"""simple docstring"""




import inspect
import unittest

from transformers import ViTConfig
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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
     from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor
class 						__A							:






     def __init__(		self				,     a__				,     a__=13				,     a__=30				,     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__=0.0_2				,     a__=None				,     a__=2				,     ):
       _lowerCAmelCase						:       List[Any]  			=     parent
       _lowerCAmelCase						:       int  			=     batch_size
       _lowerCAmelCase						:       Tuple  			=     image_size
       _lowerCAmelCase						:       Optional[Any]  			=     patch_size
       _lowerCAmelCase						:       int  			=     num_channels
       _lowerCAmelCase						:       List[str]  			=     is_training
       _lowerCAmelCase						:       Tuple  			=     use_labels
       _lowerCAmelCase						:       Any  			=     hidden_size
       _lowerCAmelCase						:       List[Any]  			=     num_hidden_layers
       _lowerCAmelCase						:       List[str]  			=     num_attention_heads
       _lowerCAmelCase						:       Optional[Any]  			=     intermediate_size
       _lowerCAmelCase						:       Any  			=     hidden_act
       _lowerCAmelCase						:       Tuple  			=     hidden_dropout_prob
       _lowerCAmelCase						:       Optional[Any]  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       Any  			=     type_sequence_label_size
       _lowerCAmelCase						:       int  			=     initializer_range
       _lowerCAmelCase						:       Any  			=     scope
       _lowerCAmelCase						:       Union[str, Any]  			=     encoder_stride

       # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       _lowerCAmelCase						:       int  			=     (image_size // patch_size) ** 2
       _lowerCAmelCase						:       Optional[Any]  			=     num_patches + 1






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

       _lowerCAmelCase						:       Dict  			=     self.get_config()

       return config, pixel_values, labels






     def     __A  (		self  ):
       return ViTConfig(
           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				,     encoder_stride=self.encoder_stride				,     )






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       str  			=     ViTModel(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[int]  			=     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__  ):
       _lowerCAmelCase						:       Any  			=     ViTForMaskedImageModeling(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       List[str]  			=     model(a__  )
       self.parent.assertEqual(
           result.reconstruction.shape				,     (self.batch_size, self.num_channels, self.image_size, self.image_size)  )

       # test greyscale images
       _lowerCAmelCase						:       Union[str, Any]  			=     1
       _lowerCAmelCase						:       Union[str, Any]  			=     ViTForMaskedImageModeling(a__  )
       model.to(a__  )
       model.eval()

       _lowerCAmelCase						:       Any  			=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
       _lowerCAmelCase						:       Dict  			=     model(a__  )
       self.parent.assertEqual(result.reconstruction.shape				,     (self.batch_size, 1, self.image_size, self.image_size)  )






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.type_sequence_label_size
       _lowerCAmelCase						:       Dict  			=     ViTForImageClassification(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       int  			=     model(a__				,     labels=a__  )
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.type_sequence_label_size)  )

       # test greyscale images
       _lowerCAmelCase						:       int  			=     1
       _lowerCAmelCase						:       Dict  			=     ViTForImageClassification(a__  )
       model.to(a__  )
       model.eval()

       _lowerCAmelCase						:       List[str]  			=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
       _lowerCAmelCase						:       Tuple  			=     model(a__  )
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.type_sequence_label_size)  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     self.prepare_config_and_inputs()
       (
           (
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	
       )						:       List[str]  			=     config_and_inputs
       _lowerCAmelCase						:       Optional[Any]  			=     {"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			int							=		(
         (
             ViTModel,
             ViTForImageClassification,
             ViTForMaskedImageModeling,
         )
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:			Tuple							=		(
         {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
         if is_torch_available()
         else {}
     )
     _UpperCamelCase							:			Dict							=		True

     _UpperCamelCase							:			str							=		False
     _UpperCamelCase							:			Optional[Any]							=		False
     _UpperCamelCase							:			List[str]							=		False






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     ViTModelTester(self  )
       _lowerCAmelCase						:       Any  			=     ConfigTester(self				,     config_class=a__				,     has_text_modality=a__				,     hidden_size=37  )






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






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






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _lowerCAmelCase						:       List[str]  			=     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     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     self.model_tester.prepare_config_and_inputs_for_common()

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

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






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






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a__  )






     @slow
     def     __A  (		self  ):
       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						:       List[str]  			=     ViTModel.from_pretrained(a__  )
         self.assertIsNotNone(a__  )







def 							SCREAMING_SNAKE_CASE     (			)       ->      List[str]:
  _lowerCAmelCase						:       Union[str, Any]  			=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
  return image






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






     @cached_property
     def     __A  (		self  ):
       return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224"""  ) if is_vision_available() else None






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""  ).to(a__  )

       _lowerCAmelCase						:       str  			=     self.default_image_processor
       _lowerCAmelCase						:       str  			=     prepare_img()
       _lowerCAmelCase						:       Tuple  			=     image_processor(images=a__				,     return_tensors="""pt"""  ).to(a__  )

       # forward pass
       with torch.no_grad():
         _lowerCAmelCase						:       Optional[int]  			=     model(**a__  )

       # verify the logits
       _lowerCAmelCase						:       str  			=     torch.Size((1, 1000)  )
       self.assertEqual(outputs.logits.shape				,     a__  )

       _lowerCAmelCase						:       List[Any]  			=     torch.tensor([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]  ).to(a__  )

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






     @slow
     def     __A  (		self  ):
       # ViT models have an `interpolate_pos_encoding` argument in their forward method,
       # allowing to interpolate the pre-trained position embeddings in order to use
       # the model on higher resolutions. The DINO model by Facebook AI leverages this
       # to visualize self-attention on higher resolution images.
       _lowerCAmelCase						:       Optional[Any]  			=     ViTModel.from_pretrained("""facebook/dino-vits8"""  ).to(a__  )

       _lowerCAmelCase						:       str  			=     ViTImageProcessor.from_pretrained("""facebook/dino-vits8"""				,     size=480  )
       _lowerCAmelCase						:       Any  			=     prepare_img()
       _lowerCAmelCase						:       Dict  			=     image_processor(images=a__				,     return_tensors="""pt"""  )
       _lowerCAmelCase						:       Optional[Any]  			=     inputs.pixel_values.to(a__  )

       # forward pass
       with torch.no_grad():
         _lowerCAmelCase						:       str  			=     model(a__				,     interpolate_pos_encoding=a__  )

       # verify the logits
       _lowerCAmelCase						:       str  			=     torch.Size((1, 3601, 384)  )
       self.assertEqual(outputs.last_hidden_state.shape				,     a__  )

       _lowerCAmelCase						:       Union[str, Any]  			=     torch.tensor(
           [[4.2_3_4_0, 4.3_9_0_6, -6.6_6_9_2], [4.5_4_6_3, 1.8_9_2_8, -6.7_2_5_7], [4.4_4_2_9, 0.8_4_9_6, -5.8_5_8_5]]  ).to(a__  )

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






     @slow
     @require_accelerate
     @require_torch_gpu
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     ViTModel.from_pretrained("""facebook/dino-vits8"""				,     torch_dtype=torch.floataa				,     device_map="""auto"""  )
       _lowerCAmelCase						:       Union[str, Any]  			=     self.default_image_processor

       _lowerCAmelCase						:       Dict  			=     prepare_img()
       _lowerCAmelCase						:       List[str]  			=     image_processor(images=a__				,     return_tensors="""pt"""  )
       _lowerCAmelCase						:       Any  			=     inputs.pixel_values.to(a__  )

       # forward pass to make sure inference works in fp16
       with torch.no_grad():
         _lowerCAmelCase						:       Optional[int]  			=     model(a__  )






import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class    lowerCAmelCase__     :






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							Dict:
						if dst_width < 0 or dst_height < 0:
										raise ValueError('''Destination width/height should be > 0'''  )

						__lowerCamelCase    					=	img
						__lowerCamelCase    					=	img.shape[1]
						__lowerCamelCase    					=	img.shape[0]
						__lowerCamelCase    					=	dst_width
						__lowerCamelCase    					=	dst_height

						__lowerCamelCase    					=	self.src_w / self.dst_w
						__lowerCamelCase    					=	self.src_h / self.dst_h

						__lowerCamelCase    					=	__lowerCamelCase    					=	(
						    np.ones((self.dst_h, self.dst_w, 3)							,			np.uinta  ) * 2_55
						)






		def  __A       (		self		:							List[Any]  )     ->							str:
						for i in range(self.dst_h  ):
										for j in range(self.dst_w  ):
														__lowerCamelCase    					=	self.img[self.get_y(SCREAMING_SNAKE_CASE__  )][self.get_x(SCREAMING_SNAKE_CASE__  )]






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_x * x  )







		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_y * y  )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__   :			List[str]												=    800, 600
					SCREAMING_SNAKE_CASE__   :			int												=    imread("image_data/lena.jpg", 1)
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    NearestNeighbour(im, dst_w, dst_h)
					n.process()

					imshow(
					    F'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
					)
					waitKey(0)
					destroyAllWindows()



