'''simple docstring'''







import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import DetrImageProcessor




class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						def __init__(    self :       Optional[Any]							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       Dict=7							,							UpperCamelCase_ :       List[Any]=3							,							UpperCamelCase_ :       Optional[Any]=30							,							UpperCamelCase_ :       Optional[int]=4_00							,							UpperCamelCase_ :       Optional[Any]=True							,							UpperCamelCase_ :       Tuple=None							,							UpperCamelCase_ :       Tuple=True							,							UpperCamelCase_ :       Optional[Any]=1 / 2_55							,							UpperCamelCase_ :       int=True							,							UpperCamelCase_ :       List[str]=[0.5, 0.5, 0.5]							,							UpperCamelCase_ :       Union[str, Any]=[0.5, 0.5, 0.5]							,							UpperCamelCase_ :       str=True							,							)     ->				Dict:
													# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
													SCREAMING_SNAKE_CASE__     :int						    = size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
													SCREAMING_SNAKE_CASE__     :str						    = parent
													SCREAMING_SNAKE_CASE__     :Tuple						    = batch_size
													SCREAMING_SNAKE_CASE__     :int						    = num_channels
													SCREAMING_SNAKE_CASE__     :Dict						    = min_resolution
													SCREAMING_SNAKE_CASE__     :Tuple						    = max_resolution
													SCREAMING_SNAKE_CASE__     :Dict						    = do_resize
													SCREAMING_SNAKE_CASE__     :int						    = size
													SCREAMING_SNAKE_CASE__     :List[str]						    = do_rescale
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = rescale_factor
													SCREAMING_SNAKE_CASE__     :List[Any]						    = do_normalize
													SCREAMING_SNAKE_CASE__     :Tuple						    = image_mean
													SCREAMING_SNAKE_CASE__     :Tuple						    = image_std
													SCREAMING_SNAKE_CASE__     :List[str]						    = do_pad




						def      __lowerCamelCase       (    self :       Dict      )     ->				Tuple:
													return {
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "do_rescale": self.do_rescale,
													    "rescale_factor": self.rescale_factor,
													    "do_normalize": self.do_normalize,
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_pad": self.do_pad,
													}




						def      __lowerCamelCase       (    self :       Any							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Dict=False      )     ->				Optional[Any]:
													if not batched:
																				SCREAMING_SNAKE_CASE__     :Any						    = image_inputs[0]
																				if isinstance(UpperCamelCase_							,							Image.Image      ):
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[str]						    = image.size
																				else:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[int]						    = image.shape[1], image.shape[2]
																				if w < h:
																											SCREAMING_SNAKE_CASE__     :Any						    = int(self.size['shortest_edge'] * h / w      )
																											SCREAMING_SNAKE_CASE__     :List[str]						    = self.size['shortest_edge']
																				elif w > h:
																											SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.size['shortest_edge']
																											SCREAMING_SNAKE_CASE__     :int						    = int(self.size['shortest_edge'] * w / h      )
																				else:
																											SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.size['shortest_edge']
																											SCREAMING_SNAKE_CASE__     :Dict						    = self.size['shortest_edge']

													else:
																				SCREAMING_SNAKE_CASE__     :int						    = []
																				for image in image_inputs:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Tuple						    = self.get_expected_values([image]      )
																											expected_values.append((expected_height, expected_width)      )
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = max(UpperCamelCase_							,							key=lambda UpperCamelCase_      : item[0]      )[0]
																				SCREAMING_SNAKE_CASE__     :Dict						    = max(UpperCamelCase_							,							key=lambda UpperCamelCase_      : item[1]      )[1]

													return expected_height, expected_width




@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							Any     		= DetrImageProcessor if is_vision_available() else None




						def      __lowerCamelCase       (    self :       Tuple      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Any						    = DetrImageProcessingTester(self      )




						@property
						def      __lowerCamelCase       (    self :       List[str]      )     ->				str:
													return self.image_processor_tester.prepare_image_processor_dict()




						def      __lowerCamelCase       (    self :       Any      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.image_processing_class(**self.image_processor_dict      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'image_mean'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'image_std'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'do_normalize'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'do_rescale'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'rescale_factor'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'do_resize'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'size'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'do_pad'      )      )




						def      __lowerCamelCase       (    self :       Dict      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.image_processing_class.from_dict(self.image_processor_dict      )
													self.assertEqual(image_processor.size							,							{'shortest_edge': 18, 'longest_edge': 13_33}      )
													self.assertEqual(image_processor.do_pad							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :List[str]						    = self.image_processing_class.from_dict(
													    self.image_processor_dict							,							size=42							,							max_size=84							,							pad_and_return_pixel_mask=UpperCamelCase_      )
													self.assertEqual(image_processor.size							,							{'shortest_edge': 42, 'longest_edge': 84}      )
													self.assertEqual(image_processor.do_pad							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       str      )     ->				Dict:
													pass




						def      __lowerCamelCase       (    self :       Dict      )     ->				int:
													# Initialize image_processing
													SCREAMING_SNAKE_CASE__     :Dict						    = self.image_processing_class(**self.image_processor_dict      )
													# create random PIL images
													SCREAMING_SNAKE_CASE__     :str						    = prepare_image_inputs(self.image_processor_tester							,							equal_resolution=UpperCamelCase_      )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase_							,							Image.Image      )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :Tuple						    = image_processing(image_inputs[0]							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )

													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Tuple						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :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,
													        expected_height,
													        expected_width,
													    )							,							)




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Optional[Any]:
													# Initialize image_processing
													SCREAMING_SNAKE_CASE__     :str						    = self.image_processing_class(**self.image_processor_dict      )
													# create random numpy tensors
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 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
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = image_processing(image_inputs[0]							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[Any]						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )

													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :Any						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )

													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Dict:
													# Initialize image_processing
													SCREAMING_SNAKE_CASE__     :List[str]						    = self.image_processing_class(**self.image_processor_dict      )
													# create random PyTorch tensors
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 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
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = image_processing(image_inputs[0]							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Dict						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )

													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :List[Any]						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )

													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)




						@slow
						def      __lowerCamelCase       (    self :       str      )     ->				Optional[Any]:
													# prepare image and target
													SCREAMING_SNAKE_CASE__     :Any						    = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
													with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'							,							'r'      ) as f:
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = json.loads(f.read()      )

													SCREAMING_SNAKE_CASE__     :Any						    = {'image_id': 3_97_69, 'annotations': target}

													# encode them
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = image_processing(images=UpperCamelCase_							,							annotations=UpperCamelCase_							,							return_tensors='pt'      )

													# verify pixel values
													SCREAMING_SNAKE_CASE__     :List[str]						    = torch.Size([1, 3, 8_00, 10_66]      )
													self.assertEqual(encoding['pixel_values'].shape							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Dict						    = torch.tensor([0.2796, 0.3138, 0.3481]      )
													self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]							,							UpperCamelCase_							,							atol=1e-4      )      )

													# verify area
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]      )
													self.assertTrue(torch.allclose(encoding['labels'][0]['area']							,							UpperCamelCase_      )      )
													# verify boxes
													SCREAMING_SNAKE_CASE__     :Tuple						    = torch.Size([6, 4]      )
													self.assertEqual(encoding['labels'][0]['boxes'].shape							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]      )
													self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]							,							UpperCamelCase_							,							atol=1e-3      )      )
													# verify image_id
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = torch.tensor([3_97_69]      )
													self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']							,							UpperCamelCase_      )      )
													# verify is_crowd
													SCREAMING_SNAKE_CASE__     :List[str]						    = torch.tensor([0, 0, 0, 0, 0, 0]      )
													self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']							,							UpperCamelCase_      )      )
													# verify class_labels
													SCREAMING_SNAKE_CASE__     :List[str]						    = torch.tensor([75, 75, 63, 65, 17, 17]      )
													self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']							,							UpperCamelCase_      )      )
													# verify orig_size
													SCREAMING_SNAKE_CASE__     :List[str]						    = torch.tensor([4_80, 6_40]      )
													self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']							,							UpperCamelCase_      )      )
													# verify size
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = torch.tensor([8_00, 10_66]      )
													self.assertTrue(torch.allclose(encoding['labels'][0]['size']							,							UpperCamelCase_      )      )




						@slow
						def      __lowerCamelCase       (    self :       str      )     ->				Optional[int]:
													# prepare image, target and masks_path
													SCREAMING_SNAKE_CASE__     :List[str]						    = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
													with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'							,							'r'      ) as f:
																				SCREAMING_SNAKE_CASE__     :List[str]						    = json.loads(f.read()      )

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

													SCREAMING_SNAKE_CASE__     :str						    = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'      )

													# encode them
													SCREAMING_SNAKE_CASE__     :List[Any]						    = DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'      )
													SCREAMING_SNAKE_CASE__     :Any						    = image_processing(images=UpperCamelCase_							,							annotations=UpperCamelCase_							,							masks_path=UpperCamelCase_							,							return_tensors='pt'      )

													# verify pixel values
													SCREAMING_SNAKE_CASE__     :Tuple						    = torch.Size([1, 3, 8_00, 10_66]      )
													self.assertEqual(encoding['pixel_values'].shape							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = torch.tensor([0.2796, 0.3138, 0.3481]      )
													self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]							,							UpperCamelCase_							,							atol=1e-4      )      )

													# verify area
													SCREAMING_SNAKE_CASE__     :List[str]						    = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]      )
													self.assertTrue(torch.allclose(encoding['labels'][0]['area']							,							UpperCamelCase_      )      )
													# verify boxes
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.Size([6, 4]      )
													self.assertEqual(encoding['labels'][0]['boxes'].shape							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]      )
													self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]							,							UpperCamelCase_							,							atol=1e-3      )      )
													# verify image_id
													SCREAMING_SNAKE_CASE__     :Tuple						    = torch.tensor([3_97_69]      )
													self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']							,							UpperCamelCase_      )      )
													# verify is_crowd
													SCREAMING_SNAKE_CASE__     :List[str]						    = torch.tensor([0, 0, 0, 0, 0, 0]      )
													self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']							,							UpperCamelCase_      )      )
													# verify class_labels
													SCREAMING_SNAKE_CASE__     :int						    = torch.tensor([17, 17, 63, 75, 75, 93]      )
													self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']							,							UpperCamelCase_      )      )
													# verify masks
													SCREAMING_SNAKE_CASE__     :Dict						    = 82_28_73
													self.assertEqual(encoding['labels'][0]['masks'].sum().item()							,							UpperCamelCase_      )
													# verify orig_size
													SCREAMING_SNAKE_CASE__     :Any						    = torch.tensor([4_80, 6_40]      )
													self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']							,							UpperCamelCase_      )      )
													# verify size
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.tensor([8_00, 10_66]      )
													self.assertTrue(torch.allclose(encoding['labels'][0]['size']							,							UpperCamelCase_      )      )







'''simple docstring'''







import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase_      	=	get_tests_dir('''fixtures/test_sentencepiece.model''')
UpperCamelCase_      	=	get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

UpperCamelCase_      	=	'''pt''' if is_torch_available() else '''tf'''




@require_sentencepiece
@require_tokenizers
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							Tuple     		= CamembertTokenizer
						A_			:							Dict     		= CamembertTokenizerFast
						A_			:							Any     		= True
						A_			:							List[Any]     		= True




						def      __lowerCamelCase       (    self :       int      )     ->				Tuple:
													super().setUp()

													# We have a SentencePiece fixture for testing
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = CamembertTokenizer(UpperCamelCase_      )
													tokenizer.save_pretrained(self.tmpdirname      )




						def      __lowerCamelCase       (    self :       int      )     ->				int:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = '<pad>'
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 1

													self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase_      )							,							UpperCamelCase_      )
													self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase_      )							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       int      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Any						    = list(self.get_tokenizer().get_vocab().keys()      )

													self.assertEqual(vocab_keys[0]							,							'<s>NOTUSED'      )
													self.assertEqual(vocab_keys[1]							,							'<pad>'      )
													self.assertEqual(vocab_keys[-1]							,							'<mask>'      )
													self.assertEqual(len(UpperCamelCase_      )							,							10_04      )




						def      __lowerCamelCase       (    self :       List[str]      )     ->				List[str]:
													self.assertEqual(self.get_tokenizer().vocab_size							,							10_05      )




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Any						    = CamembertTokenizer(UpperCamelCase_      )
													tokenizer.save_pretrained(self.tmpdirname      )
													SCREAMING_SNAKE_CASE__     :Dict						    = CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 'I was born in 92000, and this is falsé.'

													SCREAMING_SNAKE_CASE__     :List[str]						    = tokenizer.encode(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = rust_tokenizer.encode(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :int						    = tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = rust_tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													# <unk> tokens are not the same for `rust` than for `slow`.
													# Because spm gives back raw token instead of `unk` in EncodeAsPieces
													# tokens = tokenizer.tokenize(sequence)
													SCREAMING_SNAKE_CASE__     :int						    = tokenizer.convert_ids_to_tokens(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = rust_tokenizer.tokenize(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				List[str]:
													if not self.test_rust_tokenizer:
																				return

													SCREAMING_SNAKE_CASE__     :List[str]						    = self.get_tokenizer()
													SCREAMING_SNAKE_CASE__     :Dict						    = self.get_rust_tokenizer()

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 'I was born in 92000, and this is falsé.'

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = tokenizer.tokenize(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = rust_tokenizer.tokenize(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = rust_tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :str						    = self.get_rust_tokenizer()
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = tokenizer.encode(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = rust_tokenizer.encode(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )




						@slow
						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Any:
													# fmt: off
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = {'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
													# fmt: on

													# camembert is a french model. So we also use french texts.
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = [
													    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
													    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
													    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
													    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
													    'telles que la traduction et la synthèse de texte.',
													]

													self.tokenizer_integration_test_util(
													    expected_encoding=UpperCamelCase_							,							model_name='camembert-base'							,							revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'							,							sequences=UpperCamelCase_							,							)






