
'''simple docstring'''
import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 (
        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        MODEL_MAPPING,
        DeiTForImageClassification,
        DeiTForImageClassificationWithTeacher,
        DeiTForMaskedImageModeling,
        DeiTModel,
    )
    from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DeiTImageProcessor


class       _lowerCAmelCase   :







       '''simple docstring'''

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

              # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
              _snake_case       =  (image_size // patch_size) ** 2
              _snake_case       =  num_patches + 2

       def 	lowercase							(self  )					->       Optional[int]:
              _snake_case       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

              _snake_case       =  self.get_config()

              return config, pixel_values, labels

       def 	lowercase							(self  )					->       List[str]:
              return DeiTConfig(
                  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  ,  is_decoder=_UpperCAmelCase  ,  initializer_range=self.initializer_range  ,  encoder_stride=self.encoder_stride  ,  )

       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Any:
              _snake_case       =  DeiTModel(config=_UpperCAmelCase  )
              model.to(_UpperCAmelCase  )
              model.eval()
              _snake_case       =  model(_UpperCAmelCase  )
              self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, self.seq_length, self.hidden_size)  )

       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Optional[Any]:
              _snake_case       =  DeiTForMaskedImageModeling(config=_UpperCAmelCase  )
              model.to(_UpperCAmelCase  )
              model.eval()
              _snake_case       =  model(_UpperCAmelCase  )
              self.parent.assertEqual(
                  result.reconstruction.shape  ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)  )

              # test greyscale images
              _snake_case       =  1
              _snake_case       =  DeiTForMaskedImageModeling(_UpperCAmelCase  )
              model.to(_UpperCAmelCase  )
              model.eval()

              _snake_case       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
              _snake_case       =  model(_UpperCAmelCase  )
              self.parent.assertEqual(result.reconstruction.shape  ,  (self.batch_size, 1, self.image_size, self.image_size)  )

       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       str:
              _snake_case       =  self.type_sequence_label_size
              _snake_case       =  DeiTForImageClassification(_UpperCAmelCase  )
              model.to(_UpperCAmelCase  )
              model.eval()
              _snake_case       =  model(_UpperCAmelCase  ,  labels=_UpperCAmelCase  )
              self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.type_sequence_label_size)  )

              # test greyscale images
              _snake_case       =  1
              _snake_case       =  DeiTForImageClassification(_UpperCAmelCase  )
              model.to(_UpperCAmelCase  )
              model.eval()

              _snake_case       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
              _snake_case       =  model(_UpperCAmelCase  ,  labels=_UpperCAmelCase  )
              self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.type_sequence_label_size)  )





       def 	lowercase							(self  )					->       List[Any]:
              _snake_case       =  self.prepare_config_and_inputs()
              (
                  (
                  _snake_case     
              ),							(
                  _snake_case     
              ),							(
                  _snake_case     
              ),							     
              )       =  config_and_inputs
              _snake_case       =  {"""pixel_values""": pixel_values}
              return config, inputs_dict


@require_torch
class       _lowerCAmelCase   (   lowerCamelCase_					,							lowerCamelCase_					,							unittest.TestCase				):







       '''simple docstring'''

       lowerCAmelCase_ 						=						(
           (
               DeiTModel,
               DeiTForImageClassification,
               DeiTForImageClassificationWithTeacher,
               DeiTForMaskedImageModeling,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase_ 						=						(
           {
               """feature-extraction""": DeiTModel,
               """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
           }
           if is_torch_available()
           else {}
       )

       lowerCAmelCase_ 						=						False
       lowerCAmelCase_ 						=						False
       lowerCAmelCase_ 						=						False

       def 	lowercase							(self  )					->       Optional[Any]:
              _snake_case       =  DeiTModelTester(self  )
              _snake_case       =  ConfigTester(self  ,  config_class=_UpperCAmelCase  ,  has_text_modality=_UpperCAmelCase  ,  hidden_size=37  )

       def 	lowercase							(self  )					->       Dict:
              self.config_tester.run_common_tests()

       @unittest.skip(reason="""DeiT does not use inputs_embeds"""  )
       def 	lowercase							(self  )					->       Optional[int]:
              pass

       def 	lowercase							(self  )					->       Any:
              _snake_case,							_snake_case       =  self.model_tester.prepare_config_and_inputs_for_common()

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

       def 	lowercase							(self  )					->       List[Any]:
              _snake_case,							_snake_case       =  self.model_tester.prepare_config_and_inputs_for_common()

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

                     _snake_case       =  ["""pixel_values"""]
                     self.assertListEqual(arg_names[:1]  ,  _UpperCAmelCase  )

       def 	lowercase							(self  )					->       int:
              _snake_case       =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*_UpperCAmelCase  )

       def 	lowercase							(self  )					->       Optional[int]:
              _snake_case       =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCAmelCase  )

       def 	lowercase							(self  )					->       Tuple:
              _snake_case       =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase  )

       def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=False  )					->       str:
              _snake_case       =  super()._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase  ,  return_labels=_UpperCAmelCase  )

              if return_labels:
                     if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                            del inputs_dict["labels"]

              return inputs_dict

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






              if not self.model_tester.is_training:
                     return

              _snake_case,							_snake_case       =  self.model_tester.prepare_config_and_inputs_for_common()
              _snake_case       =  True

              for model_class in self.all_model_classes:
                     # DeiTForImageClassificationWithTeacher supports inference-only
                     if (
                         model_class in get_values(_UpperCAmelCase  )
                         or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
                     ):
                            continue
                     _snake_case       =  model_class(_UpperCAmelCase  )
                     model.to(_UpperCAmelCase  )
                     model.train()
                     _snake_case       =  self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase  ,  return_labels=_UpperCAmelCase  )
                     _snake_case       =  model(**_UpperCAmelCase  ).loss
                     loss.backward()

       def 	lowercase							(self  )					->       Union[str, Any]:
              _snake_case,							_snake_case       =  self.model_tester.prepare_config_and_inputs_for_common()
              if not self.model_tester.is_training:
                     return

              _snake_case       =  False
              _snake_case       =  True

              for model_class in self.all_model_classes:
                     if model_class in get_values(_UpperCAmelCase  ) or not model_class.supports_gradient_checkpointing:
                            continue
                     # DeiTForImageClassificationWithTeacher supports inference-only
                     if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                            continue
                     _snake_case       =  model_class(_UpperCAmelCase  )
                     model.gradient_checkpointing_enable()
                     model.to(_UpperCAmelCase  )
                     model.train()
                     _snake_case       =  self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase  ,  return_labels=_UpperCAmelCase  )
                     _snake_case       =  model(**_UpperCAmelCase  ).loss
                     loss.backward()

       def 	lowercase							(self  )					->       Tuple:
              _snake_case,							_snake_case       =  self.model_tester.prepare_config_and_inputs_for_common()

              _snake_case       =  [
                  {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
                  {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
                  {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
              ]

              for model_class in self.all_model_classes:
                     if (
                         model_class
                         not in [
                             *get_values(_UpperCAmelCase  ),
                             *get_values(_UpperCAmelCase  ),
                         ]
                         or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
                     ):
                            continue

                     for problem_type in problem_types:
                            with self.subTest(msg=f"""Testing {model_class} with {problem_type["title"]}"""  ):
                                   _snake_case       =  problem_type["""title"""]
                                   _snake_case       =  problem_type["""num_labels"""]

                                   _snake_case       =  model_class(_UpperCAmelCase  )
                                   model.to(_UpperCAmelCase  )
                                   model.train()

                                   _snake_case       =  self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase  ,  return_labels=_UpperCAmelCase  )

                                   if problem_type["num_labels"] > 1:
                                          _snake_case       =  inputs["""labels"""].unsqueeze(1  ).repeat(1  ,  problem_type["""num_labels"""]  )

                                   _snake_case       =  inputs["""labels"""].to(problem_type["""dtype"""]  )

                                   # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                                   # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                                   # they have the same size." which is a symptom something in wrong for the regression problem.
                                   # See https://github.com/huggingface/transformers/issues/11780
                                   with warnings.catch_warnings(record=_UpperCAmelCase  ) as warning_list:
                                          _snake_case       =  model(**_UpperCAmelCase  ).loss
                                   for w in warning_list:
                                          if "Using a target size that is different to the input size" in str(w.message  ):
                                                 raise ValueError(
                                                     f"""Something is going wrong in the regression problem: intercepted {w.message}"""  )

                                   loss.backward()





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






              for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     _snake_case       =  DeiTModel.from_pretrained(_UpperCAmelCase  )
                     self.assertIsNotNone(_UpperCAmelCase  )






def  __SCREAMING_SNAKE_CASE				(  ):
       _snake_case       =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
       return image


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







       '''simple docstring'''

       @cached_property
       def 	lowercase							(self  )					->       Tuple:
              return (
                  DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""  )
                  if is_vision_available()
                  else None
              )

       @slow
       def 	lowercase							(self  )					->       Any:
              _snake_case       =  DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""  ).to(
                  _UpperCAmelCase  )

              _snake_case       =  self.default_image_processor
              _snake_case       =  prepare_img()
              _snake_case       =  image_processor(images=_UpperCAmelCase  ,  return_tensors="""pt"""  ).to(_UpperCAmelCase  )

              # forward pass
              with torch.no_grad():
                     _snake_case       =  model(**_UpperCAmelCase  )

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

              _snake_case       =  torch.tensor([-1.0266, 0.1912, -1.2861]  ).to(_UpperCAmelCase  )

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





       @slow
       @require_accelerate
       @require_torch_gpu
       def 	lowercase							(self  )					->       str:
              _snake_case       =  DeiTModel.from_pretrained(
                  """facebook/deit-base-distilled-patch16-224"""  ,  torch_dtype=torch.floataa  ,  device_map="""auto"""  )
              _snake_case       =  self.default_image_processor

              _snake_case       =  prepare_img()
              _snake_case       =  image_processor(images=_UpperCAmelCase  ,  return_tensors="""pt"""  )
              _snake_case       =  inputs.pixel_values.to(_UpperCAmelCase  )

              # forward pass to make sure inference works in fp16
              with torch.no_grad():
                     _snake_case       =  model(_UpperCAmelCase  )

'''simple docstring'''
from typing import TYPE_CHECKING

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


__lowerCAmelCase												=	{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ResNetForImageClassification',
				    'ResNetModel',
				    'ResNetPreTrainedModel',
				    'ResNetBackbone',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFResNetForImageClassification',
				    'TFResNetModel',
				    'TFResNetPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'FlaxResNetForImageClassification',
				    'FlaxResNetModel',
				    'FlaxResNetPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_resnet import (
								    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ResNetBackbone,
								    ResNetForImageClassification,
								    ResNetModel,
								    ResNetPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_resnet import (
								    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFResNetForImageClassification,
								    TFResNetModel,
								    TFResNetPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
				import sys

				__lowerCAmelCase												=	_LazyModule(__name__, globals()['__file__'], _import_structure)