





import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class 	A_       (unittest.TestCase			):
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				0
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''		)
				self.assertIsInstance(_A							,					_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase   		=				Path(_A		) / '''preprocessor_config.json'''
							UpperCAmelCase   		=				Path(_A		) / '''config.json'''
							json.dump(
							    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}							,					open(_A							,					'''w'''		)							,					)
							json.dump({'''model_type''': '''clip'''}							,					open(_A							,					'''w'''		)		)

							UpperCAmelCase   		=				AutoImageProcessor.from_pretrained(_A		)
							self.assertIsInstance(_A							,					_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase   		=				Path(_A		) / '''preprocessor_config.json'''
							UpperCAmelCase   		=				Path(_A		) / '''config.json'''
							json.dump(
							    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}							,					open(_A							,					'''w'''		)							,					)
							json.dump({'''model_type''': '''clip'''}							,					open(_A							,					'''w'''		)		)

							UpperCAmelCase   		=				AutoImageProcessor.from_pretrained(_A		)
							self.assertIsInstance(_A							,					_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase   		=				CLIPConfig()

							# Create a dummy config file with image_proceesor_type
							UpperCAmelCase   		=				Path(_A		) / '''preprocessor_config.json'''
							UpperCAmelCase   		=				Path(_A		) / '''config.json'''
							json.dump(
							    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}							,					open(_A							,					'''w'''		)							,					)
							json.dump({'''model_type''': '''clip'''}							,					open(_A							,					'''w'''		)		)

							# remove image_processor_type to make sure config.json alone is enough to load image processor locally
							UpperCAmelCase   		=				AutoImageProcessor.from_pretrained(_A		).to_dict()

							config_dict.pop('''image_processor_type'''		)
							UpperCAmelCase   		=				CLIPImageProcessor(**_A		)

							# save in new folder
							model_config.save_pretrained(_A		)
							config.save_pretrained(_A		)

							UpperCAmelCase   		=				AutoImageProcessor.from_pretrained(_A		)

							# make sure private variable is not incorrectly saved
							UpperCAmelCase   		=				json.loads(config.to_json_string()		)
							self.assertTrue('''_processor_class''' not in dict_as_saved		)

				self.assertIsInstance(_A							,					_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase   		=				Path(_A		) / '''preprocessor_config.json'''
							json.dump(
							    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}							,					open(_A							,					'''w'''		)							,					)

							UpperCAmelCase   		=				AutoImageProcessor.from_pretrained(_A		)
							self.assertIsInstance(_A							,					_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				with self.assertRaisesRegex(
				    _A							,					'''clip-base is not a local folder and is not a valid model identifier'''		):
							UpperCAmelCase   		=				AutoImageProcessor.from_pretrained('''clip-base'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				with self.assertRaisesRegex(
				    _A							,					r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''		):
							UpperCAmelCase   		=				AutoImageProcessor.from_pretrained(_A							,					revision='''aaaaaa'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				with self.assertRaisesRegex(
				    _A							,					'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''							,					):
							UpperCAmelCase   		=				AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				with self.assertRaises(_A		):
							UpperCAmelCase   		=				AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''		)
				# If remote code is disabled, we can't load this config.
				with self.assertRaises(_A		):
							UpperCAmelCase   		=				AutoImageProcessor.from_pretrained(
							    '''hf-internal-testing/test_dynamic_image_processor'''							,					trust_remote_code=_A		)

				UpperCAmelCase   		=				AutoImageProcessor.from_pretrained(
				    '''hf-internal-testing/test_dynamic_image_processor'''							,					trust_remote_code=_A		)
				self.assertEqual(image_processor.__class__.__name__							,					'''NewImageProcessor'''		)

				# Test image processor can be reloaded.
				with tempfile.TemporaryDirectory() as tmp_dir:
							image_processor.save_pretrained(_A		)
							UpperCAmelCase   		=				AutoImageProcessor.from_pretrained(_A							,					trust_remote_code=_A		)
				self.assertEqual(reloaded_image_processor.__class__.__name__							,					'''NewImageProcessor'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				try:
							AutoConfig.register('''custom'''							,					_A		)
							AutoImageProcessor.register(_A							,					_A		)
							# Trying to register something existing in the Transformers library will raise an error
							with self.assertRaises(_A		):
										AutoImageProcessor.register(_A							,					_A		)

							with tempfile.TemporaryDirectory() as tmpdirname:
										UpperCAmelCase   		=				Path(_A		) / '''preprocessor_config.json'''
										UpperCAmelCase   		=				Path(_A		) / '''config.json'''
										json.dump(
										    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}							,					open(_A							,					'''w'''		)							,					)
										json.dump({'''model_type''': '''clip'''}							,					open(_A							,					'''w'''		)		)

										UpperCAmelCase   		=				CustomImageProcessor.from_pretrained(_A		)

							# Now that the config is registered, it can be used as any other config with the auto-API
							with tempfile.TemporaryDirectory() as tmp_dir:
										image_processor.save_pretrained(_A		)
										UpperCAmelCase   		=				AutoImageProcessor.from_pretrained(_A		)
										self.assertIsInstance(_A							,					_A		)

				finally:
							if "custom" in CONFIG_MAPPING._extra_content:
										del CONFIG_MAPPING._extra_content["custom"]
							if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
										del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]







	def      _lowercase				(    self		):






				'''simple docstring'''







				class 	A_       (a_			):
					UpperCAmelCase__					=			True

				try:
							AutoConfig.register('''custom'''							,					_A		)
							AutoImageProcessor.register(_A							,					_A		)
							# If remote code is not set, the default is to use local
							UpperCAmelCase   		=				AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''		)
							self.assertEqual(image_processor.__class__.__name__							,					'''NewImageProcessor'''		)
							self.assertTrue(image_processor.is_local		)

							# If remote code is disabled, we load the local one.
							UpperCAmelCase   		=				AutoImageProcessor.from_pretrained(
							    '''hf-internal-testing/test_dynamic_image_processor'''							,					trust_remote_code=_A		)
							self.assertEqual(image_processor.__class__.__name__							,					'''NewImageProcessor'''		)
							self.assertTrue(image_processor.is_local		)

							# If remote is enabled, we load from the Hub
							UpperCAmelCase   		=				AutoImageProcessor.from_pretrained(
							    '''hf-internal-testing/test_dynamic_image_processor'''							,					trust_remote_code=_A		)
							self.assertEqual(image_processor.__class__.__name__							,					'''NewImageProcessor'''		)
							self.assertTrue(not hasattr(_A							,					'''is_local'''		)		)

				finally:
							if "custom" in CONFIG_MAPPING._extra_content:
										del CONFIG_MAPPING._extra_content["custom"]
							if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
										del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]










import cva
import numpy as np

class 	A_       :
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				if k in (0.04, 0.06):
							UpperCAmelCase   		=				k
							UpperCAmelCase   		=				window_size
				else:
							raise ValueError('''invalid k value'''		)
	def __str__(    self		):






				'''simple docstring'''







				return str(self.k		)







	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







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

										UpperCAmelCase   		=				(wxx * wyy) - (wxy**2)
										UpperCAmelCase   		=				wxx + wyy
										UpperCAmelCase   		=				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)							,					2_5_5		)
				return color_img, corner_list


if __name__ == "__main__":
				__A				:    Tuple       =   HarrisCorner(0.04, 3)
				__A    , __A				:    List[Any]       =   edge_detect.detect("path_to_image")
				cva.imwrite("detect.png", color_img)



