




"""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


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

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


@require_sentencepiece
@require_tokenizers
class     __lowerCamelCase  (					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	Dict    =		CamembertTokenizer
     A_       :	int    =		CamembertTokenizerFast
     A_       :	int    =		True
     A_       :	int    =		True


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          super().setUp()

          # We have a SentencePiece fixture for testing
          _a    	=				CamembertTokenizer(__UpperCAmelCase							)
          tokenizer.save_pretrained(self.tmpdirname							)


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				'''<pad>'''
          _a    	=				1

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


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				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							)    ,				1004							)


     def 							_UpperCAmelCase      (      self							)       ->							int:
          self.assertEqual(self.get_tokenizer().vocab_size    ,				1005							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				CamembertTokenizer(__UpperCAmelCase							)
          tokenizer.save_pretrained(self.tmpdirname							)
          _a    	=				CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

          _a    	=				'''I was born in 92000, and this is falsé.'''

          _a    	=				tokenizer.encode(__UpperCAmelCase							)
          _a    	=				rust_tokenizer.encode(__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)

          _a    	=				tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase							)
          _a    	=				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)
          _a    	=				tokenizer.convert_ids_to_tokens(__UpperCAmelCase							)
          _a    	=				rust_tokenizer.tokenize(__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          if not self.test_rust_tokenizer:
               return

          _a    	=				self.get_tokenizer()
          _a    	=				self.get_rust_tokenizer()

          _a    	=				'''I was born in 92000, and this is falsé.'''

          _a    	=				tokenizer.tokenize(__UpperCAmelCase							)
          _a    	=				rust_tokenizer.tokenize(__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)

          _a    	=				tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase							)
          _a    	=				rust_tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)

          _a    	=				self.get_rust_tokenizer()
          _a    	=				tokenizer.encode(__UpperCAmelCase							)
          _a    	=				rust_tokenizer.encode(__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							int:
          # fmt: off
          _a    	=				{'''input_ids''': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 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.
          _a    	=				[
              '''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    ,				)



"""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 DeformableDetrImageProcessor


class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=7    ,				__UpperCAmelCase=3    ,				__UpperCAmelCase=30    ,				__UpperCAmelCase=400    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=[0.5, 0.5, 0.5]    ,				__UpperCAmelCase=[0.5, 0.5, 0.5]    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=1 / 255    ,				__UpperCAmelCase=True    ,				)       ->							Optional[int]:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          _a    	=				size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
          _a    	=				parent
          _a    	=				batch_size
          _a    	=				num_channels
          _a    	=				min_resolution
          _a    	=				max_resolution
          _a    	=				do_resize
          _a    	=				size
          _a    	=				do_normalize
          _a    	=				image_mean
          _a    	=				image_std
          _a    	=				do_rescale
          _a    	=				rescale_factor
          _a    	=				do_pad


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, 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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=False							)       ->							List[str]:
          if not batched:
               _a    	=				image_inputs[0]
               if isinstance(__UpperCAmelCase    ,				Image.Image							):
                    _a	,      _a    	=				image.size
               else:
                    _a	,      _a    	=				image.shape[1], image.shape[2]
               if w < h:
                    _a    	=				int(self.size['''shortest_edge'''] * h / w							)
                    _a    	=				self.size['''shortest_edge''']
               elif w > h:
                    _a    	=				self.size['''shortest_edge''']
                    _a    	=				int(self.size['''shortest_edge'''] * w / h							)
               else:
                    _a    	=				self.size['''shortest_edge''']
                    _a    	=				self.size['''shortest_edge''']

          else:
               _a    	=				[]
               for image in image_inputs:
                    _a	,      _a    	=				self.get_expected_values([image]							)
                    expected_values.append((expected_height, expected_width)							)
               _a    	=				max(__UpperCAmelCase    ,				key=lambda __UpperCAmelCase							: item[0]							)[0]
               _a    	=				max(__UpperCAmelCase    ,				key=lambda __UpperCAmelCase							: item[1]							)[1]

          return expected_height, expected_width







@require_torch
@require_vision
class     __lowerCamelCase  (					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	List[str]    =		DeformableDetrImageProcessor if is_vision_available() else None


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				DeformableDetrImageProcessingTester(self							)


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


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				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_resize'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''do_rescale'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''do_pad'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''size'''							)							)


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				self.image_processing_class.from_dict(self.image_processor_dict							)
          self.assertEqual(image_processor.size    ,				{'''shortest_edge''': 18, '''longest_edge''': 1333}							)
          self.assertEqual(image_processor.do_pad    ,				__UpperCAmelCase							)

          _a    	=				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 							_UpperCAmelCase      (      self							)       ->							int:
          pass


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          # Initialize image_processing
          _a    	=				self.image_processing_class(**self.image_processor_dict							)
          # create random PIL images
          _a    	=				prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=__UpperCAmelCase							)
          for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase    ,				Image.Image							)

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

          _a	,      _a    	=				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
          _a	,      _a    	=				self.image_processor_tester.get_expected_values(__UpperCAmelCase    ,				batched=__UpperCAmelCase							)

          _a    	=				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 							_UpperCAmelCase      (      self							)       ->							int:
          # Initialize image_processing
          _a    	=				self.image_processing_class(**self.image_processor_dict							)
          # create random numpy tensors
          _a    	=				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
          _a    	=				image_processing(image_inputs[0]    ,				return_tensors='''pt'''							).pixel_values

          _a	,      _a    	=				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
          _a    	=				image_processing(__UpperCAmelCase    ,				return_tensors='''pt'''							).pixel_values

          _a	,      _a    	=				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 							_UpperCAmelCase      (      self							)       ->							Dict:
          # Initialize image_processing
          _a    	=				self.image_processing_class(**self.image_processor_dict							)
          # create random PyTorch tensors
          _a    	=				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
          _a    	=				image_processing(image_inputs[0]    ,				return_tensors='''pt'''							).pixel_values

          _a	,      _a    	=				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
          _a    	=				image_processing(__UpperCAmelCase    ,				return_tensors='''pt'''							).pixel_values

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

          _a    	=				{'''image_id''': 39769, '''annotations''': target}

          # encode them
          _a    	=				DeformableDetrImageProcessor()
          _a    	=				image_processing(images=__UpperCAmelCase    ,				annotations=__UpperCAmelCase    ,				return_tensors='''pt'''							)

          # verify pixel values
          _a    	=				torch.Size([1, 3, 800, 1066]							)
          self.assertEqual(encoding['''pixel_values'''].shape    ,				__UpperCAmelCase							)

          _a    	=				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
          _a    	=				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,				__UpperCAmelCase							)							)
          # verify boxes
          _a    	=				torch.Size([6, 4]							)
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,				__UpperCAmelCase							)
          _a    	=				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
          _a    	=				torch.tensor([39769]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,				__UpperCAmelCase							)							)
          # verify is_crowd
          _a    	=				torch.tensor([0, 0, 0, 0, 0, 0]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,				__UpperCAmelCase							)							)
          # verify class_labels
          _a    	=				torch.tensor([75, 75, 63, 65, 17, 17]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,				__UpperCAmelCase							)							)
          # verify orig_size
          _a    	=				torch.tensor([480, 640]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,				__UpperCAmelCase							)							)
          # verify size
          _a    	=				torch.tensor([800, 1066]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,				__UpperCAmelCase							)							)





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

          _a    	=				{'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

          _a    	=				pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''							)

          # encode them
          _a    	=				DeformableDetrImageProcessor(format='''coco_panoptic'''							)
          _a    	=				image_processing(images=__UpperCAmelCase    ,				annotations=__UpperCAmelCase    ,				masks_path=__UpperCAmelCase    ,				return_tensors='''pt'''							)

          # verify pixel values
          _a    	=				torch.Size([1, 3, 800, 1066]							)
          self.assertEqual(encoding['''pixel_values'''].shape    ,				__UpperCAmelCase							)

          _a    	=				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
          _a    	=				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,				__UpperCAmelCase							)							)
          # verify boxes
          _a    	=				torch.Size([6, 4]							)
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,				__UpperCAmelCase							)
          _a    	=				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
          _a    	=				torch.tensor([39769]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,				__UpperCAmelCase							)							)
          # verify is_crowd
          _a    	=				torch.tensor([0, 0, 0, 0, 0, 0]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,				__UpperCAmelCase							)							)
          # verify class_labels
          _a    	=				torch.tensor([17, 17, 63, 75, 75, 93]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,				__UpperCAmelCase							)							)
          # verify masks
          _a    	=				822873
          self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,				__UpperCAmelCase							)
          # verify orig_size
          _a    	=				torch.tensor([480, 640]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,				__UpperCAmelCase							)							)
          # verify size
          _a    	=				torch.tensor([800, 1066]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,				__UpperCAmelCase							)							)