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

									"""simple docstring"""


									torch.manual_seed(0      )
									lowerCamelCase					:			int									= 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=A,    )
									lowerCamelCase					:			Optional[Any]									= DDIMScheduler(
									    beta_start=0.0_0085,    beta_end=0.012,    beta_schedule='scaled_linear',    clip_sample=A,    set_alpha_to_one=A,    )
									lowerCamelCase					:			List[Any]									= DDIMInverseScheduler(
									    beta_start=0.0_0085,    beta_end=0.012,    beta_schedule='scaled_linear',    clip_sample=A,    set_alpha_to_zero=A,    )
									torch.manual_seed(0      )
									lowerCamelCase					:			Dict									= 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					:			Optional[Any]									= 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=1000,    hidden_act='gelu',    projection_dim=512,    )
									lowerCamelCase					:			List[Any]									= CLIPTextModel(A      )
									lowerCamelCase					:			Optional[int]									= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

									lowerCamelCase					:			str									= {
									    '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,    A,    A=0      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= floats_tensor((1, 16, 16),    rng=random.Random(A      )      ).to(A      )
									lowerCamelCase					:			int									= floats_tensor((1, 2, 4, 16, 16),    rng=random.Random(A      )      ).to(A      )
									if str(A      ).startswith('mps'      ):
													lowerCamelCase					:			int									= torch.manual_seed(A      )
									else:
													lowerCamelCase					:			Dict									= torch.Generator(device=A      ).manual_seed(A      )
									lowerCamelCase					:			Optional[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     UpperCAmelCase_							(     self,    A,    A=0      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= floats_tensor((1, 3, 32, 32),    rng=random.Random(A      )      ).to(A      )
									lowerCamelCase					:			List[Any]									= image.cpu().permute(0,    2,    3,    1      )[0]
									lowerCamelCase					:			int									= Image.fromarray(np.uinta(A      )      ).convert('RGB'      )
									if str(A      ).startswith('mps'      ):
													lowerCamelCase					:			Union[str, Any]									= torch.manual_seed(A      )
									else:
													lowerCamelCase					:			Dict									= torch.Generator(device=A      ).manual_seed(A      )
									lowerCamelCase					:			int									= {
									    '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,    A,    A=0      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= floats_tensor((1, 3, 32, 32),    rng=random.Random(A      )      ).to(A      )
									lowerCamelCase					:			Union[str, Any]									= image.cpu().permute(0,    2,    3,    1      )[0]
									lowerCamelCase					:			Dict									= Image.fromarray(np.uinta(A      )      ).convert('RGB'      )
									if str(A      ).startswith('mps'      ):
													lowerCamelCase					:			Optional[int]									= torch.manual_seed(A      )
									else:
													lowerCamelCase					:			Optional[int]									= torch.Generator(device=A      ).manual_seed(A      )
									lowerCamelCase					:			List[Any]									= {
									    '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      ):

									"""simple docstring"""


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

									lowerCamelCase					:			Tuple									= self.get_dummy_components()
									lowerCamelCase					:			List[str]									= self.pipeline_class(**A      )
									pipe.to(A      )
									pipe.set_progress_bar_config(disable=A      )

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

									lowerCamelCase					:			Tuple									= self.get_dummy_inputs(A      )
									lowerCamelCase					:			List[str]									= pipe(**A      )[0]

									with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(A      )
													lowerCamelCase					:			Tuple									= self.pipeline_class.from_pretrained(A      )
													pipe_loaded.to(A      )
													pipe_loaded.set_progress_bar_config(disable=A      )

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

									lowerCamelCase					:			Optional[Any]									= self.get_dummy_inputs(A      )
									lowerCamelCase					:			str									= pipe_loaded(**A      )[0]

									lowerCamelCase					:			int									= np.abs(output - output_loaded      ).max()
									self.assertLess(A,    1e-4      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= 'cpu'

									lowerCamelCase					:			List[str]									= self.get_dummy_components()
									lowerCamelCase					:			Any									= self.pipeline_class(**A      )
									pipe.to(A      )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Tuple									= self.get_dummy_mask_inputs(A      )
									lowerCamelCase					:			List[str]									= pipe.generate_mask(**A      )
									lowerCamelCase					:			int									= mask[0, -3:, -3:]

									self.assertEqual(mask.shape,    (1, 16, 16)      )
									lowerCamelCase					:			Any									= np.array([0] * 9      )
									lowerCamelCase					:			str									= np.abs(mask_slice.flatten() - expected_slice      ).max()
									self.assertLessEqual(A,    1e-3      )
									self.assertEqual(mask[0, -3, -4],    0      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= 'cpu'

									lowerCamelCase					:			Any									= self.get_dummy_components()
									lowerCamelCase					:			Optional[int]									= self.pipeline_class(**A      )
									pipe.to(A      )
									pipe.set_progress_bar_config(disable=A      )

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

									self.assertEqual(image.shape,    (2, 32, 32, 3)      )
									lowerCamelCase					:			Dict									= np.array(
									    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799],    )
									lowerCamelCase					:			List[str]									= np.abs(image_slice.flatten() - expected_slice      ).max()
									self.assertLessEqual(A,    1e-3      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									super().test_inference_batch_single_identical(expected_max_diff=5e-3      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= 'cpu'

									lowerCamelCase					:			Union[str, Any]									= self.get_dummy_components()

									lowerCamelCase					:			Dict									= {'beta_start': 0.0_0085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
									lowerCamelCase					:			int									= DPMSolverMultistepScheduler(**A      )
									lowerCamelCase					:			Optional[int]									= DPMSolverMultistepInverseScheduler(**A      )

									lowerCamelCase					:			Any									= self.pipeline_class(**A      )
									pipe.to(A      )
									pipe.set_progress_bar_config(disable=A      )

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

									self.assertEqual(image.shape,    (2, 32, 32, 3)      )
									lowerCamelCase					:			Any									= np.array(
									    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799],    )
									lowerCamelCase					:			Optional[int]									= np.abs(image_slice.flatten() - expected_slice      ).max()
									self.assertLessEqual(A,    1e-3      )





@require_torch_gpu
@slow
class     __snake_case		(   unittest.TestCase):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()
					@classmethod
					def     UpperCAmelCase_							(     cls      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'      )

									lowerCamelCase					:			List[str]									= raw_image.convert('RGB'      ).resize((768, 768)      )

									lowerCamelCase					:			Optional[Any]									= raw_image
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= torch.manual_seed(0      )

									lowerCamelCase					:			List[Any]									= StableDiffusionDiffEditPipeline.from_pretrained(
									    'stabilityai/stable-diffusion-2-1',    safety_checker=A,    torch_dtype=torch.floataa      )
									lowerCamelCase					:			int									= DDIMScheduler.from_config(pipe.scheduler.config      )
									lowerCamelCase					:			Dict									= DDIMInverseScheduler.from_config(pipe.scheduler.config      )
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Tuple									= 'a bowl of fruit'
									lowerCamelCase					:			int									= 'a bowl of pears'

									lowerCamelCase					:			Optional[int]									= pipe.generate_mask(
									    image=self.raw_image,    source_prompt=A,    target_prompt=A,    generator=A,    )

									lowerCamelCase					:			Any									= pipe.invert(
									    prompt=A,    image=self.raw_image,    inpaint_strength=0.7,    generator=A      ).latents

									lowerCamelCase					:			Optional[Any]									= pipe(
									    prompt=A,    mask_image=A,    image_latents=A,    generator=A,    negative_prompt=A,    inpaint_strength=0.7,    output_type='numpy',    ).images[0]

									lowerCamelCase					:			Optional[Any]									= (
									    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      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= torch.manual_seed(0      )

									lowerCamelCase					:			Optional[Any]									= StableDiffusionDiffEditPipeline.from_pretrained(
									    'stabilityai/stable-diffusion-2-1',    safety_checker=A,    torch_dtype=torch.floataa      )
									lowerCamelCase					:			List[str]									= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
									lowerCamelCase					:			Union[str, Any]									= DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config      )
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Optional[Any]									= 'a bowl of fruit'
									lowerCamelCase					:			int									= 'a bowl of pears'

									lowerCamelCase					:			Optional[int]									= pipe.generate_mask(
									    image=self.raw_image,    source_prompt=A,    target_prompt=A,    generator=A,    )

									lowerCamelCase					:			int									= pipe.invert(
									    prompt=A,    image=self.raw_image,    inpaint_strength=0.7,    generator=A,    num_inference_steps=25,    ).latents

									lowerCamelCase					:			Tuple									= pipe(
									    prompt=A,    mask_image=A,    image_latents=A,    generator=A,    negative_prompt=A,    inpaint_strength=0.7,    num_inference_steps=25,    output_type='numpy',    ).images[0]

									lowerCamelCase					:			str									= (
									    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 os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english




@require_tokenizers
class     __snake_case		(   a__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	BertTokenizer
					_lowerCAmelCase			     =	BertTokenizerFast
					_lowerCAmelCase			     =	True
					_lowerCAmelCase			     =	True
					_lowerCAmelCase			     =	filter_non_english
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									super().setUp()

									lowerCamelCase					:			str									= [
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    '[PAD]',
									    '[MASK]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									lowerCamelCase					:			Any									= os.path.join(self.tmpdirname,    VOCAB_FILES_NAMES['vocab_file']      )
									with open(self.vocab_file,    'w',    encoding='utf-8'      ) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= 'UNwant\u00E9d,running'
									lowerCamelCase					:			List[str]									= 'unwanted, running'
									return input_text, output_text
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.tokenizer_class(self.vocab_file      )

									lowerCamelCase					:			List[str]									= tokenizer.tokenize('UNwant\u00E9d,running'      )
									self.assertListEqual(A,    ['un', '##want', '##ed', ',', 'runn', '##ing']      )
									self.assertListEqual(tokenizer.convert_tokens_to_ids(A      ),    [9, 6, 7, 12, 10, 11]      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									if not self.test_rust_tokenizer:
													return

									lowerCamelCase					:			Dict									= self.get_tokenizer()
									lowerCamelCase					:			Optional[Any]									= self.get_rust_tokenizer()

									lowerCamelCase					:			Dict									= 'UNwant\u00E9d,running'

									lowerCamelCase					:			Tuple									= tokenizer.tokenize(A      )
									lowerCamelCase					:			Optional[int]									= rust_tokenizer.tokenize(A      )
									self.assertListEqual(A,    A      )

									lowerCamelCase					:			Optional[Any]									= tokenizer.encode(A,    add_special_tokens=A      )
									lowerCamelCase					:			List[Any]									= rust_tokenizer.encode(A,    add_special_tokens=A      )
									self.assertListEqual(A,    A      )

									lowerCamelCase					:			Optional[Any]									= self.get_rust_tokenizer()
									lowerCamelCase					:			Optional[Any]									= tokenizer.encode(A      )
									lowerCamelCase					:			Tuple									= rust_tokenizer.encode(A      )
									self.assertListEqual(A,    A      )

									# With lower casing
									lowerCamelCase					:			Union[str, Any]									= self.get_tokenizer(do_lower_case=A      )
									lowerCamelCase					:			List[Any]									= self.get_rust_tokenizer(do_lower_case=A      )

									lowerCamelCase					:			Optional[int]									= 'UNwant\u00E9d,running'

									lowerCamelCase					:			Optional[Any]									= tokenizer.tokenize(A      )
									lowerCamelCase					:			str									= rust_tokenizer.tokenize(A      )
									self.assertListEqual(A,    A      )

									lowerCamelCase					:			Optional[Any]									= tokenizer.encode(A,    add_special_tokens=A      )
									lowerCamelCase					:			Union[str, Any]									= rust_tokenizer.encode(A,    add_special_tokens=A      )
									self.assertListEqual(A,    A      )

									lowerCamelCase					:			Optional[Any]									= self.get_rust_tokenizer()
									lowerCamelCase					:			Any									= tokenizer.encode(A      )
									lowerCamelCase					:			Dict									= rust_tokenizer.encode(A      )
									self.assertListEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= BasicTokenizer()

									self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'      ),    ['ah', '\u535A', '\u63A8', 'zz']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= BasicTokenizer(do_lower_case=A      )

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '      ),    ['hello', '!', 'how', 'are', 'you', '?']      )
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      ),    ['hello']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= BasicTokenizer(do_lower_case=A,    strip_accents=A      )

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      ),    ['hällo', '!', 'how', 'are', 'you', '?']      )
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      ),    ['h\u00E9llo']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= BasicTokenizer(do_lower_case=A,    strip_accents=A      )

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      ),    ['hallo', '!', 'how', 'are', 'you', '?']      )
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      ),    ['hello']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= BasicTokenizer(do_lower_case=A      )

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      ),    ['hallo', '!', 'how', 'are', 'you', '?']      )
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      ),    ['hello']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= BasicTokenizer(do_lower_case=A      )

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '      ),    ['HeLLo', '!', 'how', 'Are', 'yoU', '?']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= BasicTokenizer(do_lower_case=A,    strip_accents=A      )

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      ),    ['HäLLo', '!', 'how', 'Are', 'yoU', '?']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= BasicTokenizer(do_lower_case=A,    strip_accents=A      )

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      ),    ['HaLLo', '!', 'how', 'Are', 'yoU', '?']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= BasicTokenizer(do_lower_case=A,    never_split=['[UNK]']      )

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'      ),    ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= BasicTokenizer()
									lowerCamelCase					:			Optional[Any]									= 'a\n\'ll !!to?\'d of, can\'t.'
									lowerCamelCase					:			int									= ['a', '\'', 'll', '!', '!', 'to', '?', '\'', 'd', 'of', ',', 'can', '\'', 't', '.']
									self.assertListEqual(tokenizer.tokenize(A      ),    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

									lowerCamelCase					:			Optional[int]									= {}
									for i, token in enumerate(A      ):
													lowerCamelCase					:			int									= i
									lowerCamelCase					:			List[Any]									= WordpieceTokenizer(vocab=A,    unk_token='[UNK]'      )

									self.assertListEqual(tokenizer.tokenize(''      ),    []      )

									self.assertListEqual(tokenizer.tokenize('unwanted running'      ),    ['un', '##want', '##ed', 'runn', '##ing']      )

									self.assertListEqual(tokenizer.tokenize('unwantedX running'      ),    ['[UNK]', 'runn', '##ing']      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.assertTrue(_is_whitespace(' '      )      )
									self.assertTrue(_is_whitespace('\t'      )      )
									self.assertTrue(_is_whitespace('\r'      )      )
									self.assertTrue(_is_whitespace('\n'      )      )
									self.assertTrue(_is_whitespace('\u00A0'      )      )

									self.assertFalse(_is_whitespace('A'      )      )
									self.assertFalse(_is_whitespace('-'      )      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.assertTrue(_is_control('\u0005'      )      )

									self.assertFalse(_is_control('A'      )      )
									self.assertFalse(_is_control(' '      )      )
									self.assertFalse(_is_control('\t'      )      )
									self.assertFalse(_is_control('\r'      )      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.assertTrue(_is_punctuation('-'      )      )
									self.assertTrue(_is_punctuation('$'      )      )
									self.assertTrue(_is_punctuation('`'      )      )
									self.assertTrue(_is_punctuation('.'      )      )

									self.assertFalse(_is_punctuation('A'      )      )
									self.assertFalse(_is_punctuation(' '      )      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.get_tokenizer()
									lowerCamelCase					:			Optional[int]									= self.get_rust_tokenizer()

									# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
									self.assertListEqual([tokenizer.tokenize(A      ) for t in ['Test', '\xad', 'test']],    [['[UNK]'], [], ['[UNK]']]      )

									self.assertListEqual(
									    [rust_tokenizer.tokenize(A      ) for t in ['Test', '\xad', 'test']],    [['[UNK]'], [], ['[UNK]']]      )
					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.tokenizer_class.from_pretrained('bert-base-uncased'      )

									lowerCamelCase					:			Any									= tokenizer.encode('sequence builders',    add_special_tokens=A      )
									lowerCamelCase					:			Tuple									= tokenizer.encode('multi-sequence build',    add_special_tokens=A      )

									lowerCamelCase					:			List[Any]									= tokenizer.build_inputs_with_special_tokens(A      )
									lowerCamelCase					:			Optional[int]									= tokenizer.build_inputs_with_special_tokens(A,    A      )

									assert encoded_sentence == [101] + text + [102]
									assert encoded_pair == [101] + text + [102] + text_a + [102]
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																	lowerCamelCase					:			Optional[Any]									= self.rust_tokenizer_class.from_pretrained(A,    **A      )

																	lowerCamelCase					:			Optional[int]									= F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
																	lowerCamelCase					:			List[str]									= tokenizer_r.encode_plus(
																	    A,    return_attention_mask=A,    return_token_type_ids=A,    return_offsets_mapping=A,    add_special_tokens=A,    )

																	lowerCamelCase					:			int									= tokenizer_r.do_lower_case if hasattr(A,    'do_lower_case'      ) else False
																	lowerCamelCase					:			Union[str, Any]									= (
																	    [
																	        ((0, 0), tokenizer_r.cls_token),
																	        ((0, 1), 'A'),
																	        ((1, 2), ','),
																	        ((3, 5), 'na'),
																	        ((5, 6), '##ï'),
																	        ((6, 8), '##ve'),
																	        ((9, 15), tokenizer_r.mask_token),
																	        ((16, 21), 'Allen'),
																	        ((21, 23), '##NL'),
																	        ((23, 24), '##P'),
																	        ((25, 33), 'sentence'),
																	        ((33, 34), '.'),
																	        ((0, 0), tokenizer_r.sep_token),
																	    ]
																	    if not do_lower_case
																	    else [
																	        ((0, 0), tokenizer_r.cls_token),
																	        ((0, 1), 'a'),
																	        ((1, 2), ','),
																	        ((3, 8), 'naive'),
																	        ((9, 15), tokenizer_r.mask_token),
																	        ((16, 21), 'allen'),
																	        ((21, 23), '##nl'),
																	        ((23, 24), '##p'),
																	        ((25, 33), 'sentence'),
																	        ((33, 34), '.'),
																	        ((0, 0), tokenizer_r.sep_token),
																	    ]
																	)

																	self.assertEqual(
																	    [e[1] for e in expected_results],    tokenizer_r.convert_ids_to_tokens(tokens['input_ids']      )      )
																	self.assertEqual([e[0] for e in expected_results],    tokens['offset_mapping']      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= ['的', '人', '有']
									lowerCamelCase					:			List[str]									= ''.join(A      )
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																	lowerCamelCase					:			Dict									= True
																	lowerCamelCase					:			Tuple									= self.tokenizer_class.from_pretrained(A,    **A      )
																	lowerCamelCase					:			int									= self.rust_tokenizer_class.from_pretrained(A,    **A      )

																	lowerCamelCase					:			Union[str, Any]									= tokenizer_p.encode(A,    add_special_tokens=A      )
																	lowerCamelCase					:			Dict									= tokenizer_r.encode(A,    add_special_tokens=A      )

																	lowerCamelCase					:			Optional[Any]									= tokenizer_r.convert_ids_to_tokens(A      )
																	lowerCamelCase					:			str									= tokenizer_p.convert_ids_to_tokens(A      )

																	# it is expected that each Chinese character is not preceded by "##"
																	self.assertListEqual(A,    A      )
																	self.assertListEqual(A,    A      )

																	lowerCamelCase					:			int									= False
																	lowerCamelCase					:			int									= self.rust_tokenizer_class.from_pretrained(A,    **A      )
																	lowerCamelCase					:			Tuple									= self.tokenizer_class.from_pretrained(A,    **A      )

																	lowerCamelCase					:			int									= tokenizer_r.encode(A,    add_special_tokens=A      )
																	lowerCamelCase					:			List[str]									= tokenizer_p.encode(A,    add_special_tokens=A      )

																	lowerCamelCase					:			Optional[Any]									= tokenizer_r.convert_ids_to_tokens(A      )
																	lowerCamelCase					:			Tuple									= tokenizer_p.convert_ids_to_tokens(A      )

																	# it is expected that only the first Chinese character is not preceded by "##".
																	lowerCamelCase					:			Tuple									= [
																	    F'''##{token}''' if idx != 0 else token for idx, token in enumerate(A      )
																	]
																	self.assertListEqual(A,    A      )
																	self.assertListEqual(A,    A      )
