






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    a   (			unittest.TestCase       ):
      def __init__(				self :      Union[str, Any]		,				__lowercase :      int		,				__lowercase :      Optional[Any]=7		,				__lowercase :      int=3		,				__lowercase :      int=18		,				__lowercase :      int=30		,				__lowercase :      Any=400		,				__lowercase :      Any=True		,				__lowercase :      Optional[Any]=1 / 255		,				__lowercase :      List[str]=True		,				__lowercase :      Any=8		,				)							->				str:
        __UpperCAmelCase							:     List[str]									=  parent
        __UpperCAmelCase							:     Tuple									=  batch_size
        __UpperCAmelCase							:     int									=  num_channels
        __UpperCAmelCase							:     List[Any]									=  image_size
        __UpperCAmelCase							:     Optional[Any]									=  min_resolution
        __UpperCAmelCase							:     List[str]									=  max_resolution
        __UpperCAmelCase							:     List[str]									=  do_rescale
        __UpperCAmelCase							:     Optional[Any]									=  rescale_factor
        __UpperCAmelCase							:     str									=  do_pad
        __UpperCAmelCase							:     Optional[Any]									=  pad_size
      def      UpperCAmelCase				(				self :      List[str] )							->				Union[str, Any]:
        return {
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
            "pad_size": self.pad_size,
        }




      def      UpperCAmelCase				(				self :      str		,				__lowercase :      Optional[Any]=False		,				__lowercase :      Tuple=False		,				__lowercase :      List[Any]=False )							->				Dict:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
          __UpperCAmelCase							:     Optional[Any]									=  []
          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:
          __UpperCAmelCase							:     str									=  []
          for i in range(self.batch_size ):
            __UpperCAmelCase				,			__UpperCAmelCase							:     List[str]									=  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
          __UpperCAmelCase							:     Any									=  [Image.fromarray(np.moveaxis(UpperCamelCase_		,				0		,				-1 ) ) for x in image_inputs]

        if torchify:
          __UpperCAmelCase							:     List[Any]									=  [torch.from_numpy(UpperCamelCase_ ) for x in image_inputs]

        return image_inputs


@require_torch
@require_vision
class    a   (			lowercase_							,	unittest.TestCase       ):
      a  :			Optional[Any]								=     SwinaSRImageProcessor if is_vision_available() else None
      def      UpperCAmelCase				(				self :      str )							->				Optional[Any]:
        __UpperCAmelCase							:     int									=  SwinaSRImageProcessingTester(self )
      @property
      def      UpperCAmelCase				(				self :      Tuple )							->				Any:
        return self.image_processor_tester.prepare_image_processor_dict()
      def      UpperCAmelCase				(				self :      int )							->				str:
        __UpperCAmelCase							:     List[Any]									=  self.image_processing_class(**self.image_processor_dict )
        self.assertTrue(hasattr(UpperCamelCase_		,				"""do_rescale""" ) )
        self.assertTrue(hasattr(UpperCamelCase_		,				"""rescale_factor""" ) )
        self.assertTrue(hasattr(UpperCamelCase_		,				"""do_pad""" ) )
        self.assertTrue(hasattr(UpperCamelCase_		,				"""pad_size""" ) )
      def      UpperCAmelCase				(				self :      List[str] )							->				Optional[Any]:
        pass
      def      UpperCAmelCase				(				self :      Tuple		,				__lowercase :      List[Any] )							->				Union[str, Any]:
        __UpperCAmelCase				,			__UpperCAmelCase							:     Dict									=  get_image_size(UpperCamelCase_ )
        __UpperCAmelCase							:     List[str]									=  self.image_processor_tester.pad_size

        __UpperCAmelCase							:     Any									=  (old_height // size + 1) * size - old_height
        __UpperCAmelCase							:     List[Any]									=  (old_width // size + 1) * size - old_width
        return old_height + pad_height, old_width + pad_width
      def      UpperCAmelCase				(				self :      Optional[int] )							->				str:
        __UpperCAmelCase							:     str									=  self.image_processing_class(**self.image_processor_dict )
        # create random PIL images
        __UpperCAmelCase							:     List[Any]									=  self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase_ )
        for image in image_inputs:
          self.assertIsInstance(UpperCamelCase_		,				Image.Image )

        # Test not batched input
        __UpperCAmelCase							:     Union[str, Any]									=  image_processor(image_inputs[0]		,				return_tensors="""pt""" ).pixel_values
        __UpperCAmelCase				,			__UpperCAmelCase							:     str									=  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      UpperCAmelCase				(				self :      Any )							->				Tuple:
        __UpperCAmelCase							:     str									=  self.image_processing_class(**self.image_processor_dict )
        # create random numpy tensors
        __UpperCAmelCase							:     Dict									=  self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase_		,				numpify=UpperCamelCase_ )
        for image in image_inputs:
          self.assertIsInstance(UpperCamelCase_		,				np.ndarray )

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




      def      UpperCAmelCase				(				self :      Any )							->				Union[str, Any]:
        __UpperCAmelCase							:     Dict									=  self.image_processing_class(**self.image_processor_dict )
        # create random PyTorch tensors
        __UpperCAmelCase							:     Union[str, Any]									=  self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase_		,				torchify=UpperCamelCase_ )
        for image in image_inputs:
          self.assertIsInstance(UpperCamelCase_		,				torch.Tensor )

        # Test not batched input
        __UpperCAmelCase							:     Optional[int]									=  image_processor(image_inputs[0]		,				return_tensors="""pt""" ).pixel_values
        __UpperCAmelCase				,			__UpperCAmelCase							:     Union[str, Any]									=  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.neural_network import MLPClassifier

a		:	int      					=			[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
a		:	List[str]      					=			[0, 1, 0, 0]


a		:	str      					=			MLPClassifier(
    solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


a		:	Union[str, Any]      					=			[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
a		:	Optional[int]      					=			clf.predict(test)






def     lowerCamelCase__				(   __lowerCamelCase      :					Dict		):
  return list(__lowerCamelCase		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


