





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,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class   __SCREAMING_SNAKE_CASE     (					__snake_case	,__snake_case	,__snake_case	,unittest.TestCase   ):
       _UpperCAmelCase :    int					       =       StableDiffusionInstructPixaPixPipeline
       _UpperCAmelCase :    Optional[int]					       =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
       _UpperCAmelCase :    Optional[int]					       =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       _UpperCAmelCase :    Any					       =       IMAGE_TO_IMAGE_IMAGE_PARAMS
       _UpperCAmelCase :    Optional[int]					       =       IMAGE_TO_IMAGE_IMAGE_PARAMS







       def 		__lowerCamelCase       ( self	:      List[Any]     )  ->Optional[int]:
          torch.manual_seed(0     )
          lowerCamelCase__  :		Any     =				UNetaDConditionModel(
              block_out_channels=(3_2, 6_4)						,   layers_per_block=2						,   sample_size=3_2						,   in_channels=8						,   out_channels=4						,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,   cross_attention_dim=3_2						,   )
          lowerCamelCase__  :		int     =				PNDMScheduler(skip_prk_steps=A     )
          torch.manual_seed(0     )
          lowerCamelCase__  :		Optional[Any]     =				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						,   )
          torch.manual_seed(0     )
          lowerCamelCase__  :		List[str]     =				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						,   )
          lowerCamelCase__  :		List[Any]     =				CLIPTextModel(A     )
          lowerCamelCase__  :		Any     =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )

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







       def 		__lowerCamelCase       ( self	:      Any						,   A	:      List[Any]						,   A	:      Optional[Any]=0     )  ->Any:
          lowerCamelCase__  :		str     =				floats_tensor((1, 3, 3_2, 3_2)						,   rng=random.Random(A     )     ).to(A     )
          lowerCamelCase__  :		List[str]     =				image.cpu().permute(0						,   2						,   3						,   1     )[0]
          lowerCamelCase__  :		Optional[Any]     =				Image.fromarray(np.uinta(A     )     ).convert('''RGB'''     )
          if str(A     ).startswith('''mps'''     ):
             lowerCamelCase__  :		Any     =				torch.manual_seed(A     )
          else:
             lowerCamelCase__  :		List[Any]     =				torch.Generator(device=A     ).manual_seed(A     )
          lowerCamelCase__  :		Tuple     =				{
              """prompt""": """A painting of a squirrel eating a burger""",
              """image""": image,
              """generator""": generator,
              """num_inference_steps""": 2,
              """guidance_scale""": 6.0,
              """image_guidance_scale""": 1,
              """output_type""": """numpy""",
          }
          return inputs







       def 		__lowerCamelCase       ( self	:      str     )  ->Dict:
          lowerCamelCase__  :		Tuple     =				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
          lowerCamelCase__  :		str     =				self.get_dummy_components()
          lowerCamelCase__  :		Union[str, Any]     =				StableDiffusionInstructPixaPixPipeline(**A     )
          lowerCamelCase__  :		Optional[Any]     =				sd_pipe.to(A     )
          sd_pipe.set_progress_bar_config(disable=A     )

          lowerCamelCase__  :		Dict     =				self.get_dummy_inputs(A     )
          lowerCamelCase__  :		Optional[Any]     =				sd_pipe(**A     ).images
          lowerCamelCase__  :		Dict     =				image[0, -3:, -3:, -1]
          assert image.shape == (1, 3_2, 3_2, 3)
          lowerCamelCase__  :		List[str]     =				np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]     )

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







       def 		__lowerCamelCase       ( self	:      Union[str, Any]     )  ->Optional[Any]:
          lowerCamelCase__  :		Any     =				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
          lowerCamelCase__  :		str     =				self.get_dummy_components()
          lowerCamelCase__  :		Tuple     =				StableDiffusionInstructPixaPixPipeline(**A     )
          lowerCamelCase__  :		Dict     =				sd_pipe.to(A     )
          sd_pipe.set_progress_bar_config(disable=A     )

          lowerCamelCase__  :		Union[str, Any]     =				self.get_dummy_inputs(A     )
          lowerCamelCase__  :		Optional[Any]     =				"""french fries"""
          lowerCamelCase__  :		Dict     =				sd_pipe(**A						,   negative_prompt=A     )
          lowerCamelCase__  :		Optional[Any]     =				output.images
          lowerCamelCase__  :		List[Any]     =				image[0, -3:, -3:, -1]

          assert image.shape == (1, 3_2, 3_2, 3)
          lowerCamelCase__  :		List[str]     =				np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]     )

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







       def 		__lowerCamelCase       ( self	:      Union[str, Any]     )  ->Union[str, Any]:
          lowerCamelCase__  :		Any     =				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
          lowerCamelCase__  :		Dict     =				self.get_dummy_components()
          lowerCamelCase__  :		int     =				StableDiffusionInstructPixaPixPipeline(**A     )
          lowerCamelCase__  :		str     =				sd_pipe.to(A     )
          sd_pipe.set_progress_bar_config(disable=A     )

          lowerCamelCase__  :		List[Any]     =				self.get_dummy_inputs(A     )
          lowerCamelCase__  :		List[Any]     =				[inputs["""prompt"""]] * 2

          lowerCamelCase__  :		Optional[int]     =				np.array(inputs['''image''']     ).astype(np.floataa     ) / 2_55.0
          lowerCamelCase__  :		Dict     =				torch.from_numpy(A     ).unsqueeze(0     ).to(A     )
          lowerCamelCase__  :		Any     =				image / 2 + 0.5
          lowerCamelCase__  :		Union[str, Any]     =				image.permute(0						,   3						,   1						,   2     )
          lowerCamelCase__  :		Optional[int]     =				image.repeat(2						,   1						,   1						,   1     )

          lowerCamelCase__  :		Tuple     =				sd_pipe(**A     ).images
          lowerCamelCase__  :		List[Any]     =				image[-1, -3:, -3:, -1]

          assert image.shape == (2, 3_2, 3_2, 3)
          lowerCamelCase__  :		Any     =				np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]     )

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







       def 		__lowerCamelCase       ( self	:      str     )  ->List[Any]:
          lowerCamelCase__  :		str     =				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
          lowerCamelCase__  :		Union[str, Any]     =				self.get_dummy_components()
          lowerCamelCase__  :		Dict     =				EulerAncestralDiscreteScheduler(
              beta_start=0.0_00_85						,   beta_end=0.0_12						,   beta_schedule='''scaled_linear'''     )
          lowerCamelCase__  :		List[Any]     =				StableDiffusionInstructPixaPixPipeline(**A     )
          lowerCamelCase__  :		List[str]     =				sd_pipe.to(A     )
          sd_pipe.set_progress_bar_config(disable=A     )

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

          lowerCamelCase__  :		Union[str, Any]     =				[round(A						,   4     ) for x in image_slice.flatten().tolist()]
          print(''','''.join([str(A     ) for x in slice]     )     )

          assert image.shape == (1, 3_2, 3_2, 3)
          lowerCamelCase__  :		int     =				np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]     )

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







       def 		__lowerCamelCase       ( self	:      Optional[int]     )  ->int:
          super().test_inference_batch_single_identical(expected_max_diff=3e-3     )







       def 		__lowerCamelCase       ( self	:      Optional[Any]     )  ->List[Any]:
          lowerCamelCase__  :		int     =				self.get_dummy_components()
          lowerCamelCase__  :		Union[str, Any]     =				StableDiffusionInstructPixaPixPipeline(**A     )
          lowerCamelCase__  :		Optional[Any]     =				VaeImageProcessor(do_resize=A						,   do_normalize=A     )
          lowerCamelCase__  :		Optional[Any]     =				pipe.to(A     )
          pipe.set_progress_bar_config(disable=A     )

          lowerCamelCase__  :		Dict     =				pipe(**self.get_dummy_inputs_by_type(A						,   input_image_type='''pt'''     )     )[0]

          lowerCamelCase__  :		Tuple     =				components["""vae"""]
          lowerCamelCase__  :		Any     =				self.get_dummy_inputs_by_type(A						,   input_image_type='''pt'''     )

          for image_param in self.image_latents_params:
             if image_param in inputs.keys():
                lowerCamelCase__  :		Union[str, Any]     =				vae.encode(inputs[image_param]     ).latent_dist.mode()

          lowerCamelCase__  :		Dict     =				pipe(**A     )[0]

          lowerCamelCase__  :		Optional[int]     =				np.abs(out - out_latents_inputs     ).max()
          self.assertLess(A						,   1e-4						,   '''passing latents as image input generate different result from passing image'''     )


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







       def 		__lowerCamelCase       ( self	:      Dict     )  ->List[str]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







       def 		__lowerCamelCase       ( self	:      Tuple						,   A	:      List[str]=0     )  ->Union[str, Any]:
          lowerCamelCase__  :		Dict     =				torch.manual_seed(A     )
          lowerCamelCase__  :		Tuple     =				load_image(
              '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''     )
          lowerCamelCase__  :		int     =				{
              """prompt""": """turn him into a cyborg""",
              """image""": image,
              """generator""": generator,
              """num_inference_steps""": 3,
              """guidance_scale""": 7.5,
              """image_guidance_scale""": 1.0,
              """output_type""": """numpy""",
          }
          return inputs







       def 		__lowerCamelCase       ( self	:      Optional[Any]     )  ->int:
          lowerCamelCase__  :		Optional[int]     =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
              '''timbrooks/instruct-pix2pix'''						,   safety_checker=A     )
          pipe.to(A     )
          pipe.set_progress_bar_config(disable=A     )
          pipe.enable_attention_slicing()

          lowerCamelCase__  :		Dict     =				self.get_inputs()
          lowerCamelCase__  :		str     =				pipe(**A     ).images
          lowerCamelCase__  :		Dict     =				image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          lowerCamelCase__  :		List[str]     =				np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]     )

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







       def 		__lowerCamelCase       ( self	:      Optional[int]     )  ->List[Any]:
          lowerCamelCase__  :		Union[str, Any]     =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
              '''timbrooks/instruct-pix2pix'''						,   safety_checker=A     )
          lowerCamelCase__  :		str     =				LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
          pipe.to(A     )
          pipe.set_progress_bar_config(disable=A     )
          pipe.enable_attention_slicing()

          lowerCamelCase__  :		List[Any]     =				self.get_inputs()
          lowerCamelCase__  :		List[Any]     =				pipe(**A     ).images
          lowerCamelCase__  :		Tuple     =				image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          lowerCamelCase__  :		Tuple     =				np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]     )

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







       def 		__lowerCamelCase       ( self	:      Union[str, Any]     )  ->Any:
          lowerCamelCase__  :		List[str]     =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
              '''timbrooks/instruct-pix2pix'''						,   safety_checker=A     )
          lowerCamelCase__  :		Tuple     =				DDIMScheduler.from_config(pipe.scheduler.config     )
          pipe.to(A     )
          pipe.set_progress_bar_config(disable=A     )
          pipe.enable_attention_slicing()

          lowerCamelCase__  :		int     =				self.get_inputs()
          lowerCamelCase__  :		List[str]     =				pipe(**A     ).images
          lowerCamelCase__  :		Dict     =				image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          lowerCamelCase__  :		Optional[Any]     =				np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]     )

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







       def 		__lowerCamelCase       ( self	:      Dict     )  ->int:
          lowerCamelCase__  :		Tuple     =				0

          def callback_fn(A	:      int						,   A	:      int						,   A	:      torch.FloatTensor     ) -> None:
             lowerCamelCase__  :		Optional[Any]     =				True
             nonlocal number_of_steps
             number_of_steps += 1
             if step == 1:
                lowerCamelCase__  :		str     =				latents.detach().cpu().numpy()
                assert latents.shape == (1, 4, 6_4, 6_4)
                lowerCamelCase__  :		List[Any]     =				latents[0, -3:, -3:, -1]
                lowerCamelCase__  :		Optional[Any]     =				np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]     )

                assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5e-2
             elif step == 2:
                lowerCamelCase__  :		Any     =				latents.detach().cpu().numpy()
                assert latents.shape == (1, 4, 6_4, 6_4)
                lowerCamelCase__  :		int     =				latents[0, -3:, -3:, -1]
                lowerCamelCase__  :		str     =				np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]     )

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

          lowerCamelCase__  :		Any     =				False

          lowerCamelCase__  :		List[Any]     =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
              '''timbrooks/instruct-pix2pix'''						,   safety_checker=A						,   torch_dtype=torch.floataa     )
          lowerCamelCase__  :		List[str]     =				pipe.to(A     )
          pipe.set_progress_bar_config(disable=A     )
          pipe.enable_attention_slicing()

          lowerCamelCase__  :		int     =				self.get_inputs()
          pipe(**A						,   callback=A						,   callback_steps=1     )
          assert callback_fn.has_been_called
          assert number_of_steps == 3







       def 		__lowerCamelCase       ( self	:      List[Any]     )  ->Union[str, Any]:
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          lowerCamelCase__  :		Union[str, Any]     =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
              '''timbrooks/instruct-pix2pix'''						,   safety_checker=A						,   torch_dtype=torch.floataa     )
          lowerCamelCase__  :		str     =				pipe.to(A     )
          pipe.set_progress_bar_config(disable=A     )
          pipe.enable_attention_slicing(1     )
          pipe.enable_sequential_cpu_offload()

          lowerCamelCase__  :		Optional[Any]     =				self.get_inputs()
          lowerCamelCase__  :		Optional[Any]     =				pipe(**A     )

          lowerCamelCase__  :		int     =				torch.cuda.max_memory_allocated()
          # make sure that less than 2.2 GB is allocated
          assert mem_bytes < 2.2 * 1_0**9







       def 		__lowerCamelCase       ( self	:      Optional[int]     )  ->Optional[int]:
          lowerCamelCase__  :		List[str]     =				self.get_inputs()
          # resize to resolution that is divisible by 8 but not 16 or 32
          lowerCamelCase__  :		Optional[Any]     =				inputs["""image"""].resize((5_0_4, 5_0_4)     )

          lowerCamelCase__  :		List[str]     =				"""timbrooks/instruct-pix2pix"""
          lowerCamelCase__  :		str     =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
              A						,   safety_checker=A						,   )
          pipe.to(A     )
          pipe.set_progress_bar_config(disable=A     )
          pipe.enable_attention_slicing()

          lowerCamelCase__  :		Optional[int]     =				pipe(**A     )
          lowerCamelCase__  :		Tuple     =				output.images[0]

          lowerCamelCase__  :		int     =				image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

          assert image.shape == (5_0_4, 5_0_4, 3)
          lowerCamelCase__  :		str     =				np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]     )

          assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5e-3











"""simple docstring"""







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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_fnet import FNetTokenizer
else:
				_lowerCAmelCase            =	None

_lowerCAmelCase            =	logging.get_logger(__name__)
_lowerCAmelCase            =	{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCAmelCase            =	{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

_lowerCAmelCase            =	{
    'google/fnet-base': 5_12,
    'google/fnet-large': 5_12,
}

_lowerCAmelCase            =	'▁'


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Dict												=		VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		["""input_ids""", """token_type_ids"""]
						_SCREAMING_SNAKE_CASE  : Union[str, Any]												=		FNetTokenizer






						def __init__(		self  :Optional[Any]			, __magic_name__  :Union[str, Any]=None			, __magic_name__  :Any=None			, __magic_name__  :List[str]=False			, __magic_name__  :Any=True			, __magic_name__  :Any=True			, __magic_name__  :Union[str, Any]="<unk>"			, __magic_name__  :List[Any]="[SEP]"			, __magic_name__  :Union[str, Any]="<pad>"			, __magic_name__  :Tuple="[CLS]"			, __magic_name__  :Optional[int]="[MASK]"			, **__magic_name__  :Optional[int]			, )      ->Tuple:
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										lowercase    :      Union[str, Any]       = (
										    AddedToken(__magic_name__			, lstrip=__magic_name__			, rstrip=__magic_name__			, normalized=__magic_name__				)
										    if isinstance(__magic_name__			, __magic_name__				)
										    else mask_token
										)

										super().__init__(
										    __magic_name__			, tokenizer_file=__magic_name__			, do_lower_case=__magic_name__			, remove_space=__magic_name__			, keep_accents=__magic_name__			, unk_token=__magic_name__			, sep_token=__magic_name__			, pad_token=__magic_name__			, cls_token=__magic_name__			, mask_token=__magic_name__			, **__magic_name__			, )

										lowercase    :      Tuple       = do_lower_case
										lowercase    :      Tuple       = remove_space
										lowercase    :      List[str]       = keep_accents
										lowercase    :      Union[str, Any]       = vocab_file
										lowercase    :      int       = False if not self.vocab_file else True






						def      __snake_case      (		self  :Any			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      List[str]       = [self.sep_token_id]
										lowercase    :      Any       = [self.cls_token_id]
										if token_ids_a is None:
														return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep






						def      __snake_case      (		self  :int			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      Tuple       = [self.sep_token_id]
										lowercase    :      Union[str, Any]       = [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				) * [0] + len(token_ids_a + sep				) * [1]






						def      __snake_case      (		self  :int			, __magic_name__  :str			, __magic_name__  :Optional[str] = None				)      ->Tuple[str]:
										if not os.path.isdir(__magic_name__				):
														logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
														return
										lowercase    :      str       = os.path.join(
										    __magic_name__			, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

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

										return (out_vocab_file,)
