




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,
			    BertTokenizerFast,
			    BlipImageProcessor,
			    GPTaTokenizer,
			    InstructBlipProcessor,
			    PreTrainedTokenizerFast,
			)





@require_vision
class 	lowerCamelCase_							(				unittest.TestCase ):


	'''simple docstring'''







	def 				lowerCAmelCase_  (							self  :		Dict						):
							SCREAMING_SNAKE_CASE_     						=      tempfile.mkdtemp()

							SCREAMING_SNAKE_CASE_     						=      BlipImageProcessor()
							SCREAMING_SNAKE_CASE_     						=      GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'						)
							SCREAMING_SNAKE_CASE_     						=      BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert'						)

							SCREAMING_SNAKE_CASE_     						=      InstructBlipProcessor(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)

							processor.save_pretrained(self.tmpdirname						)



	def 				lowerCAmelCase_  (							self  :		Any    ,    **_lowerCAmelCase  :		Dict						):
							return AutoProcessor.from_pretrained(self.tmpdirname    ,    **_lowerCAmelCase						).tokenizer



	def 				lowerCAmelCase_  (							self  :		Tuple    ,    **_lowerCAmelCase  :		List[str]						):
							return AutoProcessor.from_pretrained(self.tmpdirname    ,    **_lowerCAmelCase						).image_processor



	def 				lowerCAmelCase_  (							self  :		Optional[int]    ,    **_lowerCAmelCase  :		str						):
							return AutoProcessor.from_pretrained(self.tmpdirname    ,    **_lowerCAmelCase						).qformer_tokenizer



	def 				lowerCAmelCase_  (							self  :		str						):
							shutil.rmtree(self.tmpdirname						)



	def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
							SCREAMING_SNAKE_CASE_     						=      [np.random.randint(255    ,    size=(3, 30, 400)    ,    dtype=np.uinta						)]

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

							return image_inputs



	def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
							SCREAMING_SNAKE_CASE_     						=      InstructBlipProcessor(
							    tokenizer=self.get_tokenizer()    ,    image_processor=self.get_image_processor()    ,    qformer_tokenizer=self.get_qformer_tokenizer()    ,    )
							processor.save_pretrained(self.tmpdirname						)

							SCREAMING_SNAKE_CASE_     						=      self.get_tokenizer(bos_token='(BOS)'    ,    eos_token='(EOS)'						)
							SCREAMING_SNAKE_CASE_     						=      self.get_image_processor(do_normalize=_lowerCAmelCase    ,    padding_value=1.0						)

							SCREAMING_SNAKE_CASE_     						=      InstructBlipProcessor.from_pretrained(
							    self.tmpdirname    ,    bos_token='(BOS)'    ,    eos_token='(EOS)'    ,    do_normalize=_lowerCAmelCase    ,    padding_value=1.0						)

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

							self.assertEqual(processor.image_processor.to_json_string()    ,    image_processor_add_kwargs.to_json_string()						)
							self.assertIsInstance(processor.image_processor    ,    _lowerCAmelCase						)
							self.assertIsInstance(processor.qformer_tokenizer    ,    _lowerCAmelCase						)



	def 				lowerCAmelCase_  (							self  :		str						):
							SCREAMING_SNAKE_CASE_     						=      self.get_image_processor()
							SCREAMING_SNAKE_CASE_     						=      self.get_tokenizer()
							SCREAMING_SNAKE_CASE_     						=      self.get_qformer_tokenizer()

							SCREAMING_SNAKE_CASE_     						=      InstructBlipProcessor(
							    tokenizer=_lowerCAmelCase    ,    image_processor=_lowerCAmelCase    ,    qformer_tokenizer=_lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_     						=      image_processor(_lowerCAmelCase    ,    return_tensors='np'						)
							SCREAMING_SNAKE_CASE_     						=      processor(images=_lowerCAmelCase    ,    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 				lowerCAmelCase_  (							self  :		Dict						):
							SCREAMING_SNAKE_CASE_     						=      self.get_image_processor()
							SCREAMING_SNAKE_CASE_     						=      self.get_tokenizer()
							SCREAMING_SNAKE_CASE_     						=      self.get_qformer_tokenizer()

							SCREAMING_SNAKE_CASE_     						=      InstructBlipProcessor(
							    tokenizer=_lowerCAmelCase    ,    image_processor=_lowerCAmelCase    ,    qformer_tokenizer=_lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      'lower newer'

							SCREAMING_SNAKE_CASE_     						=      processor(text=_lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      tokenizer(_lowerCAmelCase    ,    return_token_type_ids=_lowerCAmelCase						)
							SCREAMING_SNAKE_CASE_     						=      qformer_tokenizer(_lowerCAmelCase    ,    return_token_type_ids=_lowerCAmelCase						)

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

							for key in encoded_tokens_qformer.keys():
													self.assertListEqual(encoded_tokens_qformer[key]    ,    encoded_processor['qformer_' + key]						)



	def 				lowerCAmelCase_  (							self  :		Any						):
							SCREAMING_SNAKE_CASE_     						=      self.get_image_processor()
							SCREAMING_SNAKE_CASE_     						=      self.get_tokenizer()
							SCREAMING_SNAKE_CASE_     						=      self.get_qformer_tokenizer()

							SCREAMING_SNAKE_CASE_     						=      InstructBlipProcessor(
							    tokenizer=_lowerCAmelCase    ,    image_processor=_lowerCAmelCase    ,    qformer_tokenizer=_lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      'lower newer'
							SCREAMING_SNAKE_CASE_     						=      self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_     						=      processor(text=_lowerCAmelCase    ,    images=_lowerCAmelCase						)

							self.assertListEqual(
							    list(inputs.keys()						)    ,    ['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values']    ,    )

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



	def 				lowerCAmelCase_  (							self  :		List[str]						):
							SCREAMING_SNAKE_CASE_     						=      self.get_image_processor()
							SCREAMING_SNAKE_CASE_     						=      self.get_tokenizer()
							SCREAMING_SNAKE_CASE_     						=      self.get_qformer_tokenizer()

							SCREAMING_SNAKE_CASE_     						=      InstructBlipProcessor(
							    tokenizer=_lowerCAmelCase    ,    image_processor=_lowerCAmelCase    ,    qformer_tokenizer=_lowerCAmelCase						)

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

							SCREAMING_SNAKE_CASE_     						=      processor.batch_decode(_lowerCAmelCase						)
							SCREAMING_SNAKE_CASE_     						=      tokenizer.batch_decode(_lowerCAmelCase						)

							self.assertListEqual(_lowerCAmelCase    ,    _lowerCAmelCase						)




	def 				lowerCAmelCase_  (							self  :		List[Any]						):
							SCREAMING_SNAKE_CASE_     						=      self.get_image_processor()
							SCREAMING_SNAKE_CASE_     						=      self.get_tokenizer()
							SCREAMING_SNAKE_CASE_     						=      self.get_qformer_tokenizer()

							SCREAMING_SNAKE_CASE_     						=      InstructBlipProcessor(
							    tokenizer=_lowerCAmelCase    ,    image_processor=_lowerCAmelCase    ,    qformer_tokenizer=_lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      'lower newer'
							SCREAMING_SNAKE_CASE_     						=      self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_     						=      processor(text=_lowerCAmelCase    ,    images=_lowerCAmelCase						)

							self.assertListEqual(
							    list(inputs.keys()						)    ,    ['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values']    ,    )





import cva
import numpy as np





class 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]    ,    _lowerCAmelCase  :		float    ,    _lowerCAmelCase  :		int						):

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



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




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

                   SCREAMING_SNAKE_CASE_     						=      (wxx * wyy) - (wxy**2)
                   SCREAMING_SNAKE_CASE_     						=      wxx + wyy
                   SCREAMING_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__":
   lowerCamelCase__			:       Optional[int]         =							HarrisCorner(0.04, 3)
   lowerCamelCase__    ,							lowerCamelCase__			:       str         =							edge_detect.detect('path_to_image')
   cva.imwrite('detect.png', color_img)