





'''simple docstring'''






import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
      import jax

      from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel







class 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  def __init__(				self    :					int							,	UpperCamelCase__    :					Dict							,	UpperCamelCase__    :					Union[str, Any]=13							,	UpperCamelCase__    :					Optional[Any]=30							,	UpperCamelCase__    :					List[str]=2							,	UpperCamelCase__    :					Tuple=3							,	UpperCamelCase__    :					Dict=True							,	UpperCamelCase__    :					Optional[Any]=True							,	UpperCamelCase__    :					str=32							,	UpperCamelCase__    :					Union[str, Any]=5							,	UpperCamelCase__    :					List[str]=4							,	UpperCamelCase__    :					Union[str, Any]=37							,	UpperCamelCase__    :					Optional[Any]="gelu"							,	UpperCamelCase__    :					Any=0.1							,	UpperCamelCase__    :					List[str]=0.1							,	UpperCamelCase__    :					int=10							,	UpperCamelCase__    :					str=0.02							,	)     ->						int:
    _UpperCamelCase       			=parent
    _UpperCamelCase       			=batch_size
    _UpperCamelCase       			=image_size
    _UpperCamelCase       			=patch_size
    _UpperCamelCase       			=num_channels
    _UpperCamelCase       			=is_training
    _UpperCamelCase       			=use_labels
    _UpperCamelCase       			=hidden_size
    _UpperCamelCase       			=num_hidden_layers
    _UpperCamelCase       			=num_attention_heads
    _UpperCamelCase       			=intermediate_size
    _UpperCamelCase       			=hidden_act
    _UpperCamelCase       			=hidden_dropout_prob
    _UpperCamelCase       			=attention_probs_dropout_prob
    _UpperCamelCase       			=type_sequence_label_size
    _UpperCamelCase       			=initializer_range

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


  def   UpperCamelCase__      (				self    :					int  )     ->						str:
    _UpperCamelCase       			=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

    _UpperCamelCase       			=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=UpperCamelCase__							,	initializer_range=self.initializer_range							,	)

    return config, pixel_values


  def   UpperCamelCase__      (				self    :					str							,	UpperCamelCase__    :					List[str]							,	UpperCamelCase__    :					str  )     ->						Union[str, Any]:
    _UpperCamelCase       			=FlaxViTModel(config=UpperCamelCase__  )
    _UpperCamelCase       			=model(UpperCamelCase__  )
    # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
    _UpperCamelCase       			=(self.image_size, self.image_size)
    _UpperCamelCase       			=(self.patch_size, self.patch_size)
    _UpperCamelCase       			=(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
    self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, num_patches + 1, self.hidden_size)  )


  def   UpperCamelCase__      (				self    :					Optional[Any]							,	UpperCamelCase__    :					Any							,	UpperCamelCase__    :					Optional[int]  )     ->						Any:
    _UpperCamelCase       			=self.type_sequence_label_size
    _UpperCamelCase       			=FlaxViTForImageClassification(config=UpperCamelCase__  )
    _UpperCamelCase       			=model(UpperCamelCase__  )
    self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.type_sequence_label_size)  )

    # test greyscale images
    _UpperCamelCase       			=1
    _UpperCamelCase       			=FlaxViTForImageClassification(UpperCamelCase__  )

    _UpperCamelCase       			=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
    _UpperCamelCase       			=model(UpperCamelCase__  )




  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						int:
    _UpperCamelCase       			=self.prepare_config_and_inputs()
    (
        (
        _UpperCamelCase     
    )			,     (
        _UpperCamelCase     
    )			,          
    )       			=config_and_inputs
    _UpperCamelCase       			={'''pixel_values''': pixel_values}
    return config, inputs_dict







@require_flax
class 		UpperCAmelCase   (				lowercase_   ,     unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


  def   UpperCamelCase__      (				self    :					Dict  )     ->						None:
    _UpperCamelCase       			=FlaxViTModelTester(self  )
    _UpperCamelCase       			=ConfigTester(self							,	config_class=UpperCamelCase__							,	has_text_modality=UpperCamelCase__							,	hidden_size=37  )


  def   UpperCamelCase__      (				self    :					Dict  )     ->						int:
    self.config_tester.run_common_tests()


  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Union[str, Any]:
    _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Any:
    _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Any  )     ->						Optional[Any]:
    _UpperCamelCase			,     _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      _UpperCamelCase       			=model_class(UpperCamelCase__  )
      _UpperCamelCase       			=inspect.signature(model.__call__  )
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      _UpperCamelCase       			=[*signature.parameters.keys()]

      _UpperCamelCase       			=['''pixel_values''']
      self.assertListEqual(arg_names[:1]							,	UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					int  )     ->						List[Any]:
    _UpperCamelCase			,     _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__  ):
        _UpperCamelCase       			=self._prepare_for_class(UpperCamelCase__							,	UpperCamelCase__  )
        _UpperCamelCase       			=model_class(UpperCamelCase__  )

        @jax.jit
        def model_jitted(UpperCamelCase__    :					str							,	**UpperCamelCase__    :					List[Any]  ):
          return model(pixel_values=UpperCamelCase__							,	**UpperCamelCase__  )

        with self.subTest('''JIT Enabled'''  ):
          _UpperCamelCase       			=model_jitted(**UpperCamelCase__  ).to_tuple()

        with self.subTest('''JIT Disabled'''  ):
          with jax.disable_jit():
            _UpperCamelCase       			=model_jitted(**UpperCamelCase__  ).to_tuple()

        self.assertEqual(len(UpperCamelCase__  )							,	len(UpperCamelCase__  )  )
        for jitted_output, output in zip(UpperCamelCase__							,	UpperCamelCase__  ):
          self.assertEqual(jitted_output.shape							,	output.shape  )




  @slow
  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Any:
    for model_class_name in self.all_model_classes:
      _UpperCamelCase       			=model_class_name.from_pretrained('''google/vit-base-patch16-224'''  )
      _UpperCamelCase       			=model(np.ones((1, 3, 224, 224)  )  )
      self.assertIsNotNone(UpperCamelCase__  )













'''simple docstring'''






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=cva.getAffineTransform(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  return cva.warpAffine(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      (rows, cols)   )


if __name__ == "__main__":
      # read original image
      __lowerCamelCase						: List[Any]		=   cva.imread(
          str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
      )
      # turn image in gray scale value
      __lowerCamelCase						: List[Any]		=   cva.cvtColor(image, cva.COLOR_BGR2GRAY)
      # get image shape
      __lowerCamelCase		, __lowerCamelCase						: Any		=   gray_img.shape

      # set different points to rotate image
      __lowerCamelCase						: Optional[Any]		=   np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
      __lowerCamelCase						: Any		=   np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
      __lowerCamelCase						: int		=   np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
      __lowerCamelCase						: List[Any]		=   np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

      # add all rotated images in a list
      __lowerCamelCase						: int		=   [
          gray_img,
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      ]

      # plot different image rotations
      __lowerCamelCase						: Optional[Any]		=   plt.figure(1)
      __lowerCamelCase						: Tuple		=   ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
      for i, image in enumerate(images):
            plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
            plt.title(titles[i])
            plt.axis('off')
            plt.subplots_adjust(left=0.0, bottom=0.0_5, right=1.0, top=0.9_5)
      plt.show()






