






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__       							=	StableDiffusionDiffEditPipeline
				a__       							=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
				a__       							=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
				a__       							=	frozenset(
				    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				a__       							=	frozenset([]			)







				def       A						(    self  ):
								"""simple docstring"""
								torch.manual_seed(0  )
								__magic_name__		:int   =   UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)							,       layers_per_block=2							,       sample_size=3_2							,       in_channels=4							,       out_channels=4							,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')							,       cross_attention_dim=3_2							,       attention_head_dim=(2, 4)							,       use_linear_projection=__lowerCAmelCase							,       )
								__magic_name__		:Dict   =   DDIMScheduler(
								    beta_start=0.00085							,       beta_end=0.012							,       beta_schedule='''scaled_linear'''							,       clip_sample=__lowerCAmelCase							,       set_alpha_to_one=__lowerCAmelCase							,       )
								__magic_name__		:Union[str, Any]   =   DDIMInverseScheduler(
								    beta_start=0.00085							,       beta_end=0.012							,       beta_schedule='''scaled_linear'''							,       clip_sample=__lowerCAmelCase							,       set_alpha_to_zero=__lowerCAmelCase							,       )
								torch.manual_seed(0  )
								__magic_name__		:Optional[int]   =   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							,       sample_size=1_2_8							,       )
								torch.manual_seed(0  )
								__magic_name__		:Tuple   =   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							,       hidden_act='''gelu'''							,       projection_dim=5_1_2							,       )
								__magic_name__		:Dict   =   CLIPTextModel(__lowerCAmelCase  )
								__magic_name__		:Any   =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

								__magic_name__		:Tuple   =   {
								    '''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       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=0  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   floats_tensor((1, 1_6, 1_6)							,       rng=random.Random(__lowerCAmelCase  )  ).to(__lowerCAmelCase  )
								__magic_name__		:List[str]   =   floats_tensor((1, 2, 4, 1_6, 1_6)							,       rng=random.Random(__lowerCAmelCase  )  ).to(__lowerCAmelCase  )
								if str(__lowerCAmelCase  ).startswith('''mps'''  ):
												__magic_name__		:Dict   =   torch.manual_seed(__lowerCAmelCase  )
								else:
												__magic_name__		:Dict   =   torch.Generator(device=__lowerCAmelCase  ).manual_seed(__lowerCAmelCase  )
								__magic_name__		:Any   =   {
								    '''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       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=0  ):
								"""simple docstring"""
								__magic_name__		:int   =   floats_tensor((1, 3, 3_2, 3_2)							,       rng=random.Random(__lowerCAmelCase  )  ).to(__lowerCAmelCase  )
								__magic_name__		:Optional[int]   =   image.cpu().permute(0							,       2							,       3							,       1  )[0]
								__magic_name__		:List[Any]   =   Image.fromarray(np.uinta(__lowerCAmelCase  )  ).convert('''RGB'''  )
								if str(__lowerCAmelCase  ).startswith('''mps'''  ):
												__magic_name__		:str   =   torch.manual_seed(__lowerCAmelCase  )
								else:
												__magic_name__		:str   =   torch.Generator(device=__lowerCAmelCase  ).manual_seed(__lowerCAmelCase  )
								__magic_name__		:str   =   {
								    '''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       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=0  ):
								"""simple docstring"""
								__magic_name__		:str   =   floats_tensor((1, 3, 3_2, 3_2)							,       rng=random.Random(__lowerCAmelCase  )  ).to(__lowerCAmelCase  )
								__magic_name__		:str   =   image.cpu().permute(0							,       2							,       3							,       1  )[0]
								__magic_name__		:List[Any]   =   Image.fromarray(np.uinta(__lowerCAmelCase  )  ).convert('''RGB'''  )
								if str(__lowerCAmelCase  ).startswith('''mps'''  ):
												__magic_name__		:Optional[Any]   =   torch.manual_seed(__lowerCAmelCase  )
								else:
												__magic_name__		:Dict   =   torch.Generator(device=__lowerCAmelCase  ).manual_seed(__lowerCAmelCase  )
								__magic_name__		:Dict   =   {
								    '''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       A						(    self  ):
								"""simple docstring"""
								if not hasattr(self.pipeline_class							,       '''_optional_components'''  ):
												return

								__magic_name__		:Optional[int]   =   self.get_dummy_components()
								__magic_name__		:str   =   self.pipeline_class(**__lowerCAmelCase  )
								pipe.to(__lowerCAmelCase  )
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

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

								__magic_name__		:Tuple   =   self.get_dummy_inputs(__lowerCAmelCase  )
								__magic_name__		:List[Any]   =   pipe(**__lowerCAmelCase  )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
												pipe.save_pretrained(__lowerCAmelCase  )
												__magic_name__		:Any   =   self.pipeline_class.from_pretrained(__lowerCAmelCase  )
												pipe_loaded.to(__lowerCAmelCase  )
												pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase  )

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

								__magic_name__		:int   =   self.get_dummy_inputs(__lowerCAmelCase  )
								__magic_name__		:str   =   pipe_loaded(**__lowerCAmelCase  )[0]

								__magic_name__		:int   =   np.abs(output - output_loaded  ).max()
								self.assertLess(__lowerCAmelCase							,       1E-4  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   '''cpu'''

								__magic_name__		:List[Any]   =   self.get_dummy_components()
								__magic_name__		:Tuple   =   self.pipeline_class(**__lowerCAmelCase  )
								pipe.to(__lowerCAmelCase  )
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

								__magic_name__		:str   =   self.get_dummy_mask_inputs(__lowerCAmelCase  )
								__magic_name__		:Union[str, Any]   =   pipe.generate_mask(**__lowerCAmelCase  )
								__magic_name__		:str   =   mask[0, -3:, -3:]

								self.assertEqual(mask.shape							,       (1, 1_6, 1_6)  )
								__magic_name__		:List[str]   =   np.array([0] * 9  )
								__magic_name__		:Optional[int]   =   np.abs(mask_slice.flatten() - expected_slice  ).max()
								self.assertLessEqual(__lowerCAmelCase							,       1E-3  )
								self.assertEqual(mask[0, -3, -4]							,       0  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   '''cpu'''

								__magic_name__		:Tuple   =   self.get_dummy_components()
								__magic_name__		:int   =   self.pipeline_class(**__lowerCAmelCase  )
								pipe.to(__lowerCAmelCase  )
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

								__magic_name__		:Optional[Any]   =   self.get_dummy_inversion_inputs(__lowerCAmelCase  )
								__magic_name__		:str   =   pipe.invert(**__lowerCAmelCase  ).images
								__magic_name__		:List[Any]   =   image[0, -1, -3:, -3:]

								self.assertEqual(image.shape							,       (2, 3_2, 3_2, 3)  )
								__magic_name__		:Any   =   np.array(
								    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]							,       )
								__magic_name__		:List[Any]   =   np.abs(image_slice.flatten() - expected_slice  ).max()
								self.assertLessEqual(__lowerCAmelCase							,       1E-3  )







				def       A						(    self  ):
								"""simple docstring"""
								super().test_inference_batch_single_identical(expected_max_diff=5E-3  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   '''cpu'''

								__magic_name__		:Optional[int]   =   self.get_dummy_components()

								__magic_name__		:int   =   {'''beta_start''': 0.00085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
								__magic_name__		:Dict   =   DPMSolverMultistepScheduler(**__lowerCAmelCase  )
								__magic_name__		:List[str]   =   DPMSolverMultistepInverseScheduler(**__lowerCAmelCase  )

								__magic_name__		:Union[str, Any]   =   self.pipeline_class(**__lowerCAmelCase  )
								pipe.to(__lowerCAmelCase  )
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

								__magic_name__		:List[str]   =   self.get_dummy_inversion_inputs(__lowerCAmelCase  )
								__magic_name__		:Dict   =   pipe.invert(**__lowerCAmelCase  ).images
								__magic_name__		:List[str]   =   image[0, -1, -3:, -3:]

								self.assertEqual(image.shape							,       (2, 3_2, 3_2, 3)  )
								__magic_name__		:List[str]   =   np.array(
								    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]							,       )
								__magic_name__		:List[str]   =   np.abs(image_slice.flatten() - expected_slice  ).max()
								self.assertLessEqual(__lowerCAmelCase							,       1E-3  )







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







				def       A						(    self  ):
								"""simple docstring"""
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







				@classmethod
				def       A						(    cls  ):
								"""simple docstring"""
								__magic_name__		:Any   =   load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''  )

								__magic_name__		:Optional[Any]   =   raw_image.convert('''RGB'''  ).resize((7_6_8, 7_6_8)  )

								__magic_name__		:Optional[Any]   =   raw_image







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   torch.manual_seed(0  )

								__magic_name__		:Dict   =   StableDiffusionDiffEditPipeline.from_pretrained(
								    '''stabilityai/stable-diffusion-2-1'''							,       safety_checker=__lowerCAmelCase							,       torch_dtype=torch.floataa  )
								__magic_name__		:Tuple   =   DDIMScheduler.from_config(pipe.scheduler.config  )
								__magic_name__		:str   =   DDIMInverseScheduler.from_config(pipe.scheduler.config  )
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

								__magic_name__		:Optional[Any]   =   '''a bowl of fruit'''
								__magic_name__		:Dict   =   '''a bowl of pears'''

								__magic_name__		:Dict   =   pipe.generate_mask(
								    image=self.raw_image							,       source_prompt=__lowerCAmelCase							,       target_prompt=__lowerCAmelCase							,       generator=__lowerCAmelCase							,       )

								__magic_name__		:Optional[int]   =   pipe.invert(
								    prompt=__lowerCAmelCase							,       image=self.raw_image							,       inpaint_strength=0.7							,       generator=__lowerCAmelCase  ).latents

								__magic_name__		:List[Any]   =   pipe(
								    prompt=__lowerCAmelCase							,       mask_image=__lowerCAmelCase							,       image_latents=__lowerCAmelCase							,       generator=__lowerCAmelCase							,       negative_prompt=__lowerCAmelCase							,       inpaint_strength=0.7							,       output_type='''numpy'''							,       ).images[0]

								__magic_name__		:str   =   (
								    np.array(
								        load_image(
								            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								            '''/diffedit/pears.png'''  ).resize((7_6_8, 7_6_8)  )  )
								    / 2_5_5
								)
								assert np.abs((expected_image - image).max()  ) < 5E-1







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   torch.manual_seed(0  )

								__magic_name__		:Dict   =   StableDiffusionDiffEditPipeline.from_pretrained(
								    '''stabilityai/stable-diffusion-2-1'''							,       safety_checker=__lowerCAmelCase							,       torch_dtype=torch.floataa  )
								__magic_name__		:Optional[int]   =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
								__magic_name__		:Any   =   DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config  )
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

								__magic_name__		:str   =   '''a bowl of fruit'''
								__magic_name__		:Any   =   '''a bowl of pears'''

								__magic_name__		:List[Any]   =   pipe.generate_mask(
								    image=self.raw_image							,       source_prompt=__lowerCAmelCase							,       target_prompt=__lowerCAmelCase							,       generator=__lowerCAmelCase							,       )

								__magic_name__		:int   =   pipe.invert(
								    prompt=__lowerCAmelCase							,       image=self.raw_image							,       inpaint_strength=0.7							,       generator=__lowerCAmelCase							,       num_inference_steps=2_5							,       ).latents

								__magic_name__		:List[Any]   =   pipe(
								    prompt=__lowerCAmelCase							,       mask_image=__lowerCAmelCase							,       image_latents=__lowerCAmelCase							,       generator=__lowerCAmelCase							,       negative_prompt=__lowerCAmelCase							,       inpaint_strength=0.7							,       num_inference_steps=2_5							,       output_type='''numpy'''							,       ).images[0]

								__magic_name__		:List[str]   =   (
								    np.array(
								        load_image(
								            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								            '''/diffedit/pears.png'''  ).resize((7_6_8, 7_6_8)  )  )
								    / 2_5_5
								)
								assert np.abs((expected_image - image).max()  ) < 5E-1












import unittest

from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__magic_name__  :  Optional[int]					=       get_tests_dir("""fixtures/spiece.model""")





@require_sentencepiece
@require_tokenizers
class     SCREAMING_SNAKE_CASE__		(_a    ,					unittest.TestCase			):
     lowercase_ :      Optional[Any]				= DebertaVaTokenizer
     lowercase_ :      Any				= DebertaVaTokenizerFast
     lowercase_ :      List[str]				= True
     lowercase_ :      str				= True
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       super().setUp()

       # We have a SentencePiece fixture for testing
       lowerCAmelCase__										=      DebertaVaTokenizer(__lowerCamelCase       ,						unk_token='''<unk>'''						)
       tokenizer.save_pretrained(self.tmpdirname						)
     def        A__      (						self	:		Dict       ,						__lowerCamelCase	:		Any						):



       """simple docstring"""
       lowerCAmelCase__										=      '''this is a test'''
       lowerCAmelCase__										=      '''this is a test'''
       return input_text, output_text
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       lowerCAmelCase__										=      '''<pad>'''
       lowerCAmelCase__										=      0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase						)       ,						__lowerCamelCase						)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase						)       ,						__lowerCamelCase						)
     def        A__      (						self	:		Any						):



       """simple docstring"""
       lowerCAmelCase__										=      list(self.get_tokenizer().get_vocab().keys()						)
       self.assertEqual(vocab_keys[0]       ,						'''<pad>'''						)
       self.assertEqual(vocab_keys[1]       ,						'''<unk>'''						)
       self.assertEqual(vocab_keys[-1]       ,						'''[PAD]'''						)
       self.assertEqual(len(__lowerCamelCase						)       ,						3_00_01						)
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       self.assertEqual(self.get_tokenizer().vocab_size       ,						3_00_00						)
     def        A__      (						self	:		str						):



       """simple docstring"""
       # fmt: off
       lowerCAmelCase__										=      ''' \tHeLLo!how  \n Are yoU?  '''
       lowerCAmelCase__										=      ['''▁hello''', '''!''', '''how''', '''▁are''', '''▁you''', '''?''']
       # fmt: on

       lowerCAmelCase__										=      DebertaVaTokenizer(__lowerCamelCase       ,						do_lower_case=__lowerCamelCase						)
       lowerCAmelCase__										=      tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)						)

       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)

       lowerCAmelCase__										=      DebertaVaTokenizerFast(__lowerCamelCase       ,						do_lower_case=__lowerCamelCase						)
       lowerCAmelCase__										=      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)						)

       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
     @unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.'''						)
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       pass
     @unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.'''						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       pass
     def        A__      (						self	:		str						):



       """simple docstring"""
       # fmt: off
       lowerCAmelCase__										=      '''I was born in 92000, and this is falsé.'''
       lowerCAmelCase__										=      ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
       # fmt: on

       lowerCAmelCase__										=      DebertaVaTokenizer(__lowerCamelCase       ,						split_by_punct=__lowerCamelCase						)
       lowerCAmelCase__										=      tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)						)

       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)

       lowerCAmelCase__										=      DebertaVaTokenizerFast(__lowerCamelCase       ,						split_by_punct=__lowerCamelCase						)
       lowerCAmelCase__										=      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)						)

       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       # fmt: off
       lowerCAmelCase__										=      '''I was born in 92000, and this is falsé.'''
       lowerCAmelCase__										=      ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
       # fmt: on

       lowerCAmelCase__										=      DebertaVaTokenizer(__lowerCamelCase       ,						do_lower_case=__lowerCamelCase       ,						split_by_punct=__lowerCamelCase						)
       lowerCAmelCase__										=      tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)

       lowerCAmelCase__										=      DebertaVaTokenizerFast(__lowerCamelCase       ,						do_lower_case=__lowerCamelCase       ,						split_by_punct=__lowerCamelCase						)
       lowerCAmelCase__										=      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       # fmt: off
       lowerCAmelCase__										=      '''I was born in 92000, and this is falsé.'''
       lowerCAmelCase__										=      ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
       # fmt: on

       lowerCAmelCase__										=      DebertaVaTokenizer(__lowerCamelCase       ,						do_lower_case=__lowerCamelCase       ,						split_by_punct=__lowerCamelCase						)
       lowerCAmelCase__										=      tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)						)

       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)

       lowerCAmelCase__										=      DebertaVaTokenizerFast(__lowerCamelCase       ,						do_lower_case=__lowerCamelCase       ,						split_by_punct=__lowerCamelCase						)
       lowerCAmelCase__										=      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)						)

       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       # fmt: off
       lowerCAmelCase__										=      '''I was born in 92000, and this is falsé.'''
       lowerCAmelCase__										=      ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
       # fmt: on

       lowerCAmelCase__										=      DebertaVaTokenizer(__lowerCamelCase       ,						do_lower_case=__lowerCamelCase       ,						split_by_punct=__lowerCamelCase						)
       lowerCAmelCase__										=      tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)						)

       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)

       lowerCAmelCase__										=      DebertaVaTokenizerFast(__lowerCamelCase       ,						do_lower_case=__lowerCamelCase       ,						split_by_punct=__lowerCamelCase						)
       lowerCAmelCase__										=      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)						)

       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       # fmt: off
       lowerCAmelCase__										=      ''' \tHeLLo!how  \n Are yoU?  '''
       lowerCAmelCase__										=      ['''▁''', '''<unk>''', '''e''', '''<unk>''', '''o''', '''!''', '''how''', '''▁''', '''<unk>''', '''re''', '''▁yo''', '''<unk>''', '''?''']
       # fmt: on

       lowerCAmelCase__										=      DebertaVaTokenizer(__lowerCamelCase       ,						do_lower_case=__lowerCamelCase       ,						split_by_punct=__lowerCamelCase						)
       lowerCAmelCase__										=      tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)						)

       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)

       lowerCAmelCase__										=      DebertaVaTokenizerFast(__lowerCamelCase       ,						do_lower_case=__lowerCamelCase       ,						split_by_punct=__lowerCamelCase						)
       lowerCAmelCase__										=      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)						)

       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
     def        A__      (						self	:		List[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.get_tokenizer()
       lowerCAmelCase__										=      self.get_rust_tokenizer()

       lowerCAmelCase__										=      '''I was born in 92000, and this is falsé.'''

       lowerCAmelCase__										=      tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)						)
       lowerCAmelCase__										=      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)

       lowerCAmelCase__										=      tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)
       lowerCAmelCase__										=      rust_tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)

       lowerCAmelCase__										=      self.get_rust_tokenizer()
       lowerCAmelCase__										=      tokenizer.encode(__lowerCamelCase						)
       lowerCAmelCase__										=      rust_tokenizer.encode(__lowerCamelCase						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
     def        A__      (						self	:		Optional[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      '''This is a test'''
       lowerCAmelCase__										=      [13, 1, 43_98, 25, 21, 12_89]
       lowerCAmelCase__										=      ['''▁''', '''T''', '''his''', '''▁is''', '''▁a''', '''▁test''']
       lowerCAmelCase__										=      ['''▁''', '''<unk>''', '''his''', '''▁is''', '''▁a''', '''▁test''']

       lowerCAmelCase__										=      DebertaVaTokenizer(__lowerCamelCase       ,						keep_accents=__lowerCamelCase						)
       lowerCAmelCase__										=      DebertaVaTokenizerFast(__lowerCamelCase       ,						keep_accents=__lowerCamelCase						)

       lowerCAmelCase__										=      tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
       lowerCAmelCase__										=      tokenizer.tokenize(__lowerCamelCase						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
       lowerCAmelCase__										=      tokenizer.convert_ids_to_tokens(__lowerCamelCase						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)

       lowerCAmelCase__										=      rust_tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
       lowerCAmelCase__										=      rust_tokenizer.tokenize(__lowerCamelCase						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
       lowerCAmelCase__										=      rust_tokenizer.convert_ids_to_tokens(__lowerCamelCase						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)

       # fmt: off
       lowerCAmelCase__										=      '''I was born in 92000, and this is falsé.'''
       lowerCAmelCase__										=      [13, 1, 23, 3_86, 19, 5_61, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]
       lowerCAmelCase__										=      ['''▁''', '''I''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''', ]
       lowerCAmelCase__										=      ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
       # fmt: on

       lowerCAmelCase__										=      tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
       lowerCAmelCase__										=      tokenizer.tokenize(__lowerCamelCase						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
       lowerCAmelCase__										=      tokenizer.convert_ids_to_tokens(__lowerCamelCase						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)

       lowerCAmelCase__										=      rust_tokenizer.encode(__lowerCamelCase       ,						add_special_tokens=__lowerCamelCase						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
       lowerCAmelCase__										=      rust_tokenizer.tokenize(__lowerCamelCase						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
       lowerCAmelCase__										=      rust_tokenizer.convert_ids_to_tokens(__lowerCamelCase						)
       self.assertListEqual(__lowerCamelCase       ,						__lowerCamelCase						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       lowerCAmelCase__										=      DebertaVaTokenizer(__lowerCamelCase						)

       lowerCAmelCase__										=      tokenizer.encode('''sequence builders'''						)
       lowerCAmelCase__										=      tokenizer.encode('''multi-sequence build'''						)

       lowerCAmelCase__										=      tokenizer.build_inputs_with_special_tokens(__lowerCamelCase						)
       lowerCAmelCase__										=      tokenizer.build_inputs_with_special_tokens(__lowerCamelCase       ,						__lowerCamelCase						)

       self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]       ,						__lowerCamelCase						)
       self.assertEqual(
           [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id]       ,						__lowerCamelCase       ,						)



     @slow
     def        A__      (						self	:		str						):



       """simple docstring"""
       # fmt: off
       lowerCAmelCase__										=      {'''input_ids''': [[1, 3_98_67, 36, 1_93_90, 4_86, 27, 3_50_52, 8_14_36, 18, 6_06_85, 12_25, 7, 3_50_52, 8_14_36, 18, 93_67, 1_68_99, 18, 1_59_37, 53, 5_94, 7_73, 18, 1_62_87, 3_04_65, 36, 1_59_37, 6, 4_11_39, 38, 3_69_79, 6_07_63, 1_91, 6, 3_41_32, 99, 6, 5_05_38, 3_90, 4_32_30, 6, 3_41_32, 27_79, 2_08_50, 14, 6_99, 10_72, 11_94, 36, 3_82, 1_09_01, 53, 7, 6_99, 10_72, 20_84, 36, 2_04_22, 6_30, 53, 19, 1_05, 30_49, 18_96, 10_53, 1_68_99, 15_06, 11, 3_79_78, 42_43, 7, 12_37, 3_18_69, 2_00, 1_65_66, 6_54, 6, 3_50_52, 8_14_36, 7, 5_56_30, 1_35_93, 4, 2], [1, 26, 1_50_11, 13, 6_67, 8, 10_53, 18, 2_36_11, 12_37, 7_23_56, 1_28_20, 34, 10_41_34, 12_09, 35, 1_33_13, 66_27, 21, 2_02, 3_47, 7, 1_64, 23_99, 11, 46, 44_85, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 12_32, 28_64, 1_57_85, 1_49_51, 1_05, 5, 85_81, 12_50, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=__lowerCamelCase       ,						model_name='''microsoft/deberta-v2-xlarge'''       ,						revision='''ad6e42c1532ddf3a15c39246b63f5559d558b670'''       ,						)

