
"""simple docstring"""


import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class  __lowerCamelCase  ( lowerCAmelCase						,	lowerCAmelCase						,	unittest.TestCase			):
				a__:      Optional[int]          =    StableDiffusionDiffEditPipeline
				a__:      Any          =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
				a__:      Optional[int]          =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
				a__:      str          =    frozenset(
				    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				a__:      Dict          =    frozenset([]			)




				def        UpperCAmelCase__						(				self   ):
							torch.manual_seed(0   )
							lowerCamelCase_					=  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							,						attention_head_dim=(2, 4)							,						use_linear_projection=UpperCAmelCase							,						)
							lowerCamelCase_					=  DDIMScheduler(
							    beta_start=0.0_0_0_8_5							,						beta_end=0.0_1_2							,						beta_schedule='''scaled_linear'''							,						clip_sample=UpperCAmelCase							,						set_alpha_to_one=UpperCAmelCase							,						)
							lowerCamelCase_					=  DDIMInverseScheduler(
							    beta_start=0.0_0_0_8_5							,						beta_end=0.0_1_2							,						beta_schedule='''scaled_linear'''							,						clip_sample=UpperCAmelCase							,						set_alpha_to_zero=UpperCAmelCase							,						)
							torch.manual_seed(0   )
							lowerCamelCase_					=  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							,						sample_size=128							,						)
							torch.manual_seed(0   )
							lowerCamelCase_					=  CLIPTextConfig(
							    bos_token_id=0							,						eos_token_id=2							,						hidden_size=32							,						intermediate_size=37							,						layer_norm_eps=1e-0_5							,						num_attention_heads=4							,						num_hidden_layers=5							,						pad_token_id=1							,						vocab_size=1000							,						hidden_act='''gelu'''							,						projection_dim=512							,						)
							lowerCamelCase_					=  CLIPTextModel(UpperCAmelCase   )
							lowerCamelCase_					=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

							lowerCamelCase_					=  {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''inverse_scheduler''': inverse_scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}

							return components




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=0   ):
							lowerCamelCase_					=  floats_tensor((1, 16, 16)							,						rng=random.Random(UpperCAmelCase   )   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  floats_tensor((1, 2, 4, 16, 16)							,						rng=random.Random(UpperCAmelCase   )   ).to(UpperCAmelCase   )
							if str(UpperCAmelCase   ).startswith('''mps'''   ):
										lowerCamelCase_					=  torch.manual_seed(UpperCAmelCase   )
							else:
										lowerCamelCase_					=  torch.Generator(device=UpperCAmelCase   ).manual_seed(UpperCAmelCase   )
							lowerCamelCase_					=  {
							    '''prompt''': '''a dog and a newt''',
							    '''mask_image''': mask,
							    '''image_latents''': latents,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''inpaint_strength''': 1.0,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}

							return inputs




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=0   ):
							lowerCamelCase_					=  floats_tensor((1, 3, 32, 32)							,						rng=random.Random(UpperCAmelCase   )   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  image.cpu().permute(0							,						2							,						3							,						1   )[0]
							lowerCamelCase_					=  Image.fromarray(np.uinta(UpperCAmelCase   )   ).convert('''RGB'''   )
							if str(UpperCAmelCase   ).startswith('''mps'''   ):
										lowerCamelCase_					=  torch.manual_seed(UpperCAmelCase   )
							else:
										lowerCamelCase_					=  torch.Generator(device=UpperCAmelCase   ).manual_seed(UpperCAmelCase   )
							lowerCamelCase_					=  {
							    '''image''': image,
							    '''source_prompt''': '''a cat and a frog''',
							    '''target_prompt''': '''a dog and a newt''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''num_maps_per_mask''': 2,
							    '''mask_encode_strength''': 1.0,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}

							return inputs




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase=0   ):
							lowerCamelCase_					=  floats_tensor((1, 3, 32, 32)							,						rng=random.Random(UpperCAmelCase   )   ).to(UpperCAmelCase   )
							lowerCamelCase_					=  image.cpu().permute(0							,						2							,						3							,						1   )[0]
							lowerCamelCase_					=  Image.fromarray(np.uinta(UpperCAmelCase   )   ).convert('''RGB'''   )
							if str(UpperCAmelCase   ).startswith('''mps'''   ):
										lowerCamelCase_					=  torch.manual_seed(UpperCAmelCase   )
							else:
										lowerCamelCase_					=  torch.Generator(device=UpperCAmelCase   ).manual_seed(UpperCAmelCase   )
							lowerCamelCase_					=  {
							    '''image''': image,
							    '''prompt''': '''a cat and a frog''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''inpaint_strength''': 1.0,
							    '''guidance_scale''': 6.0,
							    '''decode_latents''': True,
							    '''output_type''': '''numpy''',
							}
							return inputs




				def        UpperCAmelCase__						(				self   ):
							if not hasattr(self.pipeline_class							,						'''_optional_components'''   ):
										return

							lowerCamelCase_					=  self.get_dummy_components()
							lowerCamelCase_					=  self.pipeline_class(**UpperCAmelCase   )
							pipe.to(UpperCAmelCase   )
							pipe.set_progress_bar_config(disable=UpperCAmelCase   )

							# set all optional components to None and update pipeline config accordingly
							for optional_component in pipe._optional_components:
										setattr(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )
							pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}   )

							lowerCamelCase_					=  self.get_dummy_inputs(UpperCAmelCase   )
							lowerCamelCase_					=  pipe(**UpperCAmelCase   )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(UpperCAmelCase   )
										lowerCamelCase_					=  self.pipeline_class.from_pretrained(UpperCAmelCase   )
										pipe_loaded.to(UpperCAmelCase   )
										pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase   )

							for optional_component in pipe._optional_components:
										self.assertTrue(
										    getattr(UpperCAmelCase							,						UpperCAmelCase   ) is None							,						f"`{optional_component}` did not stay set to None after loading."							,						)

							lowerCamelCase_					=  self.get_dummy_inputs(UpperCAmelCase   )
							lowerCamelCase_					=  pipe_loaded(**UpperCAmelCase   )[0]

							lowerCamelCase_					=  np.abs(output - output_loaded   ).max()
							self.assertLess(UpperCAmelCase							,						1e-4   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  '''cpu'''

							lowerCamelCase_					=  self.get_dummy_components()
							lowerCamelCase_					=  self.pipeline_class(**UpperCAmelCase   )
							pipe.to(UpperCAmelCase   )
							pipe.set_progress_bar_config(disable=UpperCAmelCase   )

							lowerCamelCase_					=  self.get_dummy_mask_inputs(UpperCAmelCase   )
							lowerCamelCase_					=  pipe.generate_mask(**UpperCAmelCase   )
							lowerCamelCase_					=  mask[0, -3:, -3:]

							self.assertEqual(mask.shape							,						(1, 16, 16)   )
							lowerCamelCase_					=  np.array([0] * 9   )
							lowerCamelCase_					=  np.abs(mask_slice.flatten() - expected_slice   ).max()
							self.assertLessEqual(UpperCAmelCase							,						1e-3   )
							self.assertEqual(mask[0, -3, -4]							,						0   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  '''cpu'''

							lowerCamelCase_					=  self.get_dummy_components()
							lowerCamelCase_					=  self.pipeline_class(**UpperCAmelCase   )
							pipe.to(UpperCAmelCase   )
							pipe.set_progress_bar_config(disable=UpperCAmelCase   )

							lowerCamelCase_					=  self.get_dummy_inversion_inputs(UpperCAmelCase   )
							lowerCamelCase_					=  pipe.invert(**UpperCAmelCase   ).images
							lowerCamelCase_					=  image[0, -1, -3:, -3:]

							self.assertEqual(image.shape							,						(2, 32, 32, 3)   )
							lowerCamelCase_					=  np.array(
							    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]							,						)
							lowerCamelCase_					=  np.abs(image_slice.flatten() - expected_slice   ).max()
							self.assertLessEqual(UpperCAmelCase							,						1e-3   )




				def        UpperCAmelCase__						(				self   ):
							super().test_inference_batch_single_identical(expected_max_diff=5e-3   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  '''cpu'''

							lowerCamelCase_					=  self.get_dummy_components()

							lowerCamelCase_					=  {'''beta_start''': 0.0_0_0_8_5, '''beta_end''': 0.0_1_2, '''beta_schedule''': '''scaled_linear'''}
							lowerCamelCase_					=  DPMSolverMultistepScheduler(**UpperCAmelCase   )
							lowerCamelCase_					=  DPMSolverMultistepInverseScheduler(**UpperCAmelCase   )

							lowerCamelCase_					=  self.pipeline_class(**UpperCAmelCase   )
							pipe.to(UpperCAmelCase   )
							pipe.set_progress_bar_config(disable=UpperCAmelCase   )

							lowerCamelCase_					=  self.get_dummy_inversion_inputs(UpperCAmelCase   )
							lowerCamelCase_					=  pipe.invert(**UpperCAmelCase   ).images
							lowerCamelCase_					=  image[0, -1, -3:, -3:]

							self.assertEqual(image.shape							,						(2, 32, 32, 3)   )
							lowerCamelCase_					=  np.array(
							    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]							,						)
							lowerCamelCase_					=  np.abs(image_slice.flatten() - expected_slice   ).max()
							self.assertLessEqual(UpperCAmelCase							,						1e-3   )




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




				def        UpperCAmelCase__						(				self   ):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				@classmethod
				def        UpperCAmelCase__						(				cls   ):
							lowerCamelCase_					=  load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''   )

							lowerCamelCase_					=  raw_image.convert('''RGB'''   ).resize((768, 768)   )

							lowerCamelCase_					=  raw_image




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  torch.manual_seed(0   )

							lowerCamelCase_					=  StableDiffusionDiffEditPipeline.from_pretrained(
							    '''stabilityai/stable-diffusion-2-1'''							,						safety_checker=UpperCAmelCase							,						torch_dtype=torch.floataa   )
							lowerCamelCase_					=  DDIMScheduler.from_config(pipe.scheduler.config   )
							lowerCamelCase_					=  DDIMInverseScheduler.from_config(pipe.scheduler.config   )
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=UpperCAmelCase   )

							lowerCamelCase_					=  '''a bowl of fruit'''
							lowerCamelCase_					=  '''a bowl of pears'''

							lowerCamelCase_					=  pipe.generate_mask(
							    image=self.raw_image							,						source_prompt=UpperCAmelCase							,						target_prompt=UpperCAmelCase							,						generator=UpperCAmelCase							,						)

							lowerCamelCase_					=  pipe.invert(
							    prompt=UpperCAmelCase							,						image=self.raw_image							,						inpaint_strength=0.7							,						generator=UpperCAmelCase   ).latents

							lowerCamelCase_					=  pipe(
							    prompt=UpperCAmelCase							,						mask_image=UpperCAmelCase							,						image_latents=UpperCAmelCase							,						generator=UpperCAmelCase							,						negative_prompt=UpperCAmelCase							,						inpaint_strength=0.7							,						output_type='''numpy'''							,						).images[0]

							lowerCamelCase_					=  (
							    np.array(
							        load_image(
							            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							            '''/diffedit/pears.png'''   ).resize((768, 768)   )   )
							    / 255
							)
							assert np.abs((expected_image - image).max()   ) < 5e-1





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  torch.manual_seed(0   )

							lowerCamelCase_					=  StableDiffusionDiffEditPipeline.from_pretrained(
							    '''stabilityai/stable-diffusion-2-1'''							,						safety_checker=UpperCAmelCase							,						torch_dtype=torch.floataa   )
							lowerCamelCase_					=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
							lowerCamelCase_					=  DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config   )
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=UpperCAmelCase   )

							lowerCamelCase_					=  '''a bowl of fruit'''
							lowerCamelCase_					=  '''a bowl of pears'''

							lowerCamelCase_					=  pipe.generate_mask(
							    image=self.raw_image							,						source_prompt=UpperCAmelCase							,						target_prompt=UpperCAmelCase							,						generator=UpperCAmelCase							,						)

							lowerCamelCase_					=  pipe.invert(
							    prompt=UpperCAmelCase							,						image=self.raw_image							,						inpaint_strength=0.7							,						generator=UpperCAmelCase							,						num_inference_steps=25							,						).latents

							lowerCamelCase_					=  pipe(
							    prompt=UpperCAmelCase							,						mask_image=UpperCAmelCase							,						image_latents=UpperCAmelCase							,						generator=UpperCAmelCase							,						negative_prompt=UpperCAmelCase							,						inpaint_strength=0.7							,						num_inference_steps=25							,						output_type='''numpy'''							,						).images[0]

							lowerCamelCase_					=  (
							    np.array(
							        load_image(
							            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							            '''/diffedit/pears.png'''   ).resize((768, 768)   )   )
							    / 255
							)
							assert np.abs((expected_image - image).max()   ) < 5e-1










"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__A											=						logging.get_logger(__name__)

__A											=						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__A											=						{
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

__A											=						{
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:str						    =   VOCAB_FILES_NAMES
       __magic_name__	:List[Any]						    =   PRETRAINED_VOCAB_FILES_MAP
       __magic_name__	:Optional[Any]						    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __magic_name__	:str						    =   ["""input_ids""", """attention_mask"""]
       __magic_name__	:Any						    =   RobertaTokenizer






       def __init__(       self       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase="replace"       ,					__UpperCAmelCase="<s>"       ,					__UpperCAmelCase="</s>"       ,					__UpperCAmelCase="</s>"       ,					__UpperCAmelCase="<s>"       ,					__UpperCAmelCase="<unk>"       ,					__UpperCAmelCase="<pad>"       ,					__UpperCAmelCase="<mask>"       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=True       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         super().__init__(
             __UpperCAmelCase       ,					__UpperCAmelCase       ,					tokenizer_file=__UpperCAmelCase       ,					errors=__UpperCAmelCase       ,					bos_token=__UpperCAmelCase       ,					eos_token=__UpperCAmelCase       ,					sep_token=__UpperCAmelCase       ,					cls_token=__UpperCAmelCase       ,					unk_token=__UpperCAmelCase       ,					pad_token=__UpperCAmelCase       ,					mask_token=__UpperCAmelCase       ,					add_prefix_space=__UpperCAmelCase       ,					trim_offsets=__UpperCAmelCase       ,					**__UpperCAmelCase       ,					)

         lowerCAmelCase__     :Dict											=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
         if pre_tok_state.get('add_prefix_space'       ,					__UpperCAmelCase ) != add_prefix_space:
           lowerCAmelCase__     :Optional[int]											=				getattr(__UpperCAmelCase       ,					pre_tok_state.pop('type' ) )
           lowerCAmelCase__     :List[Any]											=				add_prefix_space
           lowerCAmelCase__     :str											=				pre_tok_class(**__UpperCAmelCase )

         lowerCAmelCase__     :List[str]											=				add_prefix_space

         lowerCAmelCase__     :str											=				'post_processor'
         lowerCAmelCase__     :Optional[Any]											=				getattr(self.backend_tokenizer       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
         if tokenizer_component_instance:
           lowerCAmelCase__     :Optional[Any]											=				json.loads(tokenizer_component_instance.__getstate__() )

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
             lowerCAmelCase__     :Any											=				tuple(state['sep'] )
           if "cls" in state:
             lowerCAmelCase__     :int											=				tuple(state['cls'] )

           lowerCAmelCase__     :List[Any]											=				False

           if state.get('add_prefix_space'       ,					__UpperCAmelCase ) != add_prefix_space:
             lowerCAmelCase__     :Union[str, Any]											=				add_prefix_space
             lowerCAmelCase__     :Any											=				True

           if state.get('trim_offsets'       ,					__UpperCAmelCase ) != trim_offsets:
             lowerCAmelCase__     :Union[str, Any]											=				trim_offsets
             lowerCAmelCase__     :Optional[int]											=				True

           if changes_to_apply:
             lowerCAmelCase__     :str											=				getattr(__UpperCAmelCase       ,					state.pop('type' ) )
             lowerCAmelCase__     :Any											=				component_class(**__UpperCAmelCase )
             setattr(self.backend_tokenizer       ,					__UpperCAmelCase       ,					__UpperCAmelCase )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         if self._mask_token is None:
           if self.verbose:
             logger.error('Using mask_token, but it is not set yet.' )
           return None
         return str(self._mask_token )






       @mask_token.setter
       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				AddedToken(__UpperCAmelCase       ,					lstrip=__UpperCAmelCase       ,					rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else value
         lowerCAmelCase__     :List[str]											=				value






       def 	snake_case		(       self       ,					*__UpperCAmelCase       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				kwargs.get('is_split_into_words'       ,					__UpperCAmelCase )
         assert self.add_prefix_space or not is_split_into_words, (
             F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*__UpperCAmelCase       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					*__UpperCAmelCase       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				kwargs.get('is_split_into_words'       ,					__UpperCAmelCase )

         assert self.add_prefix_space or not is_split_into_words, (
             F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*__UpperCAmelCase       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				self._tokenizer.model.save(__UpperCAmelCase       ,					name=__UpperCAmelCase )
         return tuple(__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=None ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
           return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				[self.sep_token_id]
         lowerCAmelCase__     :int											=				[self.cls_token_id]

         if token_ids_a is None:
           return len(cls + token_ids_a + sep ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
