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 	__snake_case	:





      '''simple docstring'''






      def __init__(					self     :				int    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				Union[str, Any]=13    ,  _UpperCamelCase     :				List[str]=64    ,  _UpperCamelCase     :				Any=2    ,  _UpperCamelCase     :				Tuple=3    ,  _UpperCamelCase     :				List[Any]=True    ,  _UpperCamelCase     :				str=True    ,  _UpperCamelCase     :				List[str]=32    ,  _UpperCamelCase     :				str=5    ,  _UpperCamelCase     :				Tuple=4    ,  _UpperCamelCase     :				Tuple=37    ,  _UpperCamelCase     :				List[str]="gelu"    ,  _UpperCamelCase     :				List[str]=0.1    ,  _UpperCamelCase     :				Optional[Any]=0.1    ,  _UpperCamelCase     :				str=10    ,  _UpperCamelCase     :				str=0.0_2    ,  _UpperCamelCase     :				Union[str, Any]=[1, 16, 4, 4]    ,  _UpperCamelCase     :				Dict=None    ,  )				->int:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						parent
          _lowerCamelCase     :		str 						=						batch_size
          _lowerCamelCase     :		Tuple 						=						image_size
          _lowerCamelCase     :		List[Any] 						=						patch_size
          _lowerCamelCase     :		Optional[Any] 						=						num_channels
          _lowerCamelCase     :		List[str] 						=						is_training
          _lowerCamelCase     :		List[Any] 						=						use_labels
          _lowerCamelCase     :		Optional[Any] 						=						hidden_size
          _lowerCamelCase     :		Any 						=						num_hidden_layers
          _lowerCamelCase     :		List[Any] 						=						num_attention_heads
          _lowerCamelCase     :		Any 						=						intermediate_size
          _lowerCamelCase     :		Optional[int] 						=						hidden_act
          _lowerCamelCase     :		List[str] 						=						hidden_dropout_prob
          _lowerCamelCase     :		str 						=						attention_probs_dropout_prob
          _lowerCamelCase     :		int 						=						type_sequence_label_size
          _lowerCamelCase     :		Dict 						=						initializer_range
          _lowerCamelCase     :		Tuple 						=						scope
          _lowerCamelCase     :		Dict 						=						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
          _lowerCamelCase     :		Optional[int] 						=						(self.image_size // 32) ** 2
          _lowerCamelCase     :		List[str] 						=						num_patches + 1



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


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          _lowerCamelCase     :		str 						=						None
          if self.use_labels:
              _lowerCamelCase     :		Union[str, Any] 						=						ids_tensor([self.batch_size]    ,  self.type_sequence_label_size)

          _lowerCamelCase     :		Dict 						=						self.get_config()

          return config, pixel_values, labels



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						{
              """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 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				List[Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						ViTHybridModel(config=A__)
          model.to(A__)
          model.eval()
          _lowerCamelCase     :		Tuple 						=						model(A__)
          self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				List[Any])				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.type_sequence_label_size
          _lowerCamelCase     :		Dict 						=						ViTHybridForImageClassification(A__)
          model.to(A__)
          model.eval()
          _lowerCamelCase     :		Any 						=						model(A__    ,  labels=A__)
          self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.type_sequence_label_size))




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


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.prepare_config_and_inputs()
          _lowerCamelCase     :		Union[str, Any] 						=						config_and_inputs
          _lowerCamelCase     :		str 						=						{"""pixel_values""": pixel_values}
          return config, inputs_dict





@require_torch
class 	__snake_case	( __lowerCAmelCase ,  __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
      _snake_case    		=       (
          {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
          if is_torch_available()
          else {}
      )
      _snake_case    		=       False
      _snake_case    		=       False
      _snake_case    		=       False



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						ViTHybridModelTester(self)
          _lowerCamelCase     :		List[Any] 						=						ConfigTester(self    ,  config_class=A__    ,  has_text_modality=A__    ,  hidden_size=37)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->List[Any]:


          """simple docstring"""


          self.config_tester.run_common_tests()



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


          """simple docstring"""


          pass



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


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.model_tester.prepare_config_and_inputs_for_common()

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						self.model_tester.prepare_config_and_inputs_for_common()

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

              _lowerCamelCase     :		Union[str, Any] 						=						["""pixel_values"""]
              self.assertListEqual(arg_names[:1]    ,  A__)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->str:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A__)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*A__)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						self.model_tester.prepare_config_and_inputs_for_common()

          _lowerCamelCase     :		Any 						=						_config_zero_init(A__)
          for model_class in self.all_model_classes:
              _lowerCamelCase     :		List[str] 						=						model_class(config=A__)
              # Skip the check for the backbone
              for name, module in model.named_modules():
                  if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                      _lowerCamelCase     :		Tuple 						=						[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 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->int:


          """simple docstring"""


          for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _lowerCamelCase     :		Union[str, Any] 						=						ViTHybridModel.from_pretrained(A__)
              self.assertIsNotNone(A__)







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
    return image




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





      '''simple docstring'''






      @cached_property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Dict:


          """simple docstring"""


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



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


          """simple docstring"""


          _lowerCamelCase     :		str 						=						ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
              A__)

          _lowerCamelCase     :		Optional[Any] 						=						self.default_image_processor
          _lowerCamelCase     :		Tuple 						=						prepare_img()
          _lowerCamelCase     :		Optional[int] 						=						image_processor(images=A__    ,  return_tensors="""pt""").to(A__)

          # forward pass
          with torch.no_grad():
              _lowerCamelCase     :		List[Any] 						=						model(**A__)

          # verify the logits
          _lowerCamelCase     :		Tuple 						=						torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape    ,  A__)

          _lowerCamelCase     :		List[Any] 						=						torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9]).to(A__)

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




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


          """simple docstring"""


          _lowerCamelCase     :		str 						=						ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384""")
          _lowerCamelCase     :		Tuple 						=						ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384"""    ,  device_map="""auto""")

          _lowerCamelCase     :		Optional[Any] 						=						prepare_img()

          _lowerCamelCase     :		Union[str, Any] 						=						image_processor(images=A__    ,  return_tensors="""pt""")
          _lowerCamelCase     :		Union[str, Any] 						=						model(**A__)
          _lowerCamelCase     :		Tuple 						=						outputs.logits
          # model predicts one of the 1000 ImageNet classes
          _lowerCamelCase     :		Union[str, Any] 						=						logits.argmax(-1).item()

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





import math







def 						A__  (					__A	):







    '''simple docstring'''
    assert isinstance(__A   , __A	) and (
        number >= 0
    ), "'number' must been an int and positive"

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or not number % 2:
        # Negatives, 0, 1 and all even numbers are not primes
        return False

    _lowerCamelCase     :		List[Any] 						=						range(3   , int(math.sqrt(__A	) + 1	)   , 2	)
    return not any(not number % i for i in odd_numbers	)







def 						A__  (					__A   , __A=1   , **__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Dict 						=						factor * value
    _lowerCamelCase     :		str 						=						value

    while not is_prime(__A	):
        value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

    if value == first_value_val:
        return next_prime(value + 1   , **__A	)
    return value




