



'''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 				__lowerCAmelCase(							lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__		,			unittest.TestCase      ):
			__snake_case  :		Tuple		=      StableDiffusionImageVariationPipeline
			__snake_case  :		Optional[int]		=      IMAGE_VARIATION_PARAMS
			__snake_case  :		List[Any]		=      IMAGE_VARIATION_BATCH_PARAMS
			__snake_case  :		Any		=      frozenset([]      )
			# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			__snake_case  :		List[str]		=      frozenset([]      )






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Dict										=      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      ,       )
					SCREAMING_SNAKE_CASE_						:Dict										=      PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE	)
					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:int										=      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	)
					SCREAMING_SNAKE_CASE_						:Any										=      CLIPVisionConfig(
					    hidden_size=32      ,       projection_dim=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       image_size=32      ,       patch_size=4      ,       )
					SCREAMING_SNAKE_CASE_						:str										=      CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:str										=      CLIPImageProcessor(crop_size=32      ,       size=32	)

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






			def 				_lowercase	(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=0	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(SCREAMING_SNAKE_CASE	)	)
					SCREAMING_SNAKE_CASE_						:str										=      image.cpu().permute(0      ,       2      ,       3      ,       1	)[0]
					SCREAMING_SNAKE_CASE_						:int										=      Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE	)	).convert('RGB'	).resize((32, 32)	)
					if str(SCREAMING_SNAKE_CASE	).startswith('mps'	):
							SCREAMING_SNAKE_CASE_						:Dict										=      torch.manual_seed(SCREAMING_SNAKE_CASE	)
					else:
							SCREAMING_SNAKE_CASE_						:Tuple										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      {
					    'image': image,
					    'generator': generator,
					    'num_inference_steps': 2,
					    'guidance_scale': 6.0,
					    'output_type': 'numpy',
					}
					return inputs






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      'cpu'  # ensure determinism for the device-dependent torch.Generator
					SCREAMING_SNAKE_CASE_						:Any										=      self.get_dummy_components()
					SCREAMING_SNAKE_CASE_						:Any										=      StableDiffusionImageVariationPipeline(**SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      sd_pipe.to(SCREAMING_SNAKE_CASE	)
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:int										=      self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:str										=      sd_pipe(**SCREAMING_SNAKE_CASE	).images
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      image[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      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 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      'cpu'  # ensure determinism for the device-dependent torch.Generator
					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.get_dummy_components()
					SCREAMING_SNAKE_CASE_						:Tuple										=      StableDiffusionImageVariationPipeline(**SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      sd_pipe.to(SCREAMING_SNAKE_CASE	)
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:int										=      self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      2 * [inputs['image']]
					SCREAMING_SNAKE_CASE_						:Dict										=      sd_pipe(**SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      output.images

					SCREAMING_SNAKE_CASE_						:int										=      image[-1, -3:, -3:, -1]

					assert image.shape == (2, 64, 64, 3)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      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 				_lowercase	(   self  :							str	):




					"""simple docstring"""







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




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






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							Dict="cpu"      ,       SCREAMING_SNAKE_CASE  :							Any=torch.floataa      ,       SCREAMING_SNAKE_CASE  :							Tuple=0	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      load_image(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_imgvar/input_image_vermeer.png'	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      np.random.RandomState(SCREAMING_SNAKE_CASE	).standard_normal((1, 4, 64, 64)	)
					SCREAMING_SNAKE_CASE_						:str										=      torch.from_numpy(SCREAMING_SNAKE_CASE	).to(device=SCREAMING_SNAKE_CASE      ,       dtype=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:int										=      {
					    'image': init_image,
					    'latents': latents,
					    'generator': generator,
					    'num_inference_steps': 3,
					    'guidance_scale': 7.5,
					    'output_type': 'numpy',
					}
					return inputs






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      StableDiffusionImageVariationPipeline.from_pretrained(
					    'lambdalabs/sd-image-variations-diffusers'      ,       safety_checker=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      sd_pipe.to(SCREAMING_SNAKE_CASE	)
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

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

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      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 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      0

					def callback_fn(SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							torch.FloatTensor	) -> None:
							SCREAMING_SNAKE_CASE_						:List[Any]										=      True
							nonlocal number_of_steps
							number_of_steps += 1
							if step == 1:
									SCREAMING_SNAKE_CASE_						:List[Any]										=      latents.detach().cpu().numpy()
									assert latents.shape == (1, 4, 64, 64)
									SCREAMING_SNAKE_CASE_						:Tuple										=      latents[0, -3:, -3:, -1]
									SCREAMING_SNAKE_CASE_						:Tuple										=      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:
									SCREAMING_SNAKE_CASE_						:List[Any]										=      latents.detach().cpu().numpy()
									assert latents.shape == (1, 4, 64, 64)
									SCREAMING_SNAKE_CASE_						:Dict										=      latents[0, -3:, -3:, -1]
									SCREAMING_SNAKE_CASE_						:Dict										=      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

					SCREAMING_SNAKE_CASE_						:List[Any]										=      False

					SCREAMING_SNAKE_CASE_						:Any										=      StableDiffusionImageVariationPipeline.from_pretrained(
					    'fusing/sd-image-variations-diffusers'      ,       safety_checker=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()

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






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







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

					SCREAMING_SNAKE_CASE_						:List[Any]										=      'fusing/sd-image-variations-diffusers'
					SCREAMING_SNAKE_CASE_						:Tuple										=      StableDiffusionImageVariationPipeline.from_pretrained(
					    SCREAMING_SNAKE_CASE      ,       safety_checker=SCREAMING_SNAKE_CASE      ,       torch_dtype=torch.floataa	)
					SCREAMING_SNAKE_CASE_						:Dict										=      pipe.to(SCREAMING_SNAKE_CASE	)
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)
					pipe.enable_attention_slicing(1	)
					pipe.enable_sequential_cpu_offload()

					SCREAMING_SNAKE_CASE_						:List[str]										=      self.get_inputs(SCREAMING_SNAKE_CASE      ,       dtype=torch.floataa	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      pipe(**SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Any										=      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 				__lowerCAmelCase(							unittest.TestCase      ):






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							Any="cpu"      ,       SCREAMING_SNAKE_CASE  :							List[Any]=torch.floataa      ,       SCREAMING_SNAKE_CASE  :							List[str]=0	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:int										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      load_image(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_imgvar/input_image_vermeer.png'	)
					SCREAMING_SNAKE_CASE_						:int										=      np.random.RandomState(SCREAMING_SNAKE_CASE	).standard_normal((1, 4, 64, 64)	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.from_numpy(SCREAMING_SNAKE_CASE	).to(device=SCREAMING_SNAKE_CASE      ,       dtype=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Any										=      {
					    'image': init_image,
					    'latents': latents,
					    'generator': generator,
					    'num_inference_steps': 50,
					    'guidance_scale': 7.5,
					    'output_type': 'numpy',
					}
					return inputs






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'	)
					sd_pipe.to(SCREAMING_SNAKE_CASE	)
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      self.get_inputs(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      sd_pipe(**SCREAMING_SNAKE_CASE	).images[0]

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






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'	)
					SCREAMING_SNAKE_CASE_						:int										=      DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config	)
					sd_pipe.to(SCREAMING_SNAKE_CASE	)
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Any										=      self.get_inputs(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      25
					SCREAMING_SNAKE_CASE_						:List[str]										=      sd_pipe(**SCREAMING_SNAKE_CASE	).images[0]

					SCREAMING_SNAKE_CASE_						:str										=      load_numpy(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      np.abs(expected_image - image	).max()
					assert max_diff < 1E-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_layoutlm import LayoutLMTokenizer


SCREAMING_SNAKE_CASE__     : List[Any]				=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     : List[str]				=   {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__     : Optional[int]				=   {
    """vocab_file""": {
        """microsoft/layoutlm-base-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt"""
        ),
        """microsoft/layoutlm-large-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """microsoft/layoutlm-base-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json"""
        ),
        """microsoft/layoutlm-large-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__     : Tuple				=   {
    """microsoft/layoutlm-base-uncased""": 5_12,
    """microsoft/layoutlm-large-uncased""": 5_12,
}

SCREAMING_SNAKE_CASE__     : List[Any]				=   {
    """microsoft/layoutlm-base-uncased""": {"""do_lower_case""": True},
    """microsoft/layoutlm-large-uncased""": {"""do_lower_case""": True},
}




class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		List[str]		=      VOCAB_FILES_NAMES
			__snake_case  :		Dict		=      PRETRAINED_VOCAB_FILES_MAP
			__snake_case  :		Optional[int]		=      PRETRAINED_INIT_CONFIGURATION
			__snake_case  :		Any		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case  :		str		=      LayoutLMTokenizer






			def __init__(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Dict=None      ,       SCREAMING_SNAKE_CASE  :							int=None      ,       SCREAMING_SNAKE_CASE  :							Tuple=True      ,       SCREAMING_SNAKE_CASE  :							Optional[int]="[UNK]"      ,       SCREAMING_SNAKE_CASE  :							Tuple="[SEP]"      ,       SCREAMING_SNAKE_CASE  :							int="[PAD]"      ,       SCREAMING_SNAKE_CASE  :							List[Any]="[CLS]"      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]="[MASK]"      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=True      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=None      ,       **SCREAMING_SNAKE_CASE  :							Optional[int]      ,       ):




					"""simple docstring"""







					super().__init__(
					    SCREAMING_SNAKE_CASE      ,       tokenizer_file=SCREAMING_SNAKE_CASE      ,       do_lower_case=SCREAMING_SNAKE_CASE      ,       unk_token=SCREAMING_SNAKE_CASE      ,       sep_token=SCREAMING_SNAKE_CASE      ,       pad_token=SCREAMING_SNAKE_CASE      ,       cls_token=SCREAMING_SNAKE_CASE      ,       mask_token=SCREAMING_SNAKE_CASE      ,       tokenize_chinese_chars=SCREAMING_SNAKE_CASE      ,       strip_accents=SCREAMING_SNAKE_CASE      ,       **SCREAMING_SNAKE_CASE      ,       )

					SCREAMING_SNAKE_CASE_						:str										=      json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
					if (
					    normalizer_state.get('lowercase'      ,       SCREAMING_SNAKE_CASE	) != do_lower_case
					    or normalizer_state.get('strip_accents'      ,       SCREAMING_SNAKE_CASE	) != strip_accents
					    or normalizer_state.get('handle_chinese_chars'      ,       SCREAMING_SNAKE_CASE	) != tokenize_chinese_chars
					):
							SCREAMING_SNAKE_CASE_						:List[str]										=      getattr(SCREAMING_SNAKE_CASE      ,       normalizer_state.pop('type'	)	)
							SCREAMING_SNAKE_CASE_						:Tuple										=      do_lower_case
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      strip_accents
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      tokenize_chinese_chars
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      normalizer_class(**SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      do_lower_case






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							str=None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[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 				_lowercase	(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							List[int]      ,       SCREAMING_SNAKE_CASE  :							Optional[List[int]] = None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      [self.sep_token_id]
					SCREAMING_SNAKE_CASE_						:Optional[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 				_lowercase	(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							Optional[str] = None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self._tokenizer.model.save(SCREAMING_SNAKE_CASE      ,       name=SCREAMING_SNAKE_CASE	)
					return tuple(SCREAMING_SNAKE_CASE	)
