


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   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=13				,       _SCREAMING_SNAKE_CASE=30				,       _SCREAMING_SNAKE_CASE=2				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=5				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=37				,       _SCREAMING_SNAKE_CASE="gelu"				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=10				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=None				,       )->List[str]:




         '''simple docstring'''





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

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



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




         '''simple docstring'''





         A_      :				int		     =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

         A_      :				Optional[Any]		     =					None
         if self.use_labels:
           A_      :				Optional[int]		     =					ids_tensor([self.batch_size]				,       self.type_sequence_label_size		)

         A_      :				Optional[int]		     =					self.get_config()

         return config, pixel_values, labels



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




         '''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        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				int		     =					ViTMSNModel(config=_SCREAMING_SNAKE_CASE		)
         model.to(_SCREAMING_SNAKE_CASE		)
         model.eval()
         A_      :				Union[str, Any]		     =					model(_SCREAMING_SNAKE_CASE		)
         self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Dict:




         '''simple docstring'''





         A_      :				List[Any]		     =					self.type_sequence_label_size
         A_      :				Any		     =					ViTMSNForImageClassification(_SCREAMING_SNAKE_CASE		)
         model.to(_SCREAMING_SNAKE_CASE		)
         model.eval()
         A_      :				Union[str, Any]		     =					model(_SCREAMING_SNAKE_CASE				,       labels=_SCREAMING_SNAKE_CASE		)
         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_      :				int		     =					ViTMSNForImageClassification(_SCREAMING_SNAKE_CASE		)
         model.to(_SCREAMING_SNAKE_CASE		)
         model.eval()

         A_      :				Union[str, Any]		     =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
         A_      :				int		     =					model(_SCREAMING_SNAKE_CASE		)
         self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.type_sequence_label_size)		)







       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





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






@require_torch
class   _lowerCamelCase (					UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
       snake_case      =    (
           {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
           if is_torch_available()
           else {}
       )

       snake_case      =    False
       snake_case      =    False
       snake_case      =    False
       snake_case      =    False



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         A_      :				Optional[int]		     =					ViTMSNModelTester(self		)
         A_      :				Any		     =					ConfigTester(self				,       config_class=_SCREAMING_SNAKE_CASE				,       has_text_modality=_SCREAMING_SNAKE_CASE				,       hidden_size=37		)



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




         '''simple docstring'''





         self.config_tester.run_common_tests()



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




         '''simple docstring'''





         pass



       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_     ,       A_      :				Union[str, Any]		     =					self.model_tester.prepare_config_and_inputs_for_common()

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



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




         '''simple docstring'''





         A_     ,       A_      :				str		     =					self.model_tester.prepare_config_and_inputs_for_common()

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

           A_      :				List[Any]		     =					['''pixel_values''']
           self.assertListEqual(arg_names[:1]				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         A_      :				List[Any]		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         A_      :				int		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE		)







       @slow
       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           A_      :				Optional[Any]		     =					ViTMSNModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
           self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)




def   _SCREAMING_SNAKE_CASE	( ):
  A_      :				Union[str, Any]		     =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
  return image







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




       """simple docstring"""



       @cached_property
       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





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







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




         '''simple docstring'''





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

         A_      :				Tuple		     =					self.default_image_processor
         A_      :				List[Any]		     =					prepare_img()
         A_      :				Optional[int]		     =					image_processor(images=_SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''		).to(_SCREAMING_SNAKE_CASE		)

         # forward pass
         with torch.no_grad():
           A_      :				Tuple		     =					model(**_SCREAMING_SNAKE_CASE		)

         # verify the logits
         A_      :				int		     =					torch.Size((1, 1000)		)
         self.assertEqual(outputs.logits.shape				,       _SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]		).to(_SCREAMING_SNAKE_CASE		)

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase								=	{
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	["""RobertaTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """RobertaForCausalLM""",
         """RobertaForMaskedLM""",
         """RobertaForMultipleChoice""",
         """RobertaForQuestionAnswering""",
         """RobertaForSequenceClassification""",
         """RobertaForTokenClassification""",
         """RobertaModel""",
         """RobertaPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFRobertaForCausalLM""",
         """TFRobertaForMaskedLM""",
         """TFRobertaForMultipleChoice""",
         """TFRobertaForQuestionAnswering""",
         """TFRobertaForSequenceClassification""",
         """TFRobertaForTokenClassification""",
         """TFRobertaMainLayer""",
         """TFRobertaModel""",
         """TFRobertaPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """FlaxRobertaForCausalLM""",
         """FlaxRobertaForMaskedLM""",
         """FlaxRobertaForMultipleChoice""",
         """FlaxRobertaForQuestionAnswering""",
         """FlaxRobertaForSequenceClassification""",
         """FlaxRobertaForTokenClassification""",
         """FlaxRobertaModel""",
         """FlaxRobertaPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
     from .tokenization_roberta import RobertaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_roberta_fast import RobertaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roberta import (
              ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              RobertaForCausalLM,
              RobertaForMaskedLM,
              RobertaForMultipleChoice,
              RobertaForQuestionAnswering,
              RobertaForSequenceClassification,
              RobertaForTokenClassification,
              RobertaModel,
              RobertaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_roberta import (
              TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRobertaForCausalLM,
              TFRobertaForMaskedLM,
              TFRobertaForMultipleChoice,
              TFRobertaForQuestionAnswering,
              TFRobertaForSequenceClassification,
              TFRobertaForTokenClassification,
              TFRobertaMainLayer,
              TFRobertaModel,
              TFRobertaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_roberta import (
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaModel,
              FlaxRobertaPreTrainedModel,
          )

else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




