



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 SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__=13,		lowerCamelCase__=64,		lowerCamelCase__=2,		lowerCamelCase__=3,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=32,		lowerCamelCase__=5,		lowerCamelCase__=4,		lowerCamelCase__=37,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=10,		lowerCamelCase__=0.02,		lowerCamelCase__=[1, 16, 4, 4],		lowerCamelCase__=None,		):
        A			:	List[Any]										=       parent
        A			:	str										=       batch_size
        A			:	Dict										=       image_size
        A			:	Union[str, Any]										=       patch_size
        A			:	Dict										=       num_channels
        A			:	Dict										=       is_training
        A			:	int										=       use_labels
        A			:	Optional[int]										=       hidden_size
        A			:	Dict										=       num_hidden_layers
        A			:	List[str]										=       num_attention_heads
        A			:	int										=       intermediate_size
        A			:	int										=       hidden_act
        A			:	Optional[int]										=       hidden_dropout_prob
        A			:	List[str]										=       attention_probs_dropout_prob
        A			:	Optional[int]										=       type_sequence_label_size
        A			:	List[str]										=       initializer_range
        A			:	Union[str, Any]										=       scope
        A			:	List[Any]										=       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			:	str										=       (self.image_size // 32) ** 2
        A			:	Optional[Any]										=       num_patches + 1






    def       _lowerCAmelCase (	self		):
        A			:	str										=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        A			:	List[Any]										=       None
        if self.use_labels:
            A			:	List[Any]										=       ids_tensor([self.batch_size],		self.type_sequence_label_size		)

        A			:	Dict										=       self.get_config()

        return config, pixel_values, labels






    def       _lowerCAmelCase (	self		):
        A			:	Optional[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=lowerCamelCase__,		initializer_range=self.initializer_range,		backbone_featmap_shape=self.backbone_featmap_shape,		backbone_config=lowerCamelCase__,		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Tuple										=       ViTHybridModel(config=lowerCamelCase__		)
        model.to(lowerCamelCase__		)
        model.eval()
        A			:	Any										=       model(lowerCamelCase__		)
        self.parent.assertEqual(result.last_hidden_state.shape,		(self.batch_size, self.seq_length, self.hidden_size)		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Union[str, Any]										=       self.type_sequence_label_size
        A			:	Any										=       ViTHybridForImageClassification(lowerCamelCase__		)
        model.to(lowerCamelCase__		)
        model.eval()
        A			:	Dict										=       model(lowerCamelCase__,		labels=lowerCamelCase__		)
        self.parent.assertEqual(result.logits.shape,		(self.batch_size, self.type_sequence_label_size)		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       self.prepare_config_and_inputs()
        A					,							A					,							A			:	str										=       config_and_inputs
        A			:	int										=       {"""pixel_values""": pixel_values}
        return config, inputs_dict






@require_torch
class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						unittest.TestCase   ):




    '''simple docstring'''






    __lowerCamelCase      :			Optional[Any] =				(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
    __lowerCamelCase      :			Dict =				(
        {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
        if is_torch_available()
        else {}
    )
    __lowerCamelCase      :			str =				False
    __lowerCamelCase      :			int =				False
    __lowerCamelCase      :			str =				False






    def       _lowerCAmelCase (	self		):
        A			:	str										=       ViTHybridModelTester(self		)
        A			:	List[str]										=       ConfigTester(self,		config_class=lowerCamelCase__,		has_text_modality=lowerCamelCase__,		hidden_size=37		)






    def       _lowerCAmelCase (	self		):
        self.config_tester.run_common_tests()






    @unittest.skip(reason="""ViT does not use inputs_embeds"""		)
    def       _lowerCAmelCase (	self		):
        pass






    def       _lowerCAmelCase (	self		):
        A					,							A			:	List[Any]										=       self.model_tester.prepare_config_and_inputs_for_common()

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






    def       _lowerCAmelCase (	self		):
        A					,							A			:	Tuple										=       self.model_tester.prepare_config_and_inputs_for_common()

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

            A			:	List[str]										=       ["""pixel_values"""]
            self.assertListEqual(arg_names[:1],		lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        A			:	Optional[int]										=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        A					,							A			:	Dict										=       self.model_tester.prepare_config_and_inputs_for_common()

        A			:	int										=       _config_zero_init(lowerCamelCase__		)
        for model_class in self.all_model_classes:
            A			:	Tuple										=       model_class(config=lowerCamelCase__		)
            # Skip the check for the backbone
            for name, module in model.named_modules():
                if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                    A			:	Optional[int]										=       [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       _lowerCAmelCase (	self		):
        for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A			:	int										=       ViTHybridModel.from_pretrained(lowerCamelCase__		)
            self.assertIsNotNone(lowerCamelCase__		)




def      __UpperCamelCase     (  )	->  str:



    """simple docstring"""






    A			:	Any										=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
    return image






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




    '''simple docstring'''






    @cached_property
    def       _lowerCAmelCase (	self		):
        return (
            ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
            if is_vision_available()
            else None
        )






    @slow
    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
            lowerCamelCase__		)

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

        # forward pass
        with torch.no_grad():
            A			:	Union[str, Any]										=       model(**lowerCamelCase__		)

        # verify the logits
        A			:	Optional[Any]										=       torch.Size((1, 1000)		)
        self.assertEqual(outputs.logits.shape,		lowerCamelCase__		)

        A			:	Union[str, Any]										=       torch.tensor([-1.9090, -0.4993, -0.2389]		).to(lowerCamelCase__		)

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






    @slow
    @require_accelerate
    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""		)
        A			:	str										=       ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384""",		device_map="""auto"""		)

        A			:	List[str]										=       prepare_img()

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

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







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_:Any					=						{
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:List[Any]					=						["""MobileNetV2FeatureExtractor"""]
     SCREAMING_SNAKE_CASE_:Tuple					=						["""MobileNetV2ImageProcessor"""]


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Any					=						[
         """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MobileNetV2ForImageClassification""",
         """MobileNetV2ForSemanticSegmentation""",
         """MobileNetV2Model""",
         """MobileNetV2PreTrainedModel""",
         """load_tf_weights_in_mobilenet_v2""",
     ]


if TYPE_CHECKING:
     from .configuration_mobilenet_va import (
         MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileNetVaConfig,
         MobileNetVaOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
          from .image_processing_mobilenet_va import MobileNetVaImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilenet_va import (
              MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileNetVaForImageClassification,
              MobileNetVaForSemanticSegmentation,
              MobileNetVaModel,
              MobileNetVaPreTrainedModel,
              load_tf_weights_in_mobilenet_va,
          )


else:
     import sys

     SCREAMING_SNAKE_CASE_:Optional[Any]					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


