import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import ViTMSNForImageClassification, ViTMSNModel
			from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							Tuple      ,    _A							:							str      ,    _A							:							int=1_3      ,    _A							:							List[str]=3_0      ,    _A							:							Dict=2      ,    _A							:							Optional[Any]=3      ,    _A							:							Tuple=True      ,    _A							:							Union[str, Any]=True      ,    _A							:							Optional[int]=3_2      ,    _A							:							int=5      ,    _A							:							Union[str, Any]=4      ,    _A							:							List[Any]=3_7      ,    _A							:							Tuple="gelu"      ,    _A							:							Optional[int]=0.1      ,    _A							:							Union[str, Any]=0.1      ,    _A							:							Union[str, Any]=1_0      ,    _A							:							int=0.02      ,    _A							:							int=None      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    parent
											_SCREAMING_SNAKE_CASE		:						str			  =    batch_size
											_SCREAMING_SNAKE_CASE		:						int			  =    image_size
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    patch_size
											_SCREAMING_SNAKE_CASE		:						Dict			  =    num_channels
											_SCREAMING_SNAKE_CASE		:						int			  =    is_training
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    use_labels
											_SCREAMING_SNAKE_CASE		:						str			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						Any			  =    num_hidden_layers
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    num_attention_heads
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    intermediate_size
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    hidden_act
											_SCREAMING_SNAKE_CASE		:						int			  =    hidden_dropout_prob
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    type_sequence_label_size
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    initializer_range
											_SCREAMING_SNAKE_CASE		:						str			  =    scope

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





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.get_config()

											return config, pixel_values, labels





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											return ViTMSNConfig(
											    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      ,    initializer_range=self.initializer_range      ,    )





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							List[str]      ,    _A							:							Any      ,    _A							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    ViTMSNModel(config=_A)
											model.to(_A)
											model.eval()
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    model(_A)
											self.parent.assertEqual(result.last_hidden_state.shape      ,    (self.batch_size, self.seq_length, self.hidden_size))





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							str      ,    _A							:							Optional[int]      ,    _A							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.type_sequence_label_size
											_SCREAMING_SNAKE_CASE		:						Any			  =    ViTMSNForImageClassification(_A)
											model.to(_A)
											model.eval()
											_SCREAMING_SNAKE_CASE		:						Dict			  =    model(_A      ,    labels=_A)
											print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}""")
											print("""Labels: {labels}""")
											self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.type_sequence_label_size))

											# test greyscale images
											_SCREAMING_SNAKE_CASE		:						int			  =    1
											_SCREAMING_SNAKE_CASE		:						Any			  =    ViTMSNForImageClassification(_A)
											model.to(_A)
											model.eval()

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(_A)
											self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.type_sequence_label_size))





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE		:						int			  =    config_and_inputs
											_SCREAMING_SNAKE_CASE		:						str			  =    {"""pixel_values""": pixel_values}
											return config, inputs_dict




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







				"""simple docstring"""






				a			=						(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				a			=						(
				    {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				a			=						False
				a			=						False
				a			=						False
				a			=						False





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    ViTMSNModelTester(self)
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    ConfigTester(self      ,    config_class=_A      ,    has_text_modality=_A      ,    hidden_size=3_7)





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											self.config_tester.run_common_tests()





				@unittest.skip(reason="""ViTMSN does not use inputs_embeds""")
				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											pass





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    model_class(_A)
																		self.assertIsInstance(model.get_input_embeddings()      ,    (nn.Module))
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    model.get_output_embeddings()
																		self.assertTrue(x is None or isinstance(_A      ,    nn.Linear))





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







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

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

																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    ["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A)





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_A)





				@slow
				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    ViTMSNModel.from_pretrained(_A)
																		self.assertIsNotNone(_A)



def      lowerCamelCase_()->    int:
							_SCREAMING_SNAKE_CASE		:						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 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small""") if is_vision_available() else None





				@slow
				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											torch.manual_seed(2)
											_SCREAMING_SNAKE_CASE		:						Any			  =    ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small""").to(_A)

											_SCREAMING_SNAKE_CASE		:						Any			  =    self.default_image_processor
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    prepare_img()
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    image_processor(images=_A      ,    return_tensors="""pt""").to(_A)

											# forward pass
											with torch.no_grad():
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model(**_A)

											# verify the logits
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.Size((1, 1_0_0_0))
											self.assertEqual(outputs.logits.shape      ,    _A)

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    torch.tensor([-0.0_803, -0.4_454, -0.2_375]).to(_A)

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



"""simple docstring"""





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




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							int      ,    _A							:							List[Any]      ,    _A							:							int      ,    _A							:							int):



											"""simple docstring"""







											if dst_width < 0 or dst_height < 0:
																		raise ValueError("""Destination width/height should be > 0""")

											_SCREAMING_SNAKE_CASE		:						str			  =    img
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    img.shape[1]
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    img.shape[0]
											_SCREAMING_SNAKE_CASE		:						Any			  =    dst_width
											_SCREAMING_SNAKE_CASE		:						Any			  =    dst_height

											_SCREAMING_SNAKE_CASE		:						Any			  =    self.src_w / self.dst_w
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.src_h / self.dst_h

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (
											    np.ones((self.dst_h, self.dst_w, 3)      ,    np.uinta) * 2_5_5
											)





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											for i in range(self.dst_h):
																		for j in range(self.dst_w):
																									_SCREAMING_SNAKE_CASE		:						Any			  =    self.img[self.get_y(_A)][self.get_x(_A)]





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							int):



											"""simple docstring"""







											return int(self.ratio_x * x)





				def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							int):



											"""simple docstring"""







											return int(self.ratio_y * y)


if __name__ == "__main__":
			lowerCAmelCase_ ,					lowerCAmelCase_     =	800, 600
			lowerCAmelCase_     =	imread('''image_data/lena.jpg''', 1)
			lowerCAmelCase_     =	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()


