


'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_pytesseract_available():
							from PIL import Image

							from transformers import LayoutLMvaImageProcessor
class 						__UpperCamelCase	(						unittest.TestCase							):





							def __init__(  self		:List[Any]  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Optional[Any]=7  ,_UpperCamelCase		:str=3  ,_UpperCamelCase		:Optional[int]=1_8  ,_UpperCamelCase		:Any=3_0  ,_UpperCamelCase		:Optional[Any]=4_0_0  ,_UpperCamelCase		:Tuple=True  ,_UpperCamelCase		:List[str]=None  ,_UpperCamelCase		:Tuple=True  ,):
									snake_case_		:							Dict			       =						size if size is not None else {"""height""": 1_8, """width""": 1_8}
									snake_case_		:							int			       =						parent
									snake_case_		:							str			       =						batch_size
									snake_case_		:							Any			       =						num_channels
									snake_case_		:							List[str]			       =						image_size
									snake_case_		:							str			       =						min_resolution
									snake_case_		:							Dict			       =						max_resolution
									snake_case_		:							Optional[int]			       =						do_resize
									snake_case_		:							int			       =						size
									snake_case_		:							List[Any]			       =						apply_ocr





							def 		a__   (  self		:List[Any]			):
									return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class 						__UpperCamelCase	(						lowercase__						,  unittest.TestCase							):
							lowercase			:		Dict   		=  LayoutLMvaImageProcessor if is_pytesseract_available() else None





							def 		a__   (  self		:Dict			):
									snake_case_		:							Union[str, Any]			       =						LayoutLMvaImageProcessingTester(self			)





							@property
							def 		a__   (  self		:List[Any]			):
									return self.image_processor_tester.prepare_image_processor_dict()





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							Union[str, Any]			       =						self.image_processing_class(**self.image_processor_dict			)
									self.assertTrue(hasattr(_UpperCamelCase  ,"""do_resize"""			)			)
									self.assertTrue(hasattr(_UpperCamelCase  ,"""size"""			)			)
									self.assertTrue(hasattr(_UpperCamelCase  ,"""apply_ocr"""			)			)





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							Tuple			       =						self.image_processing_class.from_dict(self.image_processor_dict			)
									self.assertEqual(image_processor.size  ,{"""height""": 1_8, """width""": 1_8}			)

									snake_case_		:							Optional[int]			       =						self.image_processing_class.from_dict(self.image_processor_dict  ,size=4_2			)
									self.assertEqual(image_processor.size  ,{"""height""": 4_2, """width""": 4_2}			)





							def 		a__   (  self		:Dict			):
									pass





							def 		a__   (  self		:Any			):
									# Initialize image_processing
									snake_case_		:							Tuple			       =						self.image_processing_class(**self.image_processor_dict			)
									# create random PIL images
									snake_case_		:							Dict			       =						prepare_image_inputs(self.image_processor_tester  ,equal_resolution=_UpperCamelCase			)
									for image in image_inputs:
											self.assertIsInstance(_UpperCamelCase  ,Image.Image			)

									# Test not batched input
									snake_case_		:							Optional[Any]			       =						image_processing(image_inputs[0]  ,return_tensors="""pt"""			)
									self.assertEqual(
									    encoding.pixel_values.shape  ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,)

									self.assertIsInstance(encoding.words  ,_UpperCamelCase			)
									self.assertIsInstance(encoding.boxes  ,_UpperCamelCase			)

									# Test batched
									snake_case_		:							Tuple			       =						image_processing(_UpperCamelCase  ,return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,)





							def 		a__   (  self		:Optional[Any]			):
									# Initialize image_processing
									snake_case_		:							Dict			       =						self.image_processing_class(**self.image_processor_dict			)
									# create random numpy tensors
									snake_case_		:							Tuple			       =						prepare_image_inputs(self.image_processor_tester  ,equal_resolution=_UpperCamelCase  ,numpify=_UpperCamelCase			)
									for image in image_inputs:
											self.assertIsInstance(_UpperCamelCase  ,np.ndarray			)

									# Test not batched input
									snake_case_		:							str			       =						image_processing(image_inputs[0]  ,return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,)

									# Test batched
									snake_case_		:							List[str]			       =						image_processing(_UpperCamelCase  ,return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,)





							def 		a__   (  self		:List[str]			):
									# Initialize image_processing
									snake_case_		:							Dict			       =						self.image_processing_class(**self.image_processor_dict			)
									# create random PyTorch tensors
									snake_case_		:							Optional[int]			       =						prepare_image_inputs(self.image_processor_tester  ,equal_resolution=_UpperCamelCase  ,torchify=_UpperCamelCase			)
									for image in image_inputs:
											self.assertIsInstance(_UpperCamelCase  ,torch.Tensor			)

									# Test not batched input
									snake_case_		:							Tuple			       =						image_processing(image_inputs[0]  ,return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,)

									# Test batched
									snake_case_		:							Optional[Any]			       =						image_processing(_UpperCamelCase  ,return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,)





							def 		a__   (  self		:List[str]			):
									# with apply_OCR = True
									snake_case_		:							List[Any]			       =						LayoutLMvaImageProcessor()

									from datasets import load_dataset

									snake_case_		:							Optional[int]			       =						load_dataset("""hf-internal-testing/fixtures_docvqa"""  ,split="""test"""			)

									snake_case_		:							Optional[Any]			       =						Image.open(ds[0]["""file"""]			).convert("""RGB"""			)

									snake_case_		:							Optional[int]			       =						image_processing(_UpperCamelCase  ,return_tensors="""pt"""			)

									self.assertEqual(encoding.pixel_values.shape  ,(1, 3, 2_2_4, 2_2_4)			)
									self.assertEqual(len(encoding.words			)  ,len(encoding.boxes			)			)

									# fmt: off
									# the words and boxes were obtained with Tesseract 4.1.1
									snake_case_		:							Tuple			       =						[["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]]  # noqa: E231
									snake_case_		:							Dict			       =						[[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]]  # noqa: E231
									# fmt: on

									self.assertListEqual(encoding.words  ,_UpperCamelCase			)
									self.assertListEqual(encoding.boxes  ,_UpperCamelCase			)

									# with apply_OCR = False
									snake_case_		:							str			       =						LayoutLMvaImageProcessor(apply_ocr=_UpperCamelCase			)

									snake_case_		:							str			       =						image_processing(_UpperCamelCase  ,return_tensors="""pt"""			)

									self.assertEqual(encoding.pixel_values.shape  ,(1, 3, 2_2_4, 2_2_4)			)



'''simple docstring'''






import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class 						__UpperCamelCase	(						lowercase__						,  unittest.TestCase							):
							lowercase			:		Dict   		=  TransfoXLTokenizer
							lowercase			:		Optional[Any]   		=  False
							lowercase			:		Dict   		=  False





							def 		a__   (  self		:Union[str, Any]			):
									super().setUp()

									snake_case_		:							Optional[int]			       =						[
									    """<unk>""",
									    """[CLS]""",
									    """[SEP]""",
									    """want""",
									    """unwanted""",
									    """wa""",
									    """un""",
									    """running""",
									    """,""",
									    """low""",
									    """l""",
									]
									snake_case_		:							Optional[int]			       =						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 		a__   (  self		:List[Any]  ,**_UpperCamelCase		:Optional[Any]			):
									snake_case_		:							Tuple			       =						True
									return TransfoXLTokenizer.from_pretrained(self.tmpdirname  ,**_UpperCamelCase			)





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:Union[str, Any]			):
									snake_case_		:							Any			       =						"""<unk> UNwanted , running"""
									snake_case_		:							Optional[int]			       =						"""<unk> unwanted, running"""
									return input_text, output_text





							def 		a__   (  self		:Dict			):
									snake_case_		:							Dict			       =						TransfoXLTokenizer(vocab_file=self.vocab_file  ,lower_case=_UpperCamelCase			)

									snake_case_		:							Dict			       =						tokenizer.tokenize("""<unk> UNwanted , running"""			)
									self.assertListEqual(_UpperCamelCase  ,["""<unk>""", """unwanted""", """,""", """running"""]			)

									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase			)  ,[0, 4, 8, 7]			)





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							Dict			       =						TransfoXLTokenizer(lower_case=_UpperCamelCase			)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """			)  ,["""hello""", """!""", """how""", """are""", """you""", """?"""]			)





							def 		a__   (  self		:Any			):
									snake_case_		:							List[Any]			       =						TransfoXLTokenizer(lower_case=_UpperCamelCase			)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """			)  ,["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]			)





							def 		a__   (  self		:List[str]			):
									snake_case_		:							str			       =						TransfoXLTokenizer(lower_case=_UpperCamelCase			)
									snake_case_		:							List[str]			       =						"""Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
									snake_case_		:							Optional[int]			       =						[
									    """Hello""",
									    """(""",
									    """bracket""",
									    """)""",
									    """and""",
									    """side""",
									    """@-@""",
									    """scrolled""",
									    """[""",
									    """and""",
									    """]""",
									    """Henry""",
									    """'s""",
									    """$""",
									    """5""",
									    """@,@""",
									    """000""",
									    """with""",
									    """3""",
									    """@.@""",
									    """34""",
									    """m""",
									    """.""",
									    """What""",
									    """'s""",
									    """up""",
									    """!""",
									    """?""",
									]

									self.assertListEqual(tokenizer.tokenize(_UpperCamelCase			)  ,_UpperCamelCase			)

									self.assertEqual(tokenizer.convert_tokens_to_string(_UpperCamelCase			)  ,_UpperCamelCase			)





							def 		a__   (  self		:Dict			):
									snake_case_		:							Union[str, Any]			       =						self.get_tokenizer()
									snake_case_		:							Dict			       =						len(_UpperCamelCase			)

									tokenizer.add_tokens(["""new1""", """new2"""]			)
									tokenizer.move_added_token("""new1"""  ,1			)

									# Check that moved token is not copied (duplicate)
									self.assertEqual(len(_UpperCamelCase			)  ,original_len + 2			)
									# Check that token is moved to specified id
									self.assertEqual(tokenizer.encode("""new1"""			)  ,[1]			)
									self.assertEqual(tokenizer.decode([1]			)  ,"""new1"""			)