



'''simple docstring'''





import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
  from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image






class __A			:



 '''simple docstring'''







 def __init__(self ,   A ,   A=13 ,   A=64 ,   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=[1, 16, 4, 4] ,   A=None ,   )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  parent
      _a      =  batch_size
      _a      =  image_size
      _a      =  patch_size
      _a      =  num_channels
      _a      =  is_training
      _a      =  use_labels
      _a      =  hidden_size
      _a      =  num_hidden_layers
      _a      =  num_attention_heads
      _a      =  intermediate_size
      _a      =  hidden_act
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  type_sequence_label_size
      _a      =  initializer_range
      _a      =  scope
      _a      =  backbone_featmap_shape

      # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      # the number of patches is based on the feature map of the backbone, which by default uses an output stride
      # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
      _a      =  (self.image_size // 32) ** 2
      _a      =  num_patches + 1



 def 					a__  (self       )    ->     str:


      """simple docstring"""



      _a      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

      _a      =  self.get_config()

      return config, pixel_values, labels



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


      """simple docstring"""



      _a      =  {
          '''global_padding''': '''same''',
          '''layer_type''': '''bottleneck''',
          '''depths''': [3, 4, 9],
          '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
          '''embedding_dynamic_padding''': True,
          '''hidden_sizes''': [4, 8, 16, 32],
          '''num_groups''': 2,
      }

      return ViTHybridConfig(
          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=A ,   initializer_range=self.initializer_range ,   backbone_featmap_shape=self.backbone_featmap_shape ,   backbone_config=A ,   )



 def 					a__  (self ,   A ,   A ,   A       )    ->     Any:


      """simple docstring"""



      _a      =  ViTHybridModel(config=A       )
      model.to(A       )
      model.eval()
      _a      =  model(A       )
      self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)       )



 def 					a__  (self ,   A ,   A ,   A       )    ->     int:


      """simple docstring"""



      _a      =  self.type_sequence_label_size
      _a      =  ViTHybridForImageClassification(A       )
      model.to(A       )
      model.eval()
      _a      =  model(A ,   labels=A       )
      self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.type_sequence_label_size)       )



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


      """simple docstring"""



      _a      =  self.prepare_config_and_inputs()
      _a  ,    _a  ,    _a      =  config_and_inputs
      _a      =  {'''pixel_values''': pixel_values}
      return config, inputs_dict






@require_torch
class __A			(  A   ,   A   ,   unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase    :       List[str]          =       (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
 __lowerCamelCase    :       Any          =       (
     {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
     if is_torch_available()
     else {}
 )
 __lowerCamelCase    :       List[str]          =       False
 __lowerCamelCase    :       Union[str, Any]          =       False
 __lowerCamelCase    :       str          =       False



 def 					a__  (self       )    ->     str:


      """simple docstring"""



      _a      =  ViTHybridModelTester(self       )
      _a      =  ConfigTester(self ,   config_class=A ,   has_text_modality=A ,   hidden_size=37       )



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      self.config_tester.run_common_tests()



 @unittest.skip(reason='''ViT does not use inputs_embeds'''       )
 def 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      pass



 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      _a  ,    _a      =  self.model_tester.prepare_config_and_inputs_for_common()

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



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a  ,    _a      =  self.model_tester.prepare_config_and_inputs_for_common()

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

           _a      =  ['''pixel_values''']
           self.assertListEqual(arg_names[:1] ,   A       )



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


      """simple docstring"""



      _a      =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A       )



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      _a      =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*A       )



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


      """simple docstring"""



      _a  ,    _a      =  self.model_tester.prepare_config_and_inputs_for_common()

      _a      =  _config_zero_init(A       )
      for model_class in self.all_model_classes:
           _a      =  model_class(config=A       )
           # Skip the check for the backbone
           for name, module in model.named_modules():
                if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                     _a      =  [f'''{name}.{key}''' for key in module.state_dict().keys()]
                     break

           for name, param in model.named_parameters():
                if param.requires_grad:
                     if name in backbone_params:
                          continue
                     self.assertIn(
                         ((param.data.mean() * 1E9).round() / 1E9).item() ,   [0.0, 1.0] ,   msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' ,   )



 @slow
 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a      =  ViTHybridModel.from_pretrained(A       )
           self.assertIsNotNone(A       )





def lowerCAmelCase  ():



     """simple docstring"""



     _a      =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
     return image






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



 '''simple docstring'''







 @cached_property
 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      return (
          ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
          if is_vision_available()
          else None
      )



 @slow
 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a      =  ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(
          A       )

      _a      =  self.default_image_processor
      _a      =  prepare_img()
      _a      =  image_processor(images=A ,   return_tensors='''pt'''       ).to(A       )

      # forward pass
      with torch.no_grad():
           _a      =  model(**A       )

      # verify the logits
      _a      =  torch.Size((1, 1_000)       )
      self.assertEqual(outputs.logits.shape ,   A       )

      _a      =  torch.tensor([-1.9090, -0.4993, -0.2389]       ).to(A       )

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



 @slow
 @require_accelerate
 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a      =  ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384'''       )
      _a      =  ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''' ,   device_map='''auto'''       )

      _a      =  prepare_img()

      _a      =  image_processor(images=A ,   return_tensors='''pt'''       )
      _a      =  model(**A       )
      _a      =  outputs.logits
      # model predicts one of the 1000 ImageNet classes
      _a      =  logits.argmax(-1       ).item()

      self.assertTrue(model.config.idalabel[predicted_class_idx] ,   '''tabby, tabby cat'''       )








'''simple docstring'''





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_		       =  {"configuration_mmbt": ["MMBTConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_		       =  ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]


if TYPE_CHECKING:
  from .configuration_mmbt import MMBTConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
  import sys

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



