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 ConditionalDetrImageProcessor



class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				List[Any]:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }







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

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

         return expected_height, expected_width







@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     List[Any]            =				ConditionalDetrImageProcessor if is_vision_available() else None







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Tuple:
         A						:     str         =ConditionalDetrImageProcessingTester(self     )







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				int:
         return self.image_processor_tester.prepare_image_processor_dict()







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				Tuple:
         A						:     Tuple         =self.image_processing_class(**self.image_processor_dict     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	'image_mean'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	'image_std'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	'do_normalize'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	'do_resize'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	'size'     )     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				int:
         A						:     int         =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      ,	SCREAMING_SNAKE_CASE__     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Optional[int]:
         pass







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Union[str, Any]:
         # Initialize image_processing
         A						:     Union[str, Any]         =self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         A						:     Tuple         =prepare_image_inputs(self.image_processor_tester      ,	equal_resolution=SCREAMING_SNAKE_CASE__     )
         for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,	Image.Image     )

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

         A  ,  A						:     List[str]         =self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__     )

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

         # Test batched
         A  ,  A						:     Union[str, Any]         =self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__      ,	batched=SCREAMING_SNAKE_CASE__     )

         A						:     Union[str, Any]         =image_processing(SCREAMING_SNAKE_CASE__      ,	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 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				int:
         # Initialize image_processing
         A						:     Optional[Any]         =self.image_processing_class(**self.image_processor_dict     )
         # create random numpy tensors
         A						:     str         =prepare_image_inputs(self.image_processor_tester      ,	equal_resolution=SCREAMING_SNAKE_CASE__      ,	numpify=SCREAMING_SNAKE_CASE__     )
         for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,	np.ndarray     )

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

         A  ,  A						:     Any         =self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__     )

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

         # Test batched
         A						:     Tuple         =image_processing(SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     ).pixel_values

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

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				List[str]:
         # Initialize image_processing
         A						:     Optional[Any]         =self.image_processing_class(**self.image_processor_dict     )
         # create random PyTorch tensors
         A						:     Any         =prepare_image_inputs(self.image_processor_tester      ,	equal_resolution=SCREAMING_SNAKE_CASE__      ,	torchify=SCREAMING_SNAKE_CASE__     )
         for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,	torch.Tensor     )

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

         A  ,  A						:     Tuple         =self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__     )

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

         # Test batched
         A						:     Tuple         =image_processing(SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     ).pixel_values

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

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







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

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

         # encode them
         A						:     str         =ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'     )
         A						:     Any         =image_processing(images=SCREAMING_SNAKE_CASE__      ,	annotations=SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     )

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

         A						:     List[str]         =torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-4     )     )

         # verify area
         A						:     Dict         =torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify boxes
         A						:     str         =torch.Size([6, 4]     )
         self.assertEqual(encoding['labels'][0]['boxes'].shape      ,	SCREAMING_SNAKE_CASE__     )
         A						:     Union[str, Any]         =torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-3     )     )
         # verify image_id
         A						:     Dict         =torch.tensor([3_97_69]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify is_crowd
         A						:     List[str]         =torch.tensor([0, 0, 0, 0, 0, 0]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify class_labels
         A						:     Union[str, Any]         =torch.tensor([75, 75, 63, 65, 17, 17]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify orig_size
         A						:     List[Any]         =torch.tensor([4_80, 6_40]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify size
         A						:     int         =torch.tensor([8_00, 10_66]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,	SCREAMING_SNAKE_CASE__     )     )







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Tuple:
         # prepare image, target and masks_path
         A						:     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:
                A						:     Optional[int]         =json.loads(f.read()     )

         A						:     int         ={'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

         A						:     Optional[Any]         =pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'     )

         # encode them
         A						:     List[Any]         =ConditionalDetrImageProcessor(format='coco_panoptic'     )
         A						:     Union[str, Any]         =image_processing(images=SCREAMING_SNAKE_CASE__      ,	annotations=SCREAMING_SNAKE_CASE__      ,	masks_path=SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     )

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

         A						:     Dict         =torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-4     )     )

         # verify area
         A						:     Optional[int]         =torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify boxes
         A						:     List[Any]         =torch.Size([6, 4]     )
         self.assertEqual(encoding['labels'][0]['boxes'].shape      ,	SCREAMING_SNAKE_CASE__     )
         A						:     Any         =torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-3     )     )
         # verify image_id
         A						:     List[Any]         =torch.tensor([3_97_69]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify is_crowd
         A						:     Any         =torch.tensor([0, 0, 0, 0, 0, 0]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify class_labels
         A						:     str         =torch.tensor([17, 17, 63, 75, 75, 93]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify masks
         A						:     int         =82_28_73
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,	SCREAMING_SNAKE_CASE__     )
         # verify orig_size
         A						:     Any         =torch.tensor([4_80, 6_40]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify size
         A						:     str         =torch.tensor([8_00, 10_66]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,	SCREAMING_SNAKE_CASE__     )     )







import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase							:      Optional[int]      							=get_tests_dir('''fixtures/test_sentencepiece.model''')



@require_sentencepiece
@require_tokenizers
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     List[str]            =				XLMRobertaTokenizer
  lowercase		:     Dict            =				XLMRobertaTokenizerFast
  lowercase		:     str            =				True
  lowercase		:     Tuple            =				True







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Optional[Any]:
         super().setUp()

         # We have a SentencePiece fixture for testing
         A						:     List[str]         =XLMRobertaTokenizer(SCREAMING_SNAKE_CASE__      ,	keep_accents=SCREAMING_SNAKE_CASE__     )
         tokenizer.save_pretrained(self.tmpdirname     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				List[Any]:
         A						:     List[str]         ='<pad>'
         A						:     int         =1

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Any:
         A						:     List[str]         =list(self.get_tokenizer().get_vocab().keys()     )

         self.assertEqual(vocab_keys[0]      ,	'<s>'     )
         self.assertEqual(vocab_keys[1]      ,	'<pad>'     )
         self.assertEqual(vocab_keys[-1]      ,	'<mask>'     )
         self.assertEqual(len(SCREAMING_SNAKE_CASE__     )      ,	10_02     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Dict:
         self.assertEqual(self.get_tokenizer().vocab_size      ,	10_02     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				str:
         A						:     Union[str, Any]         =XLMRobertaTokenizer(SCREAMING_SNAKE_CASE__      ,	keep_accents=SCREAMING_SNAKE_CASE__     )

         A						:     str         =tokenizer.tokenize('This is a test'     )
         self.assertListEqual(SCREAMING_SNAKE_CASE__      ,	['▁This', '▁is', '▁a', '▁t', 'est']     )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )      ,	[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]      ,	)

         A						:     Any         =tokenizer.tokenize('I was born in 92000, and this is falsé.'     )
         self.assertListEqual(
             SCREAMING_SNAKE_CASE__      ,	[
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '9',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 'é',
                 '.',
             ]      ,	)
         A						:     Tuple         =tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )
         self.assertListEqual(
             SCREAMING_SNAKE_CASE__      ,	[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                 #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
             ]      ,	)

         A						:     Union[str, Any]         =tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__     )
         self.assertListEqual(
             SCREAMING_SNAKE_CASE__      ,	[
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '<unk>',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 '<unk>',
                 '.',
             ]      ,	)







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				Optional[int]:
         if not self.test_slow_tokenizer:
                # as we don't have a slow version, we can't compare the outputs between slow and fast versions
                return

         A						:     Any         =(self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
                       A						:     List[Any]         =self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )
                       A						:     Dict         =self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )

                       A						:     str         =tempfile.mkdtemp()

                       A						:     Optional[int]         =tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__     )
                       A						:     Optional[Any]         =tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__     )

                       # Checks it save with the same files + the tokenizer.json file for the fast one
                       self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files     )     )
                       A						:     List[str]         =tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f     )
                       self.assertSequenceEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

                       # Checks everything loads correctly in the same way
                       A						:     Tuple         =tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__     )
                       A						:     Dict         =tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__     )

                       # Check special tokens are set accordingly on Rust and Python
                       for key in tokenizer_pp.special_tokens_map:
                              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )     )
                              # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                              # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                       shutil.rmtree(SCREAMING_SNAKE_CASE__     )

                       # Save tokenizer rust, legacy_format=True
                       A						:     Optional[int]         =tempfile.mkdtemp()

                       A						:     Optional[int]         =tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__      ,	legacy_format=SCREAMING_SNAKE_CASE__     )
                       A						:     List[Any]         =tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__     )

                       # Checks it save with the same files
                       self.assertSequenceEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

                       # Checks everything loads correctly in the same way
                       A						:     Tuple         =tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__     )
                       A						:     Tuple         =tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__     )

                       # Check special tokens are set accordingly on Rust and Python
                       for key in tokenizer_pp.special_tokens_map:
                              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )     )

                       shutil.rmtree(SCREAMING_SNAKE_CASE__     )

                       # Save tokenizer rust, legacy_format=False
                       A						:     List[Any]         =tempfile.mkdtemp()

                       A						:     Optional[int]         =tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__      ,	legacy_format=SCREAMING_SNAKE_CASE__     )
                       A						:     str         =tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__     )

                       # Checks it saved the tokenizer.json file
                       self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files     )     )

                       # Checks everything loads correctly in the same way
                       A						:     List[Any]         =tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__     )
                       A						:     List[Any]         =tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__     )

                       # Check special tokens are set accordingly on Rust and Python
                       for key in tokenizer_pp.special_tokens_map:
                              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )     )

                       shutil.rmtree(SCREAMING_SNAKE_CASE__     )







  @cached_property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Optional[int]:
         return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Any:
         with tempfile.NamedTemporaryFile() as f:
                shutil.copyfile(SCREAMING_SNAKE_CASE__      ,	f.name     )
                A						:     Optional[Any]         =XLMRobertaTokenizer(f.name      ,	keep_accents=SCREAMING_SNAKE_CASE__     )
                A						:     int         =pickle.dumps(SCREAMING_SNAKE_CASE__     )
         pickle.loads(SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				Union[str, Any]:
         if not self.test_rust_tokenizer:
                return

         A						:     Union[str, Any]         =self.get_tokenizer()
         A						:     int         =self.get_rust_tokenizer()

         A						:     List[str]         ='I was born in 92000, and this is falsé.'

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

         A						:     Any         =tokenizer.encode(SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__     )
         A						:     Tuple         =rust_tokenizer.encode(SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__     )
         self.assertListEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         A						:     Optional[Any]         =self.get_rust_tokenizer()
         A						:     int         =tokenizer.encode(SCREAMING_SNAKE_CASE__     )
         A						:     Dict         =rust_tokenizer.encode(SCREAMING_SNAKE_CASE__     )
         self.assertListEqual(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				List[str]:
         A						:     Any         ='Hello World!'
         A						:     Optional[Any]         =[0, 3_53_78, 66_61, 38, 2]
         # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
         # xlmr.eval()
         # xlmr.encode(symbols)

         self.assertListEqual(SCREAMING_SNAKE_CASE__      ,	self.big_tokenizer.encode(SCREAMING_SNAKE_CASE__     )     )







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				str:
         A						:     Any         =(
             'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
             ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
         )
         A						:     int         =[
             0,
             32_93,
             83,
             10,
             45_52,
             49_89,
             79_86,
             6_78,
             10,
             59_15,
             1_11,
             17_94_59,
             12_48_50,
             4,
             60_44,
             2_37,
             12,
             6,
             5,
             6,
             4,
             67_80,
             7_05,
             15,
             13_88,
             44,
             3_78,
             1_01_14,
             7_11,
             1_52,
             20,
             6,
             5,
             2_23_76,
             6_42,
             12_21,
             1_51_90,
             3_41_53,
             4_50,
             56_08,
             9_59,
             11_19,
             5_77_02,
             1_36,
             1_86,
             47,
             10_98,
             2_93_67,
             47,
             # 4426, # What fairseq tokenizes from "<unk>": "_<"
             # 3678, # What fairseq tokenizes from "<unk>": "unk"
             # 2740, # What fairseq tokenizes from "<unk>": ">"
             3,  # What we tokenize from "<unk>": "<unk>"
             6,  # Residue from the tokenization: an extra sentencepiece underline
             4,
             60_44,
             2_37,
             62_84,
             5_09_01,
             5_28,
             31,
             90,
             34,
             9_27,
             2,
         ]
         # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
         # xlmr.eval()
         # xlmr.encode(symbols)

         self.assertListEqual(SCREAMING_SNAKE_CASE__      ,	self.big_tokenizer.encode(SCREAMING_SNAKE_CASE__     )     )







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				Any:
         # fmt: off
         A						:     List[Any]         ={'input_ids': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 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, 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]], '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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=SCREAMING_SNAKE_CASE__      ,	model_name='xlm-roberta-base'      ,	revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'      ,	)






