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       UpperCAmelCase__     (		SCREAMING_SNAKE_CASE_		,						SCREAMING_SNAKE_CASE_		,						SCREAMING_SNAKE_CASE_		,						unittest.TestCase       ):



  """simple docstring"""






  lowerCAmelCase__		:						Optional[Any]     		=			StableDiffusionInstructPixaPixPipeline
  lowerCAmelCase__		:						str     		=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
  lowerCAmelCase__		:						List[Any]     		=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  lowerCAmelCase__		:						Union[str, Any]     		=			IMAGE_TO_IMAGE_IMAGE_PARAMS
  lowerCAmelCase__		:						Union[str, Any]     		=			IMAGE_TO_IMAGE_IMAGE_PARAMS






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




    '''simple docstring'''




    torch.manual_seed(0 )
    __UpperCAmelCase         =							UNetaDConditionModel(
        block_out_channels=(32, 64)					,					layers_per_block=2					,					sample_size=32					,					in_channels=8					,					out_channels=4					,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,					up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")					,					cross_attention_dim=32					,					)
    __UpperCAmelCase         =							PNDMScheduler(skip_prk_steps=__snake_case )
    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					,					)
    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=1_000					,					)
    __UpperCAmelCase         =							CLIPTextModel(__snake_case )
    __UpperCAmelCase         =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

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






  def 						_UpperCAmelCase					(					self:      Optional[Any]					,					__lowerCAmelCase:      int					,					__lowerCAmelCase:      List[Any]=0 )			->			Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase         =							floats_tensor((1, 3, 32, 32)					,					rng=random.Random(__snake_case ) ).to(__snake_case )
    __UpperCAmelCase         =							image.cpu().permute(0					,					2					,					3					,					1 )[0]
    __UpperCAmelCase         =							Image.fromarray(np.uinta(__snake_case ) ).convert("RGB" )
    if str(__snake_case ).startswith("mps" ):
      __UpperCAmelCase         =							torch.manual_seed(__snake_case )
    else:
      __UpperCAmelCase         =							torch.Generator(device=__snake_case ).manual_seed(__snake_case )
    __UpperCAmelCase         =							{
        """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 						_UpperCAmelCase					(					self:      Any )			->			Tuple:




    '''simple docstring'''




    __UpperCAmelCase         =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
    __UpperCAmelCase         =							self.get_dummy_components()
    __UpperCAmelCase         =							StableDiffusionInstructPixaPixPipeline(**__snake_case )
    __UpperCAmelCase         =							sd_pipe.to(__snake_case )
    sd_pipe.set_progress_bar_config(disable=__snake_case )

    __UpperCAmelCase         =							self.get_dummy_inputs(__snake_case )
    __UpperCAmelCase         =							sd_pipe(**__snake_case ).images
    __UpperCAmelCase         =							image[0, -3:, -3:, -1]
    assert image.shape == (1, 32, 32, 3)
    __UpperCAmelCase         =							np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815] )

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






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




    '''simple docstring'''




    __UpperCAmelCase         =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
    __UpperCAmelCase         =							self.get_dummy_components()
    __UpperCAmelCase         =							StableDiffusionInstructPixaPixPipeline(**__snake_case )
    __UpperCAmelCase         =							sd_pipe.to(__snake_case )
    sd_pipe.set_progress_bar_config(disable=__snake_case )

    __UpperCAmelCase         =							self.get_dummy_inputs(__snake_case )
    __UpperCAmelCase         =							"""french fries"""
    __UpperCAmelCase         =							sd_pipe(**__snake_case					,					negative_prompt=__snake_case )
    __UpperCAmelCase         =							output.images
    __UpperCAmelCase         =							image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __UpperCAmelCase         =							np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831] )

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






  def 						_UpperCAmelCase					(					self:      Optional[int] )			->			Tuple:




    '''simple docstring'''




    __UpperCAmelCase         =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
    __UpperCAmelCase         =							self.get_dummy_components()
    __UpperCAmelCase         =							StableDiffusionInstructPixaPixPipeline(**__snake_case )
    __UpperCAmelCase         =							sd_pipe.to(__snake_case )
    sd_pipe.set_progress_bar_config(disable=__snake_case )

    __UpperCAmelCase         =							self.get_dummy_inputs(__snake_case )
    __UpperCAmelCase         =							[inputs["""prompt"""]] * 2

    __UpperCAmelCase         =							np.array(inputs["image"] ).astype(np.floataa ) / 255.0
    __UpperCAmelCase         =							torch.from_numpy(__snake_case ).unsqueeze(0 ).to(__snake_case )
    __UpperCAmelCase         =							image / 2 + 0.5
    __UpperCAmelCase         =							image.permute(0					,					3					,					1					,					2 )
    __UpperCAmelCase         =							image.repeat(2					,					1					,					1					,					1 )

    __UpperCAmelCase         =							sd_pipe(**__snake_case ).images
    __UpperCAmelCase         =							image[-1, -3:, -3:, -1]

    assert image.shape == (2, 32, 32, 3)
    __UpperCAmelCase         =							np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579] )

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






  def 						_UpperCAmelCase					(					self:      Tuple )			->			Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase         =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
    __UpperCAmelCase         =							self.get_dummy_components()
    __UpperCAmelCase         =							EulerAncestralDiscreteScheduler(
        beta_start=0.00085					,					beta_end=0.012					,					beta_schedule="scaled_linear" )
    __UpperCAmelCase         =							StableDiffusionInstructPixaPixPipeline(**__snake_case )
    __UpperCAmelCase         =							sd_pipe.to(__snake_case )
    sd_pipe.set_progress_bar_config(disable=__snake_case )

    __UpperCAmelCase         =							self.get_dummy_inputs(__snake_case )
    __UpperCAmelCase         =							sd_pipe(**__snake_case ).images
    __UpperCAmelCase         =							image[0, -3:, -3:, -1]

    __UpperCAmelCase         =							[round(__snake_case					,					4 ) for x in image_slice.flatten().tolist()]
    print(",".join([str(__snake_case ) for x in slice] ) )

    assert image.shape == (1, 32, 32, 3)
    __UpperCAmelCase         =							np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986] )

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






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




    '''simple docstring'''




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






  def 						_UpperCAmelCase					(					self:      Optional[Any] )			->			List[str]:




    '''simple docstring'''




    __UpperCAmelCase         =							self.get_dummy_components()
    __UpperCAmelCase         =							StableDiffusionInstructPixaPixPipeline(**__snake_case )
    __UpperCAmelCase         =							VaeImageProcessor(do_resize=__snake_case					,					do_normalize=__snake_case )
    __UpperCAmelCase         =							pipe.to(__snake_case )
    pipe.set_progress_bar_config(disable=__snake_case )

    __UpperCAmelCase         =							pipe(**self.get_dummy_inputs_by_type(__snake_case					,					input_image_type="pt" ) )[0]

    __UpperCAmelCase         =							components["""vae"""]
    __UpperCAmelCase         =							self.get_dummy_inputs_by_type(__snake_case					,					input_image_type="pt" )

    for image_param in self.image_latents_params:
      if image_param in inputs.keys():
        __UpperCAmelCase         =							vae.encode(inputs[image_param] ).latent_dist.mode()

    __UpperCAmelCase         =							pipe(**__snake_case )[0]

    __UpperCAmelCase         =							np.abs(out - out_latents_inputs ).max()
    self.assertLess(__snake_case					,					1E-4					,					"passing latents as image input generate different result from passing image" )






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



  """simple docstring"""






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




    '''simple docstring'''




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






  def 						_UpperCAmelCase					(					self:      str					,					__lowerCAmelCase:      List[str]=0 )			->			Dict:




    '''simple docstring'''




    __UpperCAmelCase         =							torch.manual_seed(__snake_case )
    __UpperCAmelCase         =							load_image(
        "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg" )
    __UpperCAmelCase         =							{
        """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 						_UpperCAmelCase					(					self:      Any )			->			Any:




    '''simple docstring'''




    __UpperCAmelCase         =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
        "timbrooks/instruct-pix2pix"					,					safety_checker=__snake_case )
    pipe.to(__snake_case )
    pipe.set_progress_bar_config(disable=__snake_case )
    pipe.enable_attention_slicing()

    __UpperCAmelCase         =							self.get_inputs()
    __UpperCAmelCase         =							pipe(**__snake_case ).images
    __UpperCAmelCase         =							image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 512, 512, 3)
    __UpperCAmelCase         =							np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555] )

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






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




    '''simple docstring'''




    __UpperCAmelCase         =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
        "timbrooks/instruct-pix2pix"					,					safety_checker=__snake_case )
    __UpperCAmelCase         =							LMSDiscreteScheduler.from_config(pipe.scheduler.config )
    pipe.to(__snake_case )
    pipe.set_progress_bar_config(disable=__snake_case )
    pipe.enable_attention_slicing()

    __UpperCAmelCase         =							self.get_inputs()
    __UpperCAmelCase         =							pipe(**__snake_case ).images
    __UpperCAmelCase         =							image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 512, 512, 3)
    __UpperCAmelCase         =							np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301] )

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






  def 						_UpperCAmelCase					(					self:      Any )			->			List[str]:




    '''simple docstring'''




    __UpperCAmelCase         =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
        "timbrooks/instruct-pix2pix"					,					safety_checker=__snake_case )
    __UpperCAmelCase         =							DDIMScheduler.from_config(pipe.scheduler.config )
    pipe.to(__snake_case )
    pipe.set_progress_bar_config(disable=__snake_case )
    pipe.enable_attention_slicing()

    __UpperCAmelCase         =							self.get_inputs()
    __UpperCAmelCase         =							pipe(**__snake_case ).images
    __UpperCAmelCase         =							image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 512, 512, 3)
    __UpperCAmelCase         =							np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753] )

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






  def 						_UpperCAmelCase					(					self:      int )			->			Dict:




    '''simple docstring'''




    __UpperCAmelCase         =							0

    def callback_fn(__lowerCAmelCase:      int					,					__lowerCAmelCase:      int					,					__lowerCAmelCase:      torch.FloatTensor ) -> None:
      __UpperCAmelCase         =							True
      nonlocal number_of_steps
      number_of_steps += 1
      if step == 1:
        __UpperCAmelCase         =							latents.detach().cpu().numpy()
        assert latents.shape == (1, 4, 64, 64)
        __UpperCAmelCase         =							latents[0, -3:, -3:, -1]
        __UpperCAmelCase         =							np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983] )

        assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
      elif step == 2:
        __UpperCAmelCase         =							latents.detach().cpu().numpy()
        assert latents.shape == (1, 4, 64, 64)
        __UpperCAmelCase         =							latents[0, -3:, -3:, -1]
        __UpperCAmelCase         =							np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115] )

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

    __UpperCAmelCase         =							False

    __UpperCAmelCase         =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
        "timbrooks/instruct-pix2pix"					,					safety_checker=__snake_case					,					torch_dtype=torch.floataa )
    __UpperCAmelCase         =							pipe.to(__snake_case )
    pipe.set_progress_bar_config(disable=__snake_case )
    pipe.enable_attention_slicing()

    __UpperCAmelCase         =							self.get_inputs()
    pipe(**__snake_case					,					callback=__snake_case					,					callback_steps=1 )
    assert callback_fn.has_been_called
    assert number_of_steps == 3






  def 						_UpperCAmelCase					(					self:      Tuple )			->			int:




    '''simple docstring'''




    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

    __UpperCAmelCase         =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
        "timbrooks/instruct-pix2pix"					,					safety_checker=__snake_case					,					torch_dtype=torch.floataa )
    __UpperCAmelCase         =							pipe.to(__snake_case )
    pipe.set_progress_bar_config(disable=__snake_case )
    pipe.enable_attention_slicing(1 )
    pipe.enable_sequential_cpu_offload()

    __UpperCAmelCase         =							self.get_inputs()
    __UpperCAmelCase         =							pipe(**__snake_case )

    __UpperCAmelCase         =							torch.cuda.max_memory_allocated()
    # make sure that less than 2.2 GB is allocated
    assert mem_bytes < 2.2 * 10**9






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




    '''simple docstring'''




    __UpperCAmelCase         =							self.get_inputs()
    # resize to resolution that is divisible by 8 but not 16 or 32
    __UpperCAmelCase         =							inputs["""image"""].resize((504, 504) )

    __UpperCAmelCase         =							"""timbrooks/instruct-pix2pix"""
    __UpperCAmelCase         =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
        __snake_case					,					safety_checker=__snake_case					,					)
    pipe.to(__snake_case )
    pipe.set_progress_bar_config(disable=__snake_case )
    pipe.enable_attention_slicing()

    __UpperCAmelCase         =							pipe(**__snake_case )
    __UpperCAmelCase         =							output.images[0]

    __UpperCAmelCase         =							image[255:258, 383:386, -1]

    assert image.shape == (504, 504, 3)
    __UpperCAmelCase         =							np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590] )

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











"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCamelCase							=  {
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCamelCase							=  {
    'squeezebert/squeezebert-uncased': 5_12,
    'squeezebert/squeezebert-mnli': 5_12,
    'squeezebert/squeezebert-mnli-headless': 5_12,
}


__lowerCamelCase							=  {
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				VOCAB_FILES_NAMES
					UpperCAmelCase__								=				PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__								=				PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__								=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__								=				SqueezeBertTokenizer

					def __init__(			self  :      Tuple     ,		__snake_case  :      str=None     ,		__snake_case  :      Optional[Any]=None     ,		__snake_case  :      str=True     ,		__snake_case  :      str="[UNK]"     ,		__snake_case  :      int="[SEP]"     ,		__snake_case  :      int="[PAD]"     ,		__snake_case  :      Any="[CLS]"     ,		__snake_case  :      List[Any]="[MASK]"     ,		__snake_case  :      Union[str, Any]=True     ,		__snake_case  :      Dict=None     ,		**__snake_case  :      str     ,		)			->							List[Any]:
											super().__init__(
											    __snake_case     ,		tokenizer_file=__snake_case     ,		do_lower_case=__snake_case     ,		unk_token=__snake_case     ,		sep_token=__snake_case     ,		pad_token=__snake_case     ,		cls_token=__snake_case     ,		mask_token=__snake_case     ,		tokenize_chinese_chars=__snake_case     ,		strip_accents=__snake_case     ,		**__snake_case     ,		)

											__magic_name__:	List[Any]							=       json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
											if (
											    normalizer_state.get("""lowercase"""     ,		__snake_case					) != do_lower_case
											    or normalizer_state.get("""strip_accents"""     ,		__snake_case					) != strip_accents
											    or normalizer_state.get("""handle_chinese_chars"""     ,		__snake_case					) != tokenize_chinese_chars
											):
																	__magic_name__:	Dict							=       getattr(__snake_case     ,		normalizer_state.pop("""type"""					)					)
																	__magic_name__:	Optional[Any]							=       do_lower_case
																	__magic_name__:	Any							=       strip_accents
																	__magic_name__:	Any							=       tokenize_chinese_chars
																	__magic_name__:	Union[str, Any]							=       normalizer_class(**__snake_case					)

											__magic_name__:	Optional[Any]							=       do_lower_case

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      List[Any]     ,		__snake_case  :      int=None					)			->							Optional[int]:
											__magic_name__:	int							=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

											if token_ids_a:
																	output += token_ids_a + [self.sep_token_id]

											return output

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






					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      str     ,		__snake_case  :      Optional[str] = None					)			->							Tuple[str]:
											__magic_name__:	Any							=       self._tokenizer.model.save(__snake_case     ,		name=__snake_case					)
											return tuple(__snake_case					)



