





'''simple docstring'''



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast


@require_vision
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       _snake_case     		=				tempfile.mkdtemp()

       _snake_case     		=				BlipImageProcessor()
       _snake_case     		=				GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"	)

       _snake_case     		=				BlipaProcessor(UpperCamelCase__						,   UpperCamelCase__	)

       processor.save_pretrained(self.tmpdirname	)

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       return AutoProcessor.from_pretrained(self.tmpdirname						,   **UpperCamelCase__	).tokenizer

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       return AutoProcessor.from_pretrained(self.tmpdirname						,   **UpperCamelCase__	).image_processor

     def 	UpperCamelCase(					self	):
       shutil.rmtree(self.tmpdirname	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				[np.random.randint(255						,   size=(3, 30, 400)						,   dtype=np.uinta	)]

       _snake_case     		=				[Image.fromarray(np.moveaxis(UpperCamelCase__						,   0						,   -1	)	) for x in image_inputs]

       return image_inputs

     def 	UpperCamelCase(					self	):
       _snake_case     		=				BlipaProcessor(tokenizer=self.get_tokenizer()						,   image_processor=self.get_image_processor()	)
       processor.save_pretrained(self.tmpdirname	)

       _snake_case     		=				self.get_tokenizer(bos_token="(BOS)"						,   eos_token="(EOS)"	)
       _snake_case     		=				self.get_image_processor(do_normalize=UpperCamelCase__						,   padding_value=1.0	)

       _snake_case     		=				BlipaProcessor.from_pretrained(
           self.tmpdirname						,   bos_token="(BOS)"						,   eos_token="(EOS)"						,   do_normalize=UpperCamelCase__						,   padding_value=1.0	)

       self.assertEqual(processor.tokenizer.get_vocab()						,   tokenizer_add_kwargs.get_vocab()	)
       self.assertIsInstance(processor.tokenizer						,   UpperCamelCase__	)

       self.assertEqual(processor.image_processor.to_json_string()						,   image_processor_add_kwargs.to_json_string()	)
       self.assertIsInstance(processor.image_processor						,   UpperCamelCase__	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_image_processor()
       _snake_case     		=				self.get_tokenizer()

       _snake_case     		=				BlipaProcessor(tokenizer=UpperCamelCase__						,   image_processor=UpperCamelCase__	)

       _snake_case     		=				self.prepare_image_inputs()

       _snake_case     		=				image_processor(UpperCamelCase__						,   return_tensors="np"	)
       _snake_case     		=				processor(images=UpperCamelCase__						,   return_tensors="np"	)

       for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum()						,   input_processor[key].sum()						,   delta=1e-2	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_image_processor()
       _snake_case     		=				self.get_tokenizer()

       _snake_case     		=				BlipaProcessor(tokenizer=UpperCamelCase__						,   image_processor=UpperCamelCase__	)

       _snake_case     		=				"lower newer"

       _snake_case     		=				processor(text=UpperCamelCase__	)

       _snake_case     		=				tokenizer(UpperCamelCase__						,   return_token_type_ids=UpperCamelCase__	)

       for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]						,   encoded_processor[key]	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_image_processor()
       _snake_case     		=				self.get_tokenizer()

       _snake_case     		=				BlipaProcessor(tokenizer=UpperCamelCase__						,   image_processor=UpperCamelCase__	)

       _snake_case     		=				"lower newer"
       _snake_case     		=				self.prepare_image_inputs()

       _snake_case     		=				processor(text=UpperCamelCase__						,   images=UpperCamelCase__	)

       self.assertListEqual(list(inputs.keys()	)						,   ["pixel_values", "input_ids", "attention_mask"]	)

       # test if it raises when no input is passed
       with pytest.raises(UpperCamelCase__	):
         processor()

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_image_processor()
       _snake_case     		=				self.get_tokenizer()

       _snake_case     		=				BlipaProcessor(tokenizer=UpperCamelCase__						,   image_processor=UpperCamelCase__	)

       _snake_case     		=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       _snake_case     		=				processor.batch_decode(UpperCamelCase__	)
       _snake_case     		=				tokenizer.batch_decode(UpperCamelCase__	)

       self.assertListEqual(UpperCamelCase__						,   UpperCamelCase__	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_image_processor()
       _snake_case     		=				self.get_tokenizer()

       _snake_case     		=				BlipaProcessor(tokenizer=UpperCamelCase__						,   image_processor=UpperCamelCase__	)

       _snake_case     		=				"lower newer"
       _snake_case     		=				self.prepare_image_inputs()

       _snake_case     		=				processor(text=UpperCamelCase__						,   images=UpperCamelCase__	)

       # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
       self.assertListEqual(list(inputs.keys()	)						,   ["pixel_values", "input_ids", "attention_mask"]	)









'''simple docstring'''



import cva
import numpy as np


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase	):

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

     def __str__(					self	):
       return str(self.k	)






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

           _snake_case     		=				(wxx * wyy) - (wxy**2)
           _snake_case     		=				wxx + wyy
           _snake_case     		=				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__":
 __magic_name__   :						Tuple    =			HarrisCorner(0.04, 3)
 __magic_name__   ,    __magic_name__   :						Any    =			edge_detect.detect("""path_to_image""")
 cva.imwrite("""detect.png""", color_img)


