





import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase      :       int									=  """▁"""

lowercase      :       Optional[Any]									=  get_tests_dir("""fixtures/test_sentencepiece.model""")





@require_sentencepiece
@require_tokenizers
class  A__    (							__UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				List[str]         =  BigBirdTokenizer
   __A   :				Tuple         =  BigBirdTokenizerFast
   __A   :				int         =  True
   __A   :				List[str]         =  True

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          super().setUp()

          a__						:	int =				self.tokenizer_class(lowercase					,  keep_accents=lowercase)
          tokenizer.save_pretrained(self.tmpdirname)

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	List[str] =				'<s>'
          a__						:	List[Any] =				1

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

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Tuple =				list(self.get_tokenizer().get_vocab().keys())

          self.assertEqual(vocab_keys[0]					,  '<unk>')
          self.assertEqual(vocab_keys[1]					,  '<s>')
          self.assertEqual(vocab_keys[-1]					,  '[MASK]')
          self.assertEqual(len(lowercase)					,  1004)

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          self.assertEqual(self.get_tokenizer().vocab_size					,  1000)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          if not self.test_rust_tokenizer:
                 return

          a__						:	Optional[Any] =				self.get_tokenizer()
          a__						:	List[Any] =				self.get_rust_tokenizer()

          a__						:	Dict =				'I was born in 92000, and this is falsé.'

          a__						:	Optional[int] =				tokenizer.tokenize(lowercase)
          a__						:	Tuple =				rust_tokenizer.tokenize(lowercase)
          self.assertListEqual(lowercase					,  lowercase)

          a__						:	Union[str, Any] =				tokenizer.encode(lowercase					,  add_special_tokens=lowercase)
          a__						:	Tuple =				rust_tokenizer.encode(lowercase					,  add_special_tokens=lowercase)
          self.assertListEqual(lowercase					,  lowercase)

          a__						:	List[Any] =				self.get_rust_tokenizer()
          a__						:	str =				tokenizer.encode(lowercase)
          a__						:	Dict =				rust_tokenizer.encode(lowercase)
          self.assertListEqual(lowercase					,  lowercase)

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	List[Any] =				BigBirdTokenizer(lowercase					,  keep_accents=lowercase)

          a__						:	Any =				tokenizer.tokenize('This is a test')
          self.assertListEqual(lowercase					,  ['▁This', '▁is', '▁a', '▁t', 'est'])

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(lowercase)					,  [285, 46, 10, 170, 382]					,  )

          a__						:	Tuple =				tokenizer.tokenize('I was born in 92000, and this is falsé.')
          self.assertListEqual(
              lowercase					,  [
                  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__						:	Optional[int] =				tokenizer.convert_tokens_to_ids(lowercase)
          self.assertListEqual(
              lowercase					,  [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]					,  )

          a__						:	int =				tokenizer.convert_ids_to_tokens(lowercase)
          self.assertListEqual(
              lowercase					,  [
                  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>',
                  '.',
              ]					,  )

   @cached_property
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')

   @slow
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	List[str] =				'Hello World!'
          a__						:	Dict =				[65, 1_8536, 2260, 101, 66]

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

   @slow
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          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'
          )
          # fmt: off
          a__						:	Optional[Any] =				[65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 3_4324, 497, 391, 408, 1_1342, 1244, 385, 100, 938, 985, 456, 574, 362, 1_2597, 3200, 3129, 1172, 66]  # noqa: E231
          # fmt: on
          self.assertListEqual(lowercase					,  self.big_tokenizer.encode(lowercase))

   @require_torch
   @slow
   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          import torch

          from transformers import BigBirdConfig, BigBirdModel

          # Build sequence
          a__						:	Tuple =				list(self.big_tokenizer.get_vocab().keys())[:10]
          a__						:	Optional[Any] =				' '.join(lowercase)
          a__						:	Optional[Any] =				self.big_tokenizer.encode_plus(lowercase					,  return_tensors='pt'					,  return_token_type_ids=lowercase)
          a__						:	List[str] =				self.big_tokenizer.batch_encode_plus(
              [sequence + ' ' + sequence]					,  return_tensors='pt'					,  return_token_type_ids=lowercase)

          a__						:	Dict =				BigBirdConfig(attention_type='original_full')
          a__						:	str =				BigBirdModel(lowercase)

          assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

          with torch.no_grad():
                 model(**lowercase)
                 model(**lowercase)

   @slow
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	List[Any] =				BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')
          a__						:	Dict =				tokenizer.decode(tokenizer('Paris is the [MASK].').input_ids)

          self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]')

   @slow
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          a__						:	Optional[Any] =				{'input_ids': [[65, 3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114, 66], [65, 448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 66, 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], [65, 484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 66, 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]], '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, 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, 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=lowercase					,  model_name='google/bigbird-roberta-base'					,  revision='215c99f1600e06f83acce68422f2035b2b5c3510'					,  )









import inspect
import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTMSNForImageClassification, ViTMSNModel
    from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor





class  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=13					,  lowercase=30					,  lowercase=2					,  lowercase=3					,  lowercase=True					,  lowercase=True					,  lowercase=32					,  lowercase=5					,  lowercase=4					,  lowercase=37					,  lowercase="gelu"					,  lowercase=0.1					,  lowercase=0.1					,  lowercase=10					,  lowercase=0.02					,  lowercase=None					,  )			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Optional[int] =				parent
          a__						:	List[str] =				batch_size
          a__						:	List[str] =				image_size
          a__						:	Dict =				patch_size
          a__						:	Optional[Any] =				num_channels
          a__						:	List[Any] =				is_training
          a__						:	str =				use_labels
          a__						:	Dict =				hidden_size
          a__						:	Tuple =				num_hidden_layers
          a__						:	Tuple =				num_attention_heads
          a__						:	Union[str, Any] =				intermediate_size
          a__						:	List[str] =				hidden_act
          a__						:	List[str] =				hidden_dropout_prob
          a__						:	Any =				attention_probs_dropout_prob
          a__						:	Dict =				type_sequence_label_size
          a__						:	Tuple =				initializer_range
          a__						:	Optional[int] =				scope

          # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          a__						:	List[str] =				(image_size // patch_size) ** 2
          a__						:	Any =				num_patches + 1

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Tuple =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          a__						:	Tuple =				None
          if self.use_labels:
                 a__						:	List[Any] =				ids_tensor([self.batch_size]					,  self.type_sequence_label_size)

          a__						:	List[str] =				self.get_config()

          return config, pixel_values, labels

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          return ViTMSNConfig(
              image_size=self.image_size					,  patch_size=self.patch_size					,  num_channels=self.num_channels					,  hidden_size=self.hidden_size					,  num_hidden_layers=self.num_hidden_layers					,  num_attention_heads=self.num_attention_heads					,  intermediate_size=self.intermediate_size					,  hidden_act=self.hidden_act					,  hidden_dropout_prob=self.hidden_dropout_prob					,  attention_probs_dropout_prob=self.attention_probs_dropout_prob					,  initializer_range=self.initializer_range					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						List[str]:







          '''simple docstring'''

          a__						:	int =				ViTMSNModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	int =				model(lowercase)
          self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Tuple:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.type_sequence_label_size
          a__						:	List[str] =				ViTMSNForImageClassification(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	int =				model(lowercase					,  labels=lowercase)
          print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}')
          print('Labels: {labels}')
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.type_sequence_label_size))

          # test greyscale images
          a__						:	List[str] =				1
          a__						:	Optional[int] =				ViTMSNForImageClassification(lowercase)
          model.to(lowercase)
          model.eval()

          a__						:	Dict =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
          a__						:	Dict =				model(lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.type_sequence_label_size))

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	List[Any] =				self.prepare_config_and_inputs()
          a__ ,							a__ ,							a__						:	Optional[Any] =				config_and_inputs
          a__						:	Dict =				{'pixel_values': pixel_values}
          return config, inputs_dict





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Any         =  (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
   __A   :				Tuple         =  (
       {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
       if is_torch_available()
       else {}
   )

   __A   :				List[str]         =  False
   __A   :				Optional[Any]         =  False
   __A   :				Union[str, Any]         =  False
   __A   :				Any         =  False

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	Optional[int] =				ViTMSNModelTester(self)
          a__						:	Union[str, Any] =				ConfigTester(self					,  config_class=lowercase					,  has_text_modality=lowercase					,  hidden_size=37)

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          self.config_tester.run_common_tests()

   @unittest.skip(reason='ViTMSN does not use inputs_embeds')
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__ ,							a__						:	Tuple =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	Union[str, Any] =				model_class(lowercase)
                 self.assertIsInstance(model.get_input_embeddings()					,  (nn.Module))
                 a__						:	str =				model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(lowercase					,  nn.Linear))

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__ ,							a__						:	Optional[int] =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	Dict =				model_class(lowercase)
                 a__						:	List[Any] =				inspect.signature(model.forward)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 a__						:	str =				[*signature.parameters.keys()]

                 a__						:	Optional[Any] =				['pixel_values']
                 self.assertListEqual(arg_names[:1]					,  lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	List[str] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Any =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase)

   @slow
   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Tuple =				ViTMSNModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)



def A_	(		)  ->		Dict:
       a__						:	List[str] =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image





@require_torch
@require_vision
class  A__    (							unittest.TestCase	):




   """simple docstring"""



   @cached_property
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          return ViTImageProcessor.from_pretrained('facebook/vit-msn-small') if is_vision_available() else None

   @slow
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          torch.manual_seed(2)
          a__						:	List[str] =				ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small').to(lowercase)

          a__						:	Any =				self.default_image_processor
          a__						:	List[Any] =				prepare_img()
          a__						:	Optional[Any] =				image_processor(images=lowercase					,  return_tensors='pt').to(lowercase)

          # forward pass
          with torch.no_grad():
                 a__						:	Tuple =				model(**lowercase)

          # verify the logits
          a__						:	Union[str, Any] =				torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Any =				torch.tensor([-0.08_03, -0.44_54, -0.23_75]).to(lowercase)

          self.assertTrue(torch.allclose(outputs.logits[0, :3]					,  lowercase					,  atol=1e-4))


