




from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor







class 						A:



 '''simple docstring'''


 def __init__(       self      :						int		,							A_      :						Optional[int]		,							A_      :						Union[str, Any]=13		,							A_      :						str=30		,							A_      :						Optional[int]=2		,							A_      :						Dict=3		,							A_      :						Any=True		,							A_      :						Optional[Any]=True		,							A_      :						int=32		,							A_      :						Optional[int]=2		,							A_      :						Tuple=4		,							A_      :						Optional[Any]=37		,							A_      :						Dict="gelu"		,							A_      :						List[Any]=0.1		,							A_      :						Optional[Any]=0.1		,							A_      :						Dict=10		,							A_      :						Dict=0.02		,							A_      :						str=3		,							A_      :						Union[str, Any]=None		,							)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					parent
       lowerCamelCase_	  =					batch_size
       lowerCamelCase_	  =					image_size
       lowerCamelCase_	  =					patch_size
       lowerCamelCase_	  =					num_channels
       lowerCamelCase_	  =					is_training
       lowerCamelCase_	  =					use_labels
       lowerCamelCase_	  =					hidden_size
       lowerCamelCase_	  =					num_hidden_layers
       lowerCamelCase_	  =					num_attention_heads
       lowerCamelCase_	  =					intermediate_size
       lowerCamelCase_	  =					hidden_act
       lowerCamelCase_	  =					hidden_dropout_prob
       lowerCamelCase_	  =					attention_probs_dropout_prob
       lowerCamelCase_	  =					type_sequence_label_size
       lowerCamelCase_	  =					initializer_range
       lowerCamelCase_	  =					scope

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

 def        a__				(       self      :						Any						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       lowerCamelCase_	  =					None
       if self.use_labels:
             lowerCamelCase_	  =					ids_tensor([self.batch_size]		,							self.type_sequence_label_size						)

       lowerCamelCase_	  =					self.get_config()

       return config, pixel_values, labels

 def        a__				(       self      :						Optional[Any]						)     ->			List[Any]:

       """simple docstring"""






       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		,							)

 def        a__				(       self      :						int		,							A_      :						Optional[Any]		,							A_      :						Tuple		,							A_      :						List[str]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					TFViTModel(config=A_						)
       lowerCamelCase_	  =					model(A_		,							training=A_						)
       self.parent.assertEqual(result.last_hidden_state.shape		,							(self.batch_size, self.seq_length, self.hidden_size)						)

       # Test with an image with different size than the one specified in config.
       lowerCamelCase_	  =					self.image_size // 2
       lowerCamelCase_	  =					pixel_values[:, :, :image_size, :image_size]
       lowerCamelCase_	  =					model(A_		,							interpolate_pos_encoding=A_		,							training=A_						)
       lowerCamelCase_	  =					(image_size // self.patch_size) ** 2 + 1
       self.parent.assertEqual(result.last_hidden_state.shape		,							(self.batch_size, seq_length, self.hidden_size)						)

 def        a__				(       self      :						List[str]		,							A_      :						int		,							A_      :						Any		,							A_      :						int						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					self.type_sequence_label_size
       lowerCamelCase_	  =					TFViTForImageClassification(A_						)
       lowerCamelCase_	  =					model(A_		,							labels=A_		,							training=A_						)
       self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.type_sequence_label_size)						)

       # Test with an image with different size than the one specified in config.
       lowerCamelCase_	  =					self.image_size // 2
       lowerCamelCase_	  =					pixel_values[:, :, :image_size, :image_size]
       lowerCamelCase_	  =					model(A_		,							interpolate_pos_encoding=A_		,							training=A_						)
       self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.type_sequence_label_size)						)

       # test greyscale images
       lowerCamelCase_	  =					1
       lowerCamelCase_	  =					TFViTForImageClassification(A_						)
       lowerCamelCase_	  =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       lowerCamelCase_	  =					model(A_						)
       self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.type_sequence_label_size)						)





 def        a__				(       self      :						Optional[Any]						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.prepare_config_and_inputs()
       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					config_and_inputs
       lowerCamelCase_	  =					{'pixel_values': pixel_values}
       return config, inputs_dict







@require_tf
class 						A(		UpperCamelCase							,   UpperCamelCase							,   unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
 UpperCamelCase		     =		(
     {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
     if is_tf_available()
     else {}
 )

 UpperCamelCase		     =		False
 UpperCamelCase		     =		False
 UpperCamelCase		     =		False

 def        a__				(       self      :						List[Any]						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					TFViTModelTester(self						)
       lowerCamelCase_	  =					ConfigTester(self		,							config_class=A_		,							has_text_modality=A_		,							hidden_size=37						)

 def        a__				(       self      :						Optional[int]						)     ->			Union[str, Any]:

       """simple docstring"""






       self.config_tester.run_common_tests()

 @unittest.skip(reason='ViT does not use inputs_embeds'						)
 def        a__				(       self      :						Dict						)     ->			List[str]:

       """simple docstring"""






       pass

 @unittest.skip(reason='ViT does not use inputs_embeds'						)
 def        a__				(       self      :						List[str]						)     ->			str:

       """simple docstring"""






       pass

 def        a__				(       self      :						str						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             lowerCamelCase_	  =					model_class(A_						)
             self.assertIsInstance(model.get_input_embeddings()		,							(tf.keras.layers.Layer)						)
             lowerCamelCase_	  =					model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(A_		,							tf.keras.layers.Layer						)						)

 def        a__				(       self      :						str						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs_for_common()

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

             lowerCamelCase_	  =					['pixel_values']
             self.assertListEqual(arg_names[:1]		,							A_						)

 def        a__				(       self      :						Optional[int]						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_						)

 def        a__				(       self      :						Dict						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*A_						)





 @slow
 def        a__				(       self      :						Optional[int]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					TFViTModel.from_pretrained('google/vit-base-patch16-224'						)
       self.assertIsNotNone(A_						)

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
      return image







@require_tf
@require_vision
class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 @cached_property
 def        a__				(       self      :						Tuple						)     ->			Union[str, Any]:

       """simple docstring"""






       return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'						) if is_vision_available() else None





 @slow
 def        a__				(       self      :						Union[str, Any]						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'						)

       lowerCamelCase_	  =					self.default_image_processor
       lowerCamelCase_	  =					prepare_img()
       lowerCamelCase_	  =					image_processor(images=A_		,							return_tensors='tf'						)

       # forward pass
       lowerCamelCase_	  =					model(**A_						)

       # verify the logits
       lowerCamelCase_	  =					tf.TensorShape((1, 1000)						)
       self.assertEqual(outputs.logits.shape		,							A_						)

       lowerCamelCase_	  =					tf.constant([-0.2744, 0.8215, -0.0836]						)

       tf.debugging.assert_near(outputs.logits[0, :3]		,							A_		,							atol=1E-4						)









import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowerCamelCase       :	str									=     imread(r"digital_image_processing/image_data/lena_small.jpg")
lowerCamelCase       :	Optional[Any]									=     cvtColor(img, COLOR_BGR2GRAY)

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					cn.convert_to_negative(lowercase  )
      # assert negative_img array for at least one True
      assert negative_img.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      with Image.open('digital_image_processing/image_data/lena_small.jpg'  ) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(lowercase			,							1_10  )  ).startswith(
                '<PIL.Image.Image image mode=RGB size=100x100 at'  )

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					canny.gen_gaussian_kernel(9			,							sigma=1.4  )
      # Assert ambiguous array
      assert resp.all()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					imread('digital_image_processing/image_data/lena_small.jpg'			,							0  )
      # assert ambiguous array for all == True
      assert canny_img.all()
      lowerCamelCase_	  =					canny.canny(lowercase  )
      # assert canny array for at least one True
      assert canny_array.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      assert gg.gaussian_filter(lowercase			,							5			,							sigma=0.9  ).all()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]  )
      lowerCamelCase_	  =					conv.img_convolve(lowercase			,							lowercase  ).astype(lowercase  )
      assert res.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      assert med.median_filter(lowercase			,							3  ).any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_  ,       lowerCamelCase_	  =					sob.sobel_filter(lowercase  )
      assert grad.any() and theta.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					sp.make_sepia(lowercase			,							20  )
      assert sepia.all()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str = "digital_image_processing/image_data/lena_small.jpg"  ):


      '''simple docstring'''



      lowerCamelCase_	  =					bs.Burkes(imread(lowercase			,							1  )			,							1_20  )
      burkes.process()
      assert burkes.output_img.any()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str = "digital_image_processing/image_data/lena_small.jpg"			,							):


      '''simple docstring'''



      lowerCamelCase_	  =					rs.NearestNeighbour(imread(lowercase			,							1  )			,							4_00			,							2_00  )
      nn.process()
      assert nn.output.any()






def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					'digital_image_processing/image_data/lena.jpg'

      # Reading the image and converting it to grayscale.
      lowerCamelCase_	  =					imread(lowercase			,							0  )

      # Test for get_neighbors_pixel function() return not None
      lowerCamelCase_	  =					0
      lowerCamelCase_	  =					0
      lowerCamelCase_	  =					image[x_coordinate][y_coordinate]

      lowerCamelCase_	  =					lbp.get_neighbors_pixel(
          lowercase			,							lowercase			,							lowercase			,							lowercase  )

      assert neighbors_pixels is not None

      # Test for local_binary_pattern function()
      # Create a numpy array as the same height and width of read image
      lowerCamelCase_	  =					np.zeros((image.shape[0], image.shape[1])  )

      # Iterating through the image and calculating the local binary pattern value
      # for each pixel.
      for i in range(0			,							image.shape[0]  ):
            for j in range(0			,							image.shape[1]  ):
                  lowerCamelCase_	  =					lbp.local_binary_value(lowercase			,							lowercase			,							lowercase  )

      assert lbp_image.any()



