import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import SwinaSRImageProcessor
	from transformers.image_transforms import get_image_size




class        lowercase_  (  unittest.TestCase						):



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=7		,      __SCREAMING_SNAKE_CASE=3		,      __SCREAMING_SNAKE_CASE=18		,      __SCREAMING_SNAKE_CASE=30		,      __SCREAMING_SNAKE_CASE=400		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE=1 / 255		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE=8		,      )						->int:
								lowerCAmelCase     		=		parent
								lowerCAmelCase     		=		batch_size
								lowerCAmelCase     		=		num_channels
								lowerCAmelCase     		=		image_size
								lowerCAmelCase     		=		min_resolution
								lowerCAmelCase     		=		max_resolution
								lowerCAmelCase     		=		do_rescale
								lowerCAmelCase     		=		rescale_factor
								lowerCAmelCase     		=		do_pad
								lowerCAmelCase     		=		pad_size


	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								return {
								    "do_rescale": self.do_rescale,
								    "rescale_factor": self.rescale_factor,
								    "do_pad": self.do_pad,
								    "pad_size": self.pad_size,
								}




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=False    )						->Optional[Any]:
								assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

								if equal_resolution:
															lowerCAmelCase     		=		[]
															for i in range(self.batch_size    ):
																						image_inputs.append(
																						    np.random.randint(
																						        255		,      size=(self.num_channels, self.max_resolution, self.max_resolution)		,      dtype=np.uinta    )    )
								else:
															lowerCAmelCase     		=		[]
															for i in range(self.batch_size    ):
																						lowerCAmelCase		,				lowerCAmelCase     		=		np.random.choice(np.arange(self.min_resolution		,      self.max_resolution    )		,      2    )
																						image_inputs.append(np.random.randint(255		,      size=(self.num_channels, width, height)		,      dtype=np.uinta    )    )

								if not numpify and not torchify:
															# PIL expects the channel dimension as last dimension
															lowerCAmelCase     		=		[Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE		,      0		,      -1    )    ) for x in image_inputs]

								if torchify:
															lowerCAmelCase     		=		[torch.from_numpy(__SCREAMING_SNAKE_CASE    ) for x in image_inputs]

								return image_inputs




@require_torch
@require_vision
class        lowercase_  (  UpperCamelCase_				,					unittest.TestCase						):



	"""simple docstring"""





	UpperCAmelCase_  : List[str]       =    SwinaSRImageProcessor if is_vision_available() else None


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								lowerCAmelCase     		=		SwinaSRImageProcessingTester(self    )


	@property
	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								return self.image_processor_tester.prepare_image_processor_dict()


	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								lowerCAmelCase     		=		self.image_processing_class(**self.image_processor_dict    )
								self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE		,      '''do_rescale'''    )    )
								self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE		,      '''rescale_factor'''    )    )
								self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE		,      '''do_pad'''    )    )
								self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE		,      '''pad_size'''    )    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								pass


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Tuple:
								lowerCAmelCase		,				lowerCAmelCase     		=		get_image_size(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		self.image_processor_tester.pad_size

								lowerCAmelCase     		=		(old_height // size + 1) * size - old_height
								lowerCAmelCase     		=		(old_width // size + 1) * size - old_width
								return old_height + pad_height, old_width + pad_width


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[int]:
								# Initialize image_processor
								lowerCAmelCase     		=		self.image_processing_class(**self.image_processor_dict    )
								# create random PIL images
								lowerCAmelCase     		=		self.image_processor_tester.prepare_inputs(equal_resolution=__SCREAMING_SNAKE_CASE    )
								for image in image_inputs:
															self.assertIsInstance(__SCREAMING_SNAKE_CASE		,      Image.Image    )

								# Test not batched input
								lowerCAmelCase     		=		image_processor(image_inputs[0]		,      return_tensors='''pt'''    ).pixel_values
								lowerCAmelCase		,				lowerCAmelCase     		=		self.calculate_expected_size(np.array(image_inputs[0]    )    )
								self.assertEqual(
								    encoded_images.shape		,      (
								        1,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )		,      )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								# Initialize image_processor
								lowerCAmelCase     		=		self.image_processing_class(**self.image_processor_dict    )
								# create random numpy tensors
								lowerCAmelCase     		=		self.image_processor_tester.prepare_inputs(equal_resolution=__SCREAMING_SNAKE_CASE		,      numpify=__SCREAMING_SNAKE_CASE    )
								for image in image_inputs:
															self.assertIsInstance(__SCREAMING_SNAKE_CASE		,      np.ndarray    )

								# Test not batched input
								lowerCAmelCase     		=		image_processor(image_inputs[0]		,      return_tensors='''pt'''    ).pixel_values
								lowerCAmelCase		,				lowerCAmelCase     		=		self.calculate_expected_size(image_inputs[0]    )
								self.assertEqual(
								    encoded_images.shape		,      (
								        1,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )		,      )




	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								# Initialize image_processor
								lowerCAmelCase     		=		self.image_processing_class(**self.image_processor_dict    )
								# create random PyTorch tensors
								lowerCAmelCase     		=		self.image_processor_tester.prepare_inputs(equal_resolution=__SCREAMING_SNAKE_CASE		,      torchify=__SCREAMING_SNAKE_CASE    )
								for image in image_inputs:
															self.assertIsInstance(__SCREAMING_SNAKE_CASE		,      torch.Tensor    )

								# Test not batched input
								lowerCAmelCase     		=		image_processor(image_inputs[0]		,      return_tensors='''pt'''    ).pixel_values
								lowerCAmelCase		,				lowerCAmelCase     		=		self.calculate_expected_size(image_inputs[0]    )
								self.assertEqual(
								    encoded_images.shape		,      (
								        1,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )		,      )




from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowercase__      :    int          =     load_iris()
iris.keys()


print(f'Target names: \n {iris.target_names} ')
print(f'\n Features: \n {iris.feature_names}')

# Train set e Test set
lowercase__     ,   lowercase__     ,   lowercase__     ,   lowercase__      :    Any          =     train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

lowercase__      :    int          =     KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowercase__      :    List[str]          =     [[1, 2, 1, 4], [2, 3, 4, 5]]

lowercase__      :    int          =     knn.predict(X_new)

print(
    f'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    f' {iris["target_names"][prediction]}'
)



