
import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[tensor.shape for tensor in tensor_list]
			return all(shape == shapes[0] for shape in shapes[1:]       )





class 			_lowerCAmelCase      (			__a      ,   __a      ,   __a      ,   unittest.TestCase ):
				_lowercase    	=StableDiffusionLatentUpscalePipeline
				_lowercase    	=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    '''height''',
				    '''width''',
				    '''cross_attention_kwargs''',
				    '''negative_prompt_embeds''',
				    '''prompt_embeds''',
				}
				_lowercase    	=PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
				_lowercase    	=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				_lowercase    	=frozenset(
				    [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				_lowercase    	=frozenset([] )

				_lowercase    	=True




				@property
				def   __a		(      self  )       ->   int:
							lowerCAmelCase_      			=			1
							lowerCAmelCase_      			=			4
							lowerCAmelCase_      			=			(16, 16)

							lowerCAmelCase_      			=			floats_tensor((batch_size, num_channels) + sizes       ,       rng=random.Random(0  )  ).to(_UpperCamelCase  )
							return image




				def   __a		(      self  )       ->   Union[str, Any]:
							torch.manual_seed(0  )
							lowerCAmelCase_      			=			UNetaDConditionModel(
							    act_fn="gelu"       ,       attention_head_dim=8       ,       norm_num_groups=_UpperCamelCase       ,       block_out_channels=[32, 32, 64, 64]       ,       time_cond_proj_dim=160       ,       conv_in_kernel=1       ,       conv_out_kernel=1       ,       cross_attention_dim=32       ,       down_block_types=(
							        "KDownBlock2D",
							        "KCrossAttnDownBlock2D",
							        "KCrossAttnDownBlock2D",
							        "KCrossAttnDownBlock2D",
							    )       ,       in_channels=8       ,       mid_block_type=_UpperCamelCase       ,       only_cross_attention=_UpperCamelCase       ,       out_channels=5       ,       resnet_time_scale_shift="scale_shift"       ,       time_embedding_type="fourier"       ,       timestep_post_act="gelu"       ,       up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")       ,       )
							lowerCAmelCase_      			=			AutoencoderKL(
							    block_out_channels=[32, 32, 64, 64]       ,       in_channels=3       ,       out_channels=3       ,       down_block_types=[
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							    ]       ,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]       ,       latent_channels=4       ,       )
							lowerCAmelCase_      			=			EulerDiscreteScheduler(prediction_type="sample"  )
							lowerCAmelCase_      			=			CLIPTextConfig(
							    bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=32       ,       intermediate_size=37       ,       layer_norm_eps=1e-0_5       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=1_000       ,       hidden_act="quick_gelu"       ,       projection_dim=512       ,       )
							lowerCAmelCase_      			=			CLIPTextModel(_UpperCamelCase  )
							lowerCAmelCase_      			=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

							lowerCAmelCase_      			=			{
							    "unet": model.eval(),
							    "vae": vae.eval(),
							    "scheduler": scheduler,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							}

							return components




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=0  )       ->   Dict:
							if str(_UpperCamelCase  ).startswith("mps"  ):
										lowerCAmelCase_      			=			torch.manual_seed(_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )
							lowerCAmelCase_      			=			{
							    "prompt": "A painting of a squirrel eating a burger",
							    "image": self.dummy_image.cpu(),
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "numpy",
							}
							return inputs




				def   __a		(      self  )       ->   Tuple:
							lowerCAmelCase_      			=			"cpu"

							lowerCAmelCase_      			=			self.get_dummy_components()
							lowerCAmelCase_      			=			self.pipeline_class(**_UpperCamelCase  )
							pipe.to(_UpperCamelCase  )
							pipe.set_progress_bar_config(disable=_UpperCamelCase  )

							lowerCAmelCase_      			=			self.get_dummy_inputs(_UpperCamelCase  )
							lowerCAmelCase_      			=			pipe(**_UpperCamelCase  ).images
							lowerCAmelCase_      			=			image[0, -3:, -3:, -1]

							self.assertEqual(image.shape       ,       (1, 256, 256, 3)  )
							lowerCAmelCase_      			=			np.array(
							    [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]  )
							lowerCAmelCase_      			=			np.abs(image_slice.flatten() - expected_slice  ).max()
							self.assertLessEqual(_UpperCamelCase       ,       1e-3  )




				def   __a		(      self  )       ->   Optional[Any]:
							super().test_attention_slicing_forward_pass(expected_max_diff=7e-3  )




				def   __a		(      self  )       ->   Union[str, Any]:
							super().test_cpu_offload_forward_pass(expected_max_diff=3e-3  )




				def   __a		(      self  )       ->   Tuple:
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3  )




				def   __a		(      self  )       ->   str:
							super().test_inference_batch_single_identical(expected_max_diff=7e-3  )




				def   __a		(      self  )       ->   str:
							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3  )




				def   __a		(      self  )       ->   Union[str, Any]:
							super().test_save_load_local(expected_max_difference=3e-3  )




				def   __a		(      self  )       ->   str:
							super().test_save_load_optional_components(expected_max_difference=3e-3  )




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			[
							    "DDIMScheduler",
							    "DDPMScheduler",
							    "PNDMScheduler",
							    "HeunDiscreteScheduler",
							    "EulerAncestralDiscreteScheduler",
							    "KDPM2DiscreteScheduler",
							    "KDPM2AncestralDiscreteScheduler",
							    "DPMSolverSDEScheduler",
							]
							lowerCAmelCase_      			=			self.get_dummy_components()
							lowerCAmelCase_      			=			self.pipeline_class(**_UpperCamelCase  )

							# make sure that PNDM does not need warm-up
							pipe.scheduler.register_to_config(skip_prk_steps=_UpperCamelCase  )

							pipe.to(_UpperCamelCase  )
							pipe.set_progress_bar_config(disable=_UpperCamelCase  )
							lowerCAmelCase_      			=			self.get_dummy_inputs(_UpperCamelCase  )
							lowerCAmelCase_      			=			2

							lowerCAmelCase_      			=			[]
							for scheduler_enum in KarrasDiffusionSchedulers:
										if scheduler_enum.name in skip_schedulers:
													# no sigma schedulers are not supported
													# no schedulers
													continue

										lowerCAmelCase_      			=			getattr(_UpperCamelCase       ,       scheduler_enum.name  )
										lowerCAmelCase_      			=			scheduler_cls.from_config(pipe.scheduler.config  )
										lowerCAmelCase_      			=			pipe(**_UpperCamelCase  )[0]
										outputs.append(_UpperCamelCase  )

							assert check_same_shape(_UpperCamelCase  )





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




				def   __a		(      self  )       ->   Optional[int]:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				def   __a		(      self  )       ->   Tuple:
							lowerCAmelCase_      			=			torch.manual_seed(33  )

							lowerCAmelCase_      			=			StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"       ,       torch_dtype=torch.floataa  )
							pipe.to("cuda"  )

							lowerCAmelCase_      			=			StableDiffusionLatentUpscalePipeline.from_pretrained(
							    "stabilityai/sd-x2-latent-upscaler"       ,       torch_dtype=torch.floataa  )
							upscaler.to("cuda"  )

							lowerCAmelCase_      			=			"a photo of an astronaut high resolution, unreal engine, ultra realistic"

							lowerCAmelCase_      			=			pipe(_UpperCamelCase       ,       generator=_UpperCamelCase       ,       output_type="latent"  ).images

							lowerCAmelCase_      			=			upscaler(
							    prompt=_UpperCamelCase       ,       image=_UpperCamelCase       ,       num_inference_steps=20       ,       guidance_scale=0       ,       generator=_UpperCamelCase       ,       output_type="np"       ,       ).images[0]

							lowerCAmelCase_      			=			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"  )
							assert np.abs((expected_image - image).mean()  ) < 5e-2




				def   __a		(      self  )       ->   Union[str, Any]:
							lowerCAmelCase_      			=			torch.manual_seed(33  )

							lowerCAmelCase_      			=			StableDiffusionLatentUpscalePipeline.from_pretrained(
							    "stabilityai/sd-x2-latent-upscaler"       ,       torch_dtype=torch.floataa  )
							upscaler.to("cuda"  )

							lowerCAmelCase_      			=			"the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

							lowerCAmelCase_      			=			load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"  )

							lowerCAmelCase_      			=			upscaler(
							    prompt=_UpperCamelCase       ,       image=_UpperCamelCase       ,       num_inference_steps=20       ,       guidance_scale=0       ,       generator=_UpperCamelCase       ,       output_type="np"       ,       ).images[0]

							lowerCAmelCase_      			=			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"  )
							assert np.abs((expected_image - image).max()  ) < 5e-2





import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
			from test_module.custom_tokenization_fast import CustomTokenizerFast





class 			_lowerCAmelCase      (			unittest.TestCase ):




				def   __a		(      self  )       ->   str:
							# A mock response for an HTTP head request to emulate server down
							lowerCAmelCase_      			=			mock.Mock()
							lowerCAmelCase_      			=			500
							lowerCAmelCase_      			=			{}
							lowerCAmelCase_      			=			HTTPError
							lowerCAmelCase_      			=			{}

							# Download this model to make sure it's in the cache.
							lowerCAmelCase_      			=			BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"  )

							# Under the mock environment we get a 500 error when trying to reach the tokenizer.
							with mock.patch("requests.Session.request"       ,       return_value=_UpperCamelCase  ) as mock_head:
										lowerCAmelCase_      			=			BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"  )
										# This check we did call the fake head request
										mock_head.assert_called()




				@require_tokenizers
				def   __a		(      self  )       ->   int:
							# A mock response for an HTTP head request to emulate server down
							lowerCAmelCase_      			=			mock.Mock()
							lowerCAmelCase_      			=			500
							lowerCAmelCase_      			=			{}
							lowerCAmelCase_      			=			HTTPError
							lowerCAmelCase_      			=			{}

							# Download this model to make sure it's in the cache.
							lowerCAmelCase_      			=			GPTaTokenizerFast.from_pretrained("gpt2"  )

							# Under the mock environment we get a 500 error when trying to reach the tokenizer.
							with mock.patch("requests.Session.request"       ,       return_value=_UpperCamelCase  ) as mock_head:
										lowerCAmelCase_      			=			GPTaTokenizerFast.from_pretrained("gpt2"  )
										# This check we did call the fake head request
										mock_head.assert_called()




				def   __a		(      self  )       ->   List[Any]:
							# This test is for deprecated behavior and can be removed in v5
							try:
										lowerCAmelCase_      			=			tempfile.mktemp()
										with open(_UpperCamelCase       ,       "wb"  ) as f:
													http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"       ,       _UpperCamelCase  )

										lowerCAmelCase_      			=			AlbertTokenizer.from_pretrained(_UpperCamelCase  )
							finally:
										os.remove(_UpperCamelCase  )

							# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
							# the current folder and have the right name.
							if os.path.isfile("tokenizer.json"  ):
										# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
										return
							try:
										with open("tokenizer.json"       ,       "wb"  ) as f:
													http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json"       ,       _UpperCamelCase  )
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"  )
										# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
										self.assertEqual(tokenizer.vocab_size       ,       1_000  )
										# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

							finally:
										os.remove("tokenizer.json"  )




				def   __a		(      self  )       ->   Union[str, Any]:
							# This test is for deprecated behavior and can be removed in v5
							lowerCAmelCase_      			=			AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"  )





@is_staging_test
class 			_lowerCAmelCase      (			unittest.TestCase ):
				_lowercase    	=['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']




				@classmethod
				def   __a		(      cls  )       ->   Tuple:
							lowerCAmelCase_      			=			TOKEN
							HfFolder.save_token(_UpperCamelCase  )




				@classmethod
				def   __a		(      cls  )       ->   Tuple:
							try:
										delete_repo(token=cls._token       ,       repo_id="test-tokenizer"  )
							except HTTPError:
										pass

							try:
										delete_repo(token=cls._token       ,       repo_id="valid_org/test-tokenizer-org"  )
							except HTTPError:
										pass

							try:
										delete_repo(token=cls._token       ,       repo_id="test-dynamic-tokenizer"  )
							except HTTPError:
										pass




				def   __a		(      self  )       ->   int:
							with tempfile.TemporaryDirectory() as tmp_dir:
										lowerCAmelCase_      			=			os.path.join(_UpperCamelCase       ,       "vocab.txt"  )
										with open(_UpperCamelCase       ,       "w"       ,       encoding="utf-8"  ) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]  )  )
										lowerCAmelCase_      			=			BertTokenizer(_UpperCamelCase  )

							tokenizer.push_to_hub("test-tokenizer"       ,       use_auth_token=self._token  )
							lowerCAmelCase_      			=			BertTokenizer.from_pretrained(f"""{USER}/test-tokenizer"""  )
							self.assertDictEqual(new_tokenizer.vocab       ,       tokenizer.vocab  )

							# Reset repo
							delete_repo(token=self._token       ,       repo_id="test-tokenizer"  )

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer.save_pretrained(_UpperCamelCase       ,       repo_id="test-tokenizer"       ,       push_to_hub=_UpperCamelCase       ,       use_auth_token=self._token  )

							lowerCAmelCase_      			=			BertTokenizer.from_pretrained(f"""{USER}/test-tokenizer"""  )
							self.assertDictEqual(new_tokenizer.vocab       ,       tokenizer.vocab  )




				def   __a		(      self  )       ->   Optional[int]:
							with tempfile.TemporaryDirectory() as tmp_dir:
										lowerCAmelCase_      			=			os.path.join(_UpperCamelCase       ,       "vocab.txt"  )
										with open(_UpperCamelCase       ,       "w"       ,       encoding="utf-8"  ) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]  )  )
										lowerCAmelCase_      			=			BertTokenizer(_UpperCamelCase  )

							tokenizer.push_to_hub("valid_org/test-tokenizer-org"       ,       use_auth_token=self._token  )
							lowerCAmelCase_      			=			BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"  )
							self.assertDictEqual(new_tokenizer.vocab       ,       tokenizer.vocab  )

							# Reset repo
							delete_repo(token=self._token       ,       repo_id="valid_org/test-tokenizer-org"  )

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer.save_pretrained(
										    _UpperCamelCase       ,       repo_id="valid_org/test-tokenizer-org"       ,       push_to_hub=_UpperCamelCase       ,       use_auth_token=self._token  )

							lowerCAmelCase_      			=			BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"  )
							self.assertDictEqual(new_tokenizer.vocab       ,       tokenizer.vocab  )




				@require_tokenizers
				def   __a		(      self  )       ->   Optional[Any]:
							CustomTokenizer.register_for_auto_class()
							with tempfile.TemporaryDirectory() as tmp_dir:
										lowerCAmelCase_      			=			os.path.join(_UpperCamelCase       ,       "vocab.txt"  )
										with open(_UpperCamelCase       ,       "w"       ,       encoding="utf-8"  ) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]  )  )
										lowerCAmelCase_      			=			CustomTokenizer(_UpperCamelCase  )

							# No fast custom tokenizer
							tokenizer.push_to_hub("test-dynamic-tokenizer"       ,       use_auth_token=self._token  )

							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(f"""{USER}/test-dynamic-tokenizer"""       ,       trust_remote_code=_UpperCamelCase  )
							# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
							self.assertEqual(tokenizer.__class__.__name__       ,       "CustomTokenizer"  )

							# Fast and slow custom tokenizer
							CustomTokenizerFast.register_for_auto_class()
							with tempfile.TemporaryDirectory() as tmp_dir:
										lowerCAmelCase_      			=			os.path.join(_UpperCamelCase       ,       "vocab.txt"  )
										with open(_UpperCamelCase       ,       "w"       ,       encoding="utf-8"  ) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]  )  )

										lowerCAmelCase_      			=			BertTokenizerFast.from_pretrained(_UpperCamelCase  )
										bert_tokenizer.save_pretrained(_UpperCamelCase  )
										lowerCAmelCase_      			=			CustomTokenizerFast.from_pretrained(_UpperCamelCase  )

							tokenizer.push_to_hub("test-dynamic-tokenizer"       ,       use_auth_token=self._token  )

							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(f"""{USER}/test-dynamic-tokenizer"""       ,       trust_remote_code=_UpperCamelCase  )
							# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
							self.assertEqual(tokenizer.__class__.__name__       ,       "CustomTokenizerFast"  )
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(
							    f"""{USER}/test-dynamic-tokenizer"""       ,       use_fast=_UpperCamelCase       ,       trust_remote_code=_UpperCamelCase  )
							# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
							self.assertEqual(tokenizer.__class__.__name__       ,       "CustomTokenizer"  )





class 			_lowerCAmelCase      (			unittest.TestCase ):




				def   __a		(      self  )       ->   Any:
							lowerCAmelCase_      			=			Trie()
							trie.add("Hello 友達"  )
							self.assertEqual(trie.data       ,       {"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}}  )
							trie.add("Hello"  )
							trie.data
							self.assertEqual(trie.data       ,       {"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}}  )




				def   __a		(      self  )       ->   Optional[int]:
							lowerCAmelCase_      			=			Trie()
							self.assertEqual(trie.split("[CLS] This is a extra_id_100"  )       ,       ["[CLS] This is a extra_id_100"]  )
							trie.add("[CLS]"  )
							trie.add("extra_id_1"  )
							trie.add("extra_id_100"  )
							self.assertEqual(trie.split("[CLS] This is a extra_id_100"  )       ,       ["[CLS]", " This is a ", "extra_id_100"]  )




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			Trie()
							trie.add("A"  )
							self.assertEqual(trie.split("ABC"  )       ,       ["A", "BC"]  )
							self.assertEqual(trie.split("BCA"  )       ,       ["BC", "A"]  )




				def   __a		(      self  )       ->   int:
							lowerCAmelCase_      			=			Trie()
							trie.add("TOKEN]"  )
							trie.add("[SPECIAL_TOKEN]"  )
							self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"  )       ,       ["This is something ", "[SPECIAL_TOKEN]"]  )




				def   __a		(      self  )       ->   int:
							lowerCAmelCase_      			=			Trie()
							trie.add("A"  )
							trie.add("P"  )
							trie.add("[SPECIAL_TOKEN]"  )
							self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"  )       ,       ["This is something ", "[SPECIAL_TOKEN]"]  )




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			Trie()
							trie.add("AB"  )
							trie.add("B"  )
							trie.add("C"  )
							self.assertEqual(trie.split("ABC"  )       ,       ["AB", "C"]  )




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			Trie()
							trie.add("ABC"  )
							trie.add("B"  )
							trie.add("CD"  )
							self.assertEqual(trie.split("ABCD"  )       ,       ["ABC", "D"]  )




				def   __a		(      self  )       ->   List[Any]:
							# Even if the offsets are wrong, we necessarily output correct string
							# parts.
							lowerCAmelCase_      			=			Trie()
							lowerCAmelCase_      			=			trie.cut_text("ABC"       ,       [0, 0, 2, 1, 2, 3]  )
							self.assertEqual(_UpperCamelCase       ,       ["AB", "C"]  )



