






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 		_snake_case  :






   def __init__(       self		,					a		,					a=13		,					a=30		,					a=2		,					a=3		,					a=True		,					a=True		,					a=32		,					a=5		,					a=4		,					a=37		,					a="gelu"		,					a=0.1		,					a=0.1		,					a=10		,					a=0.02		,					a=None		,					)      ->       Any:
      SCREAMING_SNAKE_CASE						= parent
      SCREAMING_SNAKE_CASE						= batch_size
      SCREAMING_SNAKE_CASE						= image_size
      SCREAMING_SNAKE_CASE						= patch_size
      SCREAMING_SNAKE_CASE						= num_channels
      SCREAMING_SNAKE_CASE						= is_training
      SCREAMING_SNAKE_CASE						= use_labels
      SCREAMING_SNAKE_CASE						= hidden_size
      SCREAMING_SNAKE_CASE						= num_hidden_layers
      SCREAMING_SNAKE_CASE						= num_attention_heads
      SCREAMING_SNAKE_CASE						= intermediate_size
      SCREAMING_SNAKE_CASE						= hidden_act
      SCREAMING_SNAKE_CASE						= hidden_dropout_prob
      SCREAMING_SNAKE_CASE						= attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE						= type_sequence_label_size
      SCREAMING_SNAKE_CASE						= initializer_range
      SCREAMING_SNAKE_CASE						= scope

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






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
      SCREAMING_SNAKE_CASE						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

      SCREAMING_SNAKE_CASE						= None
      if self.use_labels:
         SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size]		,					self.type_sequence_label_size)

      SCREAMING_SNAKE_CASE						= self.get_config()

      return config, pixel_values, labels






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
      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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       Union[str, Any]:
      SCREAMING_SNAKE_CASE						= ViTMSNModel(config=_A)
      model.to(_A)
      model.eval()
      SCREAMING_SNAKE_CASE						= model(_A)
      self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size))






   def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       Any:
      SCREAMING_SNAKE_CASE						= self.type_sequence_label_size
      SCREAMING_SNAKE_CASE						= ViTMSNForImageClassification(_A)
      model.to(_A)
      model.eval()
      SCREAMING_SNAKE_CASE						= model(_A		,					labels=_A)
      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
      SCREAMING_SNAKE_CASE						= 1
      SCREAMING_SNAKE_CASE						= ViTMSNForImageClassification(_A)
      model.to(_A)
      model.eval()

      SCREAMING_SNAKE_CASE						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
      SCREAMING_SNAKE_CASE						= model(_A)
      self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.type_sequence_label_size))







   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
      SCREAMING_SNAKE_CASE						= self.prepare_config_and_inputs()
      SCREAMING_SNAKE_CASE						= config_and_inputs
      SCREAMING_SNAKE_CASE						= {'pixel_values': pixel_values}
      return config, inputs_dict



@require_torch
class 		_snake_case  (  a__   ,    a__   ,    unittest.TestCase					):
   _lowercase		:    int			     =							(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
   _lowercase		:    int			     =							(
       {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
       if is_torch_available()
       else {}
   )

   _lowercase		:    List[Any]			     =							False
   _lowercase		:    Any			     =							False
   _lowercase		:    List[Any]			     =							False
   _lowercase		:    Optional[Any]			     =							False






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
      SCREAMING_SNAKE_CASE						= ViTMSNModelTester(self)
      SCREAMING_SNAKE_CASE						= ConfigTester(self		,					config_class=_A		,					has_text_modality=_A		,					hidden_size=37)






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
      self.config_tester.run_common_tests()






   @unittest.skip(reason='ViTMSN does not use inputs_embeds')
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
      pass






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE						= model_class(_A)
         self.assertIsInstance(model.get_input_embeddings()		,					(nn.Module))
         SCREAMING_SNAKE_CASE						= model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(_A		,					nn.Linear))






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE						= model_class(_A)
         SCREAMING_SNAKE_CASE						= inspect.signature(model.forward)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         SCREAMING_SNAKE_CASE						= [*signature.parameters.keys()]

         SCREAMING_SNAKE_CASE						= ['pixel_values']
         self.assertListEqual(arg_names[:1]		,					_A)






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_A)






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_A)







   @slow
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:

      for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         SCREAMING_SNAKE_CASE						= ViTMSNModel.from_pretrained(_A)
         self.assertIsNotNone(_A)






def 				lowerCamelCase__				():
   SCREAMING_SNAKE_CASE						= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
   return image







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






   @cached_property
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
      return ViTImageProcessor.from_pretrained('facebook/vit-msn-small') if is_vision_available() else None







   @slow
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
      torch.manual_seed(2)
      SCREAMING_SNAKE_CASE						= ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small').to(_A)

      SCREAMING_SNAKE_CASE						= self.default_image_processor
      SCREAMING_SNAKE_CASE						= prepare_img()
      SCREAMING_SNAKE_CASE						= image_processor(images=_A		,					return_tensors='pt').to(_A)

      # forward pass
      with torch.no_grad():
         SCREAMING_SNAKE_CASE						= model(**_A)

      # verify the logits
      SCREAMING_SNAKE_CASE						= torch.Size((1, 1000))
      self.assertEqual(outputs.logits.shape		,					_A)

      SCREAMING_SNAKE_CASE						= torch.tensor([-0.08_03, -0.44_54, -0.23_75]).to(_A)

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_		:						List[Any]             =    {
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Dict             =    [
				    'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BigBirdPegasusForCausalLM',
				    'BigBirdPegasusForConditionalGeneration',
				    'BigBirdPegasusForQuestionAnswering',
				    'BigBirdPegasusForSequenceClassification',
				    'BigBirdPegasusModel',
				    'BigBirdPegasusPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_bigbird_pegasus import (
				    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BigBirdPegasusConfig,
				    BigBirdPegasusOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bigbird_pegasus import (
								    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BigBirdPegasusForCausalLM,
								    BigBirdPegasusForConditionalGeneration,
								    BigBirdPegasusForQuestionAnswering,
								    BigBirdPegasusForSequenceClassification,
								    BigBirdPegasusModel,
								    BigBirdPegasusPreTrainedModel,
								)


else:
				import sys

				a_		:						Tuple             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




