



import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					UnCLIPImageVariationPipeline
			UpperCamelCase        =					IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
			UpperCamelCase        =					IMAGE_VARIATION_BATCH_PARAMS

			UpperCamelCase        =					[
			    '''generator''',
			    '''return_dict''',
			    '''decoder_num_inference_steps''',
			    '''super_res_num_inference_steps''',
			]
			UpperCamelCase        =					False



			@property
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   List[Any]:



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   str:



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  List[Any])       ->   int:



						"""simple docstring"""






						return self.time_input_dim



			@property
			def    _lowerCamelCase	(						self       :  List[str])       ->   Any:



						"""simple docstring"""






						return self.time_input_dim * 4



			@property
			def    _lowerCamelCase	(						self       :  Dict)       ->   int:



						"""simple docstring"""






						return 1_00



			@property
			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
						return tokenizer



			@property
			def    _lowerCamelCase	(						self       :  Tuple)       ->   Any:



						"""simple docstring"""






						torch.manual_seed(0)
						_UpperCAmelCase			=       CLIPTextConfig(
						    bos_token_id=0			,		eos_token_id=2			,		hidden_size=self.text_embedder_hidden_size			,		projection_dim=self.text_embedder_hidden_size			,		intermediate_size=37			,		layer_norm_eps=1E-05			,		num_attention_heads=4			,		num_hidden_layers=5			,		pad_token_id=1			,		vocab_size=10_00			,		)
						return CLIPTextModelWithProjection(A)



			@property
			def    _lowerCamelCase	(						self       :  int)       ->   List[str]:



						"""simple docstring"""






						torch.manual_seed(0)
						_UpperCAmelCase			=       CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size			,		projection_dim=self.text_embedder_hidden_size			,		num_hidden_layers=5			,		num_attention_heads=4			,		image_size=32			,		intermediate_size=37			,		patch_size=1			,		)
						return CLIPVisionModelWithProjection(A)



			@property
			def    _lowerCamelCase	(						self       :  Any)       ->   Any:



						"""simple docstring"""






						torch.manual_seed(0)

						_UpperCAmelCase			=       {
						    'clip_embeddings_dim': self.text_embedder_hidden_size,
						    'time_embed_dim': self.time_embed_dim,
						    'cross_attention_dim': self.cross_attention_dim,
						}

						_UpperCAmelCase			=       UnCLIPTextProjModel(**A)
						return model



			@property
			def    _lowerCamelCase	(						self       :  int)       ->   Optional[Any]:



						"""simple docstring"""






						torch.manual_seed(0)

						_UpperCAmelCase			=       {
						    'sample_size': 32,
						    # RGB in channels
						    'in_channels': 3,
						    # Out channels is double in channels because predicts mean and variance
						    'out_channels': 6,
						    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
						    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
						    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
						    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
						    'layers_per_block': 1,
						    'cross_attention_dim': self.cross_attention_dim,
						    'attention_head_dim': 4,
						    'resnet_time_scale_shift': 'scale_shift',
						    'class_embed_type': 'identity',
						}

						_UpperCAmelCase			=       UNetaDConditionModel(**A)
						return model



			@property
			def    _lowerCamelCase	(						self       :  List[Any])       ->   Any:



						"""simple docstring"""






						return {
						    "sample_size": 64,
						    "layers_per_block": 1,
						    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
						    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
						    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    "in_channels": 6,
						    "out_channels": 3,
						}



			@property
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Any:



						"""simple docstring"""






						torch.manual_seed(0)

						_UpperCAmelCase			=       UNetaDModel(**self.dummy_super_res_kwargs)
						return model



			@property
			def    _lowerCamelCase	(						self       :  str)       ->   List[str]:



						"""simple docstring"""






						torch.manual_seed(1)

						_UpperCAmelCase			=       UNetaDModel(**self.dummy_super_res_kwargs)
						return model



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.dummy_decoder
						_UpperCAmelCase			=       self.dummy_text_proj
						_UpperCAmelCase			=       self.dummy_text_encoder
						_UpperCAmelCase			=       self.dummy_tokenizer
						_UpperCAmelCase			=       self.dummy_super_res_first
						_UpperCAmelCase			=       self.dummy_super_res_last

						_UpperCAmelCase			=       UnCLIPScheduler(
						    variance_type='learned_range'			,		prediction_type='epsilon'			,		num_train_timesteps=10_00			,		)

						_UpperCAmelCase			=       UnCLIPScheduler(
						    variance_type='fixed_small_log'			,		prediction_type='epsilon'			,		num_train_timesteps=10_00			,		)

						_UpperCAmelCase			=       CLIPImageProcessor(crop_size=32			,		size=32)

						_UpperCAmelCase			=       self.dummy_image_encoder

						return {
						    "decoder": decoder,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "text_proj": text_proj,
						    "feature_extractor": feature_extractor,
						    "image_encoder": image_encoder,
						    "super_res_first": super_res_first,
						    "super_res_last": super_res_last,
						    "decoder_scheduler": decoder_scheduler,
						    "super_res_scheduler": super_res_scheduler,
						}



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  str			,		A       :  str=0			,		A       :  Optional[Any]=True)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       floats_tensor((1, 3, 32, 32)			,		rng=random.Random(A)).to(A)
						if str(A).startswith('mps'):
									_UpperCAmelCase			=       torch.manual_seed(A)
						else:
									_UpperCAmelCase			=       torch.Generator(device=A).manual_seed(A)

						if pil_image:
									_UpperCAmelCase			=       input_image * 0.5 + 0.5
									_UpperCAmelCase			=       input_image.clamp(0			,		1)
									_UpperCAmelCase			=       input_image.cpu().permute(0			,		2			,		3			,		1).float().numpy()
									_UpperCAmelCase			=       DiffusionPipeline.numpy_to_pil(A)[0]

						return {
						    "image": input_image,
						    "generator": generator,
						    "decoder_num_inference_steps": 2,
						    "super_res_num_inference_steps": 2,
						    "output_type": "np",
						}



			def    _lowerCamelCase	(						self       :  Any)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       'cpu'

						_UpperCAmelCase			=       self.get_dummy_components()

						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)

						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       self.get_dummy_inputs(A			,		pil_image=A)

						_UpperCAmelCase			=       pipe(**A)
						_UpperCAmelCase			=       output.images

						_UpperCAmelCase			=       self.get_dummy_inputs(A			,		pil_image=A)

						_UpperCAmelCase			=       pipe(
						    **A			,		return_dict=A			,		)[0]

						_UpperCAmelCase			=       image[0, -3:, -3:, -1]
						_UpperCAmelCase			=       image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						_UpperCAmelCase			=       np.array(
						    [
						        0.9_9_9_7,
						        0.0_0_0_2,
						        0.9_9_9_7,
						        0.9_9_9_7,
						        0.9_9_6_9,
						        0.0_0_2_3,
						        0.9_9_9_7,
						        0.9_9_6_9,
						        0.9_9_7_0,
						    ])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2



			def    _lowerCamelCase	(						self       :  str)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       'cpu'

						_UpperCAmelCase			=       self.get_dummy_components()

						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)

						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       self.get_dummy_inputs(A			,		pil_image=A)

						_UpperCAmelCase			=       pipe(**A)
						_UpperCAmelCase			=       output.images

						_UpperCAmelCase			=       self.get_dummy_inputs(A			,		pil_image=A)

						_UpperCAmelCase			=       pipe(
						    **A			,		return_dict=A			,		)[0]

						_UpperCAmelCase			=       image[0, -3:, -3:, -1]
						_UpperCAmelCase			=       image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						_UpperCAmelCase			=       np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2



			def    _lowerCamelCase	(						self       :  int)       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       'cpu'

						_UpperCAmelCase			=       self.get_dummy_components()

						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)

						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       self.get_dummy_inputs(A			,		pil_image=A)
						_UpperCAmelCase			=       [
						    pipeline_inputs['image'],
						    pipeline_inputs['image'],
						]

						_UpperCAmelCase			=       pipe(**A)
						_UpperCAmelCase			=       output.images

						_UpperCAmelCase			=       self.get_dummy_inputs(A			,		pil_image=A)
						_UpperCAmelCase			=       [
						    tuple_pipeline_inputs['image'],
						    tuple_pipeline_inputs['image'],
						]

						_UpperCAmelCase			=       pipe(
						    **A			,		return_dict=A			,		)[0]

						_UpperCAmelCase			=       image[0, -3:, -3:, -1]
						_UpperCAmelCase			=       image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (2, 64, 64, 3)

						_UpperCAmelCase			=       np.array(
						    [
						        0.9_9_9_7,
						        0.9_9_8_9,
						        0.0_0_0_8,
						        0.0_0_2_1,
						        0.9_9_6_0,
						        0.0_0_1_8,
						        0.0_0_1_4,
						        0.0_0_0_2,
						        0.9_9_3_3,
						    ])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2



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



						"""simple docstring"""






						_UpperCAmelCase			=       torch.device('cpu')







						class       __lowerCAmelCase   :
									UpperCamelCase        =					1





						_UpperCAmelCase			=       self.get_dummy_components()

						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)

						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       torch.Generator(device=A).manual_seed(0)
						_UpperCAmelCase			=       pipe.decoder.dtype
						_UpperCAmelCase			=       1

						_UpperCAmelCase			=       (
						    batch_size,
						    pipe.decoder.config.in_channels,
						    pipe.decoder.config.sample_size,
						    pipe.decoder.config.sample_size,
						)
						_UpperCAmelCase			=       pipe.prepare_latents(
						    A			,		dtype=A			,		device=A			,		generator=A			,		latents=A			,		scheduler=DummyScheduler())

						_UpperCAmelCase			=       (
						    batch_size,
						    pipe.super_res_first.config.in_channels // 2,
						    pipe.super_res_first.config.sample_size,
						    pipe.super_res_first.config.sample_size,
						)
						_UpperCAmelCase			=       pipe.prepare_latents(
						    A			,		dtype=A			,		device=A			,		generator=A			,		latents=A			,		scheduler=DummyScheduler())

						_UpperCAmelCase			=       self.get_dummy_inputs(A			,		pil_image=A)

						_UpperCAmelCase			=       pipe(
						    **A			,		decoder_latents=A			,		super_res_latents=A).images

						_UpperCAmelCase			=       self.get_dummy_inputs(A			,		pil_image=A)
						# Don't pass image, instead pass embedding
						_UpperCAmelCase			=       pipeline_inputs.pop('image')
						_UpperCAmelCase			=       pipe.image_encoder(A).image_embeds

						_UpperCAmelCase			=       pipe(
						    **A			,		decoder_latents=A			,		super_res_latents=A			,		image_embeddings=A			,		).images

						# make sure passing text embeddings manually is identical
						assert np.abs(img_out_a - img_out_a).max() < 1E-4



			@skip_mps
			def    _lowerCamelCase	(						self       :  Any)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       torch_device == 'cpu'

						# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
						_UpperCAmelCase			=       1E-2

						self._test_attention_slicing_forward_pass(
						    test_max_difference=A			,		expected_max_diff=A)



			@skip_mps
			def    _lowerCamelCase	(						self       :  Dict)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       torch_device == 'cpu'
						_UpperCAmelCase			=       True
						_UpperCAmelCase			=       [
						    'decoder_num_inference_steps',
						    'super_res_num_inference_steps',
						]

						self._test_inference_batch_single_identical(
						    test_max_difference=A			,		relax_max_difference=A			,		additional_params_copy_to_batched_inputs=A			,		)



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



						"""simple docstring"""






						_UpperCAmelCase			=       [
						    'decoder_num_inference_steps',
						    'super_res_num_inference_steps',
						]

						if torch_device == "mps":
									# TODO: MPS errors with larger batch sizes
									_UpperCAmelCase			=       [2, 3]
									self._test_inference_batch_consistent(
									    batch_sizes=A			,		additional_params_copy_to_batched_inputs=A			,		)
						else:
									self._test_inference_batch_consistent(
									    additional_params_copy_to_batched_inputs=A)



			@skip_mps
			def    _lowerCamelCase	(						self       :  Dict)       ->   int:



						"""simple docstring"""






						return super().test_dict_tuple_outputs_equivalent()



			@skip_mps
			def    _lowerCamelCase	(						self       :  Tuple)       ->   Tuple:



						"""simple docstring"""






						return super().test_save_load_local()



			@skip_mps
			def    _lowerCamelCase	(						self       :  int)       ->   int:



						"""simple docstring"""






						return super().test_save_load_optional_components()







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



			def    _lowerCamelCase	(						self       :  Dict)       ->   Tuple:



						"""simple docstring"""






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



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



						"""simple docstring"""






						_UpperCAmelCase			=       load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png')
						_UpperCAmelCase			=       load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy')

						_UpperCAmelCase			=       UnCLIPImageVariationPipeline.from_pretrained(
						    'kakaobrain/karlo-v1-alpha-image-variations'			,		torch_dtype=torch.floataa)
						_UpperCAmelCase			=       pipeline.to(A)
						pipeline.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       torch.Generator(device='cpu').manual_seed(0)
						_UpperCAmelCase			=       pipeline(
						    A			,		generator=A			,		output_type='np'			,		)

						_UpperCAmelCase			=       output.images[0]

						assert image.shape == (2_56, 2_56, 3)

						assert_mean_pixel_difference(A			,		A			,		15)





import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					CLIPTokenizer
			UpperCamelCase        =					CLIPTokenizerFast
			UpperCamelCase        =					True
			UpperCamelCase        =					{}
			UpperCamelCase        =					False



			def    _lowerCamelCase	(						self       :  List[str])       ->   List[str]:



						"""simple docstring"""






						super().setUp()

						# fmt: off
						_UpperCAmelCase			=       ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
						# fmt: on
						_UpperCAmelCase			=       dict(zip(A			,		range(len(A))))
						_UpperCAmelCase			=       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
						_UpperCAmelCase			=       {'unk_token': '<unk>'}

						_UpperCAmelCase			=       os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['vocab_file'])
						_UpperCAmelCase			=       os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['merges_file'])
						with open(self.vocab_file			,		'w'			,		encoding='utf-8') as fp:
									fp.write(json.dumps(A) + '\n')
						with open(self.merges_file			,		'w'			,		encoding='utf-8') as fp:
									fp.write('\n'.join(A))



			def    _lowerCamelCase	(						self       :  Optional[Any]			,		**A       :  str)       ->   Optional[int]:



						"""simple docstring"""






						kwargs.update(self.special_tokens_map)
						return CLIPTokenizer.from_pretrained(self.tmpdirname			,		**A)



			def    _lowerCamelCase	(						self       :  Any			,		**A       :  Dict)       ->   Union[str, Any]:



						"""simple docstring"""






						kwargs.update(self.special_tokens_map)
						return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,		**A)



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  Union[str, Any])       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       'lower newer'
						_UpperCAmelCase			=       'lower newer'
						return input_text, output_text



			def    _lowerCamelCase	(						self       :  Dict)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       CLIPTokenizer(self.vocab_file			,		self.merges_file			,		**self.special_tokens_map)
						_UpperCAmelCase			=       'lower newer'
						_UpperCAmelCase			=       ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
						_UpperCAmelCase			=       tokenizer.tokenize(A)
						self.assertListEqual(A			,		A)

						_UpperCAmelCase			=       tokens + [tokenizer.unk_token]
						_UpperCAmelCase			=       [10, 2, 16, 9, 3, 2, 16, 20]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(A)			,		A)



			@require_ftfy
			def    _lowerCamelCase	(						self       :  List[Any])       ->   Dict:



						"""simple docstring"""






						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
												_UpperCAmelCase			=       self.tokenizer_class.from_pretrained(A			,		**A)
												_UpperCAmelCase			=       self.rust_tokenizer_class.from_pretrained(A			,		**A)

												_UpperCAmelCase			=       'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
												_UpperCAmelCase			=       tokenizer_s.tokenize(A)
												_UpperCAmelCase			=       tokenizer_r.tokenize(A)

												self.assertListEqual(A			,		A)

												# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
												# with Tilde) encoded in 2 different ways
												_UpperCAmelCase			=       'xa\u0303y' + ' ' + 'x\xe3y'
												_UpperCAmelCase			=       tokenizer_s.tokenize(A)
												_UpperCAmelCase			=       tokenizer_r.tokenize(A)

												self.assertListEqual(A			,		A)

												# Test that the tokenization is identical on unicode of space type
												_UpperCAmelCase			=       [
												    '\u0009',  # (horizontal tab, '\t')
												    '\u000B',  # (vertical tab)
												    '\u000C',  # (form feed)
												    '\u0020',  # (space, ' ')
												    '\u200E',  # (left-to-right mark):w
												    '\u200F',  # (right-to-left mark)
												]
												for unicode_seq in spaces_unicodes:
															_UpperCAmelCase			=       tokenizer_s.tokenize(A)
															_UpperCAmelCase			=       tokenizer_r.tokenize(A)

															self.assertListEqual(A			,		A)

												# Test that the tokenization is identical on unicode of line break type
												_UpperCAmelCase			=       [
												    '\u000A',  # (line feed, '\n')
												    '\r\n',  # (carriage return and line feed, '\r\n')
												    '\u000D',  # (carriage return, '\r')
												    '\r',  # (carriage return, '\r')
												    '\u000D',  # (carriage return, '\r')
												    '\u2028',  # (line separator)
												    '\u2029',  # (paragraph separator)
												    # "\u0085", # (next line)
												]

												# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
												# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
												# space (and thus into an empty list).

												for unicode_seq in line_break_unicodes:
															_UpperCAmelCase			=       tokenizer_s.tokenize(A)
															_UpperCAmelCase			=       tokenizer_r.tokenize(A)

															self.assertListEqual(A			,		A)



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



						"""simple docstring"""






						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
												_UpperCAmelCase			=       'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
												_UpperCAmelCase			=       F"{text_of_1_token} {text_of_1_token}"

												_UpperCAmelCase			=       self.rust_tokenizer_class.from_pretrained(
												    A			,		use_fast=A			,		)
												_UpperCAmelCase			=       tokenizer_r(A			,		return_offsets_mapping=A			,		add_special_tokens=A)
												self.assertEqual(encoding.offset_mapping[0]			,		(0, len(A)))
												self.assertEqual(
												    encoding.offset_mapping[1]			,		(len(A) + 1, len(A) + 1 + len(A))			,		)

												_UpperCAmelCase			=       F" {text}"

												_UpperCAmelCase			=       self.rust_tokenizer_class.from_pretrained(
												    A			,		use_fast=A			,		)
												_UpperCAmelCase			=       tokenizer_r(A			,		return_offsets_mapping=A			,		add_special_tokens=A)
												self.assertEqual(encoding.offset_mapping[0]			,		(1, 1 + len(A)))
												self.assertEqual(
												    encoding.offset_mapping[1]			,		(1 + len(A) + 1, 1 + len(A) + 1 + len(A))			,		)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   str:



						"""simple docstring"""






						with self.assertRaises(A) as context:
									self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer')

						self.assertTrue(
						    context.exception.args[0].startswith(
						        'The `backend_tokenizer` provided does not match the expected format.'))



			@require_ftfy
			def    _lowerCamelCase	(						self       :  int)       ->   int:



						"""simple docstring"""






						super().test_tokenization_python_rust_equals()



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Any:



						"""simple docstring"""






						pass
