




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
     TFDeiTForImageClassification,
     TFDeiTForImageClassificationWithTeacher,
     TFDeiTForMaskedImageModeling,
     TFDeiTModel,
 )
 from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DeiTImageProcessor







class 						A:



 '''simple docstring'''


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

       """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
       lowerCamelCase_	  =					encoder_stride

       # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
       lowerCamelCase_	  =					(image_size // patch_size) ** 2
       lowerCamelCase_	  =					num_patches + 2

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

       """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      :						str						)     ->			Optional[Any]:

       """simple docstring"""






       return DeiTConfig(
           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		,							encoder_stride=self.encoder_stride		,							)

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

       """simple docstring"""






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

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

       """simple docstring"""






       lowerCamelCase_	  =					TFDeiTForMaskedImageModeling(config=A_						)
       lowerCamelCase_	  =					model(A_						)
       self.parent.assertEqual(
           result.reconstruction.shape		,							(self.batch_size, self.num_channels, self.image_size, self.image_size)						)

       # test greyscale images
       lowerCamelCase_	  =					1
       lowerCamelCase_	  =					TFDeiTForMaskedImageModeling(A_						)

       lowerCamelCase_	  =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       lowerCamelCase_	  =					model(A_						)
       self.parent.assertEqual(result.reconstruction.shape		,							(self.batch_size, 1, self.image_size, self.image_size)						)

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

       """simple docstring"""






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

       # test greyscale images
       lowerCamelCase_	  =					1
       lowerCamelCase_	  =					TFDeiTForImageClassification(A_						)

       lowerCamelCase_	  =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       lowerCamelCase_	  =					model(A_		,							labels=A_						)
       self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.type_sequence_label_size)						)





 def        a__				(       self      :						Dict						)     ->			str:

       """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		     =		(
     (
         TFDeiTModel,
         TFDeiTForImageClassification,
         TFDeiTForImageClassificationWithTeacher,
         TFDeiTForMaskedImageModeling,
     )
     if is_tf_available()
     else ()
 )
 UpperCamelCase		     =		(
     {
         '''feature-extraction''': TFDeiTModel,
         '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
     }
     if is_tf_available()
     else {}
 )

 UpperCamelCase		     =		False
 UpperCamelCase		     =		False
 UpperCamelCase		     =		False
 UpperCamelCase		     =		False

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

       """simple docstring"""






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

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

       """simple docstring"""






       self.config_tester.run_common_tests()

 @unittest.skip(reason='DeiT does not use inputs_embeds'						)
 def        a__				(       self      :						Optional[int]						)     ->			List[Any]:

       """simple docstring"""






       pass

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

       """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.Dense						)						)

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

       """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      :						List[str]						)     ->			List[Any]:

       """simple docstring"""






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

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

       """simple docstring"""






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

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

       """simple docstring"""






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

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

       """simple docstring"""






       lowerCamelCase_	  =					super()._prepare_for_class(A_		,							A_		,							return_labels=A_						)

       if return_labels:
             if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call						).parameters:
                   del inputs_dict["labels"]

       return inputs_dict





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

       """simple docstring"""






       for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase_	  =					TFDeiTModel.from_pretrained(A_						)
             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      :						Dict						)     ->			Any:

       """simple docstring"""






       return (
           DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'						)
           if is_vision_available()
           else None
       )





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

       """simple docstring"""






       lowerCamelCase_	  =					TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-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([-1.0266, 0.1912, -1.2861]						)

       self.assertTrue(np.allclose(outputs.logits[0, :3]		,							A_		,							atol=1E-4						)						)









import cva
import numpy as np







class 						A:



 '''simple docstring'''


 def __init__(       self      :						int		,							A_      :						float		,							A_      :						int						)     ->			List[Any]:

       """simple docstring"""






       if k in (0.04, 0.06):
             lowerCamelCase_	  =					k
             lowerCamelCase_	  =					window_size
       else:
             raise ValueError('invalid k value'						)

 def __str__(       self      :						str						)     ->			str:

       """simple docstring"""






       return str(self.k						)





 def        a__				(       self      :						Any		,							A_      :						str						)     ->			tuple[cva.Mat, list[list[int]]]:

       """simple docstring"""






       lowerCamelCase_	  =					cva.imread(A_		,							0						)
       lowerCamelCase_  ,       lowerCamelCase_	  =					img.shape
       lowerCamelCase_	  =					[]
       lowerCamelCase_	  =					img.copy()
       lowerCamelCase_	  =					cva.cvtColor(A_		,							cva.COLOR_GRAY2RGB						)
       lowerCamelCase_  ,       lowerCamelCase_	  =					np.gradient(A_						)
       lowerCamelCase_	  =					dx**2
       lowerCamelCase_	  =					dy**2
       lowerCamelCase_	  =					dx * dy
       lowerCamelCase_	  =					0.04
       lowerCamelCase_	  =					self.window_size // 2
       for y in range(A_		,							h - offset						):
             for x in range(A_		,							w - offset						):
                   lowerCamelCase_	  =					ixx[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   lowerCamelCase_	  =					iyy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   lowerCamelCase_	  =					ixy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()

                   lowerCamelCase_	  =					(wxx * wyy) - (wxy**2)
                   lowerCamelCase_	  =					wxx + wyy
                   lowerCamelCase_	  =					det - k * (trace**2)
                   # Can change the value
                   if r > 0.5:
                         corner_list.append([x, y, r]						)
                         color_img.itemset((y, x, 0)		,							0						)
                         color_img.itemset((y, x, 1)		,							0						)
                         color_img.itemset((y, x, 2)		,							255						)
       return color_img, corner_list


if __name__ == "__main__":
 lowerCamelCase       :	Optional[int]									=     HarrisCorner(0.04, 3)
 lowerCamelCase					,					lowerCamelCase       :	Optional[int]									=     edge_detect.detect("path_to_image")
 cva.imwrite("detect.png", color_img)



