDiffusionImageVariationPipeline.from_pretrained(
            "fusing/sd-image-variations-diffusers"    ,      safety_checker=__a    ,      torch_dtype=torch.floataa    ,      )
        pipe.to(__a		)
        pipe.set_progress_bar_config(disable=__a		)
        pipe.enable_attention_slicing()

        _UpperCamelCase					:		Any  =				self.get_inputs(__a    ,      dtype=torch.floataa		)
        pipe(**__a    ,      callback=__a    ,      callback_steps=1		)
        assert callback_fn.has_been_called
        assert number_of_steps == inputs["num_inference_steps"]


  def       __SCREAMING_SNAKE_CASE (						self      :				Any		)						-> Dict:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        _UpperCamelCase					:		List[Any]  =				"fusing/sd-image-variations-diffusers"
        _UpperCamelCase					:		List[str]  =				StableDiffusionImageVariationPipeline.from_pretrained(
            __a    ,      safety_checker=__a    ,      torch_dtype=torch.floataa		)
        _UpperCamelCase					:		Any  =				pipe.to(__a		)
        pipe.set_progress_bar_config(disable=__a		)
        pipe.enable_attention_slicing(1		)
        pipe.enable_sequential_cpu_offload()

        _UpperCamelCase					:		str  =				self.get_inputs(__a    ,      dtype=torch.floataa		)
        _UpperCamelCase					:		List[Any]  =				pipe(**__a		)

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





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







  '''simple docstring'''


  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple		)						-> Any:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  def       __SCREAMING_SNAKE_CASE (						self      :				str    ,      __a      :				List[Any]    ,      __a      :				List[Any]="cpu"    ,      __a      :				str=torch.floataa    ,      __a      :				Optional[Any]=0		)						-> Any:
        _UpperCamelCase					:		Tuple  =				torch.Generator(device=__a		).manual_seed(__a		)
        _UpperCamelCase					:		List[str]  =				load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_imgvar/input_image_vermeer.png"		)
        _UpperCamelCase					:		Union[str, Any]  =				np.random.RandomState(__a		).standard_normal((1, 4, 64, 64)		)
        _UpperCamelCase					:		List[str]  =				torch.from_numpy(__a		).to(device=__a    ,      dtype=__a		)
        _UpperCamelCase					:		int  =				{
            "image": init_image,
            "latents": latents,
            "generator": generator,
            "num_inference_steps": 50,
            "guidance_scale": 7.5,
            "output_type": "numpy",
        }
        return inputs

  def       __SCREAMING_SNAKE_CASE (						self      :				Any		)						-> Any:
        _UpperCamelCase					:		Any  =				StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers"		)
        sd_pipe.to(__a		)
        sd_pipe.set_progress_bar_config(disable=__a		)

        _UpperCamelCase					:		str  =				self.get_inputs(__a		)
        _UpperCamelCase					:		Optional[int]  =				sd_pipe(**__a		).images[0]

        _UpperCamelCase					:		Optional[Any]  =				load_numpy(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"		)
        _UpperCamelCase					:		int  =				np.abs(expected_image - image		).max()
        assert max_diff < 1e-3


  def       __SCREAMING_SNAKE_CASE (						self      :				str		)						-> Tuple:
        _UpperCamelCase					:		List[str]  =				StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers"		)
        _UpperCamelCase					:		int  =				DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config		)
        sd_pipe.to(__a		)
        sd_pipe.set_progress_bar_config(disable=__a		)

        _UpperCamelCase					:		Optional[int]  =				self.get_inputs(__a		)
        _UpperCamelCase					:		Optional[int]  =				25
        _UpperCamelCase					:		Any  =				sd_pipe(**__a		).images[0]

        _UpperCamelCase					:		Tuple  =				load_numpy(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"		)
        _UpperCamelCase					:		Dict  =				np.abs(expected_image - image		).max()
        assert max_diff < 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_mbartaa import MBartaaTokenizer
else:
  lowerCamelCase__      =      None


lowerCamelCase__      =      logging.get_logger(__name__)

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

lowerCamelCase__      =      {
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"
        ),
    },
}

lowerCamelCase__      =      {
    "facebook/mbart-large-50-one-to-many-mmt": 1024,
}

# fmt: off
lowerCamelCase__      =      ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]



class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple		   =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__							:Union[str, Any]		   =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__							:int		   =    ["input_ids", "attention_mask"]
  SCREAMING_SNAKE_CASE__							:Dict		   =    MBartaaTokenizer

  SCREAMING_SNAKE_CASE__							:List[int]		   =    []
  SCREAMING_SNAKE_CASE__							:List[int]		   =    []

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

        _UpperCamelCase					:		str  =				kwargs.get("additional_special_tokens"    ,      []		)
        kwargs["additional_special_tokens"] += [
            code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            __a    ,      src_lang=__a    ,      tgt_lang=__a    ,      tokenizer_file=__a    ,      eos_token=__a    ,      sep_token=__a    ,      cls_token=__a    ,      unk_token=__a    ,      pad_token=__a    ,      mask_token=__a    ,      **__a    ,      )

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

        _UpperCamelCase					:		int  =				{
            lang_code: self.convert_tokens_to_ids(__a		) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        _UpperCamelCase					:		List[str]  =				src_lang if src_lang is not None else "en_XX"
        _UpperCamelCase					:		Dict  =				tgt_lang
        _UpperCamelCase					:		int  =				self.lang_code_to_id[self._src_lang]
        self.set_src_lang_special_tokens(self._src_lang		)

  @property
  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> str:
        return self._src_lang

  @src_lang.setter
  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]    ,      __a      :				str		)						-> None:
        _UpperCamelCase					:		int  =				new_src_lang
        self.set_src_lang_special_tokens(self._src_lang		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]    ,      __a      :				List[int]    ,      __a      :				Optional[List[int]] = None		)						-> List[int]:
        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       __SCREAMING_SNAKE_CASE (						self      :				Any    ,      __a      :				List[str]    ,      __a      :				str = "en_XX"    ,      __a      :				Optional[List[str]] = None    ,      __a      :				str = "ro_RO"    ,      **__a      :				Tuple    ,      )						-> BatchEncoding:
        _UpperCamelCase					:		List[Any]  =				src_lang
        _UpperCamelCase					:		Union[str, Any]  =				tgt_lang
        return super().prepare_seqaseq_batch(__a    ,      __a    ,      **__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> int:
        return self.set_src_lang_special_tokens(self.src_lang		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple		)						-> Tuple:
        return self.set_tgt_lang_special_tokens(self.tgt_lang		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]    ,      __a      :				str		)						-> None:
        _UpperCamelCase					:		List[Any]  =				self.convert_tokens_to_ids(__a		)
        _UpperCamelCase					:		Union[str, Any]  =				[self.cur_lang_code_id]
        _UpperCamelCase					:		List[Any]  =				[self.eos_token_id]

        _UpperCamelCase					:		Union[str, Any]  =				self.convert_ids_to_tokens(self.prefix_tokens		)
        _UpperCamelCase					:		Union[str, Any]  =				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       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				str		)						-> None:
        _UpperCamelCase					:		Tuple  =				self.convert_tokens_to_ids(__a		)
        _UpperCamelCase					:		Union[str, Any]  =				[self.cur_lang_code_id]
        _UpperCamelCase					:		Optional[Any]  =				[self.eos_token_id]

        _UpperCamelCase					:		int  =				self.convert_ids_to_tokens(self.prefix_tokens		)
        _UpperCamelCase					:		Any  =				self.convert_ids_to_tokens(self.suffix_tokens		)

        _UpperCamelCase					:		Tuple  =				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       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				Optional[int]    ,      __a      :				str    ,      __a      :				Optional[str]    ,      __a      :				Optional[str]    ,      **__a      :				int		)						-> List[str]:
        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					:		List[str]  =				src_lang
        _UpperCamelCase					:		List[str]  =				self(__a    ,      add_special_tokens=__a    ,      return_tensors=__a    ,      **__a		)
        _UpperCamelCase					:		str  =				self.convert_tokens_to_ids(__a		)
        _UpperCamelCase					:		str  =				tgt_lang_id
        return inputs


  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]    ,      __a      :				str    ,      __a      :				Optional[str] = None		)						-> Tuple[str]:
        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(__a		):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
              return
        _UpperCamelCase					:		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		):
              copyfile(self.vocab_file    ,      __a		)

        return (out_vocab_file,)
