






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt

def    UpperCamelCase						(	_A,   _A,   _A,   _A,   _A			):






						"""simple docstring"""







						__magic_name__      :       int       						= cva.getAffineTransform(_A,   _A			)
						return cva.warpAffine(_A,   _A,   (rows, cols)			)


if __name__ == "__main__":
		# read original image
		__magic_name__:					Optional[int]							=     cva.imread(
		    str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
		)
		# turn image in gray scale value
		__magic_name__:					Tuple							=     cva.cvtColor(image, cva.COLOR_BGR2GRAY)
		# get image shape
		__magic_name__		,						__magic_name__:					Dict							=     gray_img.shape

		# set different points to rotate image
		__magic_name__:					Dict							=     np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
		__magic_name__:					List[Any]							=     np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
		__magic_name__:					Tuple							=     np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
		__magic_name__:					List[str]							=     np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

		# add all rotated images in a list
		__magic_name__:					Optional[Any]							=     [
		    gray_img,
		    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
		    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
		    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
		]

		# plot different image rotations
		__magic_name__:					Union[str, Any]							=     plt.figure(1)
		__magic_name__:					Any							=     ["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
		for i, image in enumerate(images):
				plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
				plt.title(titles[i])
				plt.axis("off")
				plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
		plt.show()








import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import OwlViTImageProcessor, OwlViTProcessor





@require_vision
class 	snake_case__ (							unittest.TestCase				):
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       List[str]       						= tempfile.mkdtemp()

							# fmt: off
							__magic_name__      :       Union[str, Any]       						= ["""""", """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
							# fmt: on
							__magic_name__      :       Any       						= dict(zip(lowerCAmelCase__      ,       range(len(lowerCAmelCase__			)			)			)			)
							__magic_name__      :       int       						= ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
							__magic_name__      :       Any       						= {"""unk_token""": """<unk>"""}

							__magic_name__      :       Optional[Any]       						= os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES["""vocab_file"""]			)
							__magic_name__      :       Optional[int]       						= os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES["""merges_file"""]			)
							with open(self.vocab_file      ,       """w"""      ,       encoding="""utf-8"""			) as fp:
													fp.write(json.dumps(lowerCAmelCase__			) + """\n"""			)
							with open(self.merges_file      ,       """w"""      ,       encoding="""utf-8"""			) as fp:
													fp.write("""\n""".join(lowerCAmelCase__			)			)

							__magic_name__      :       int       						= {
							    """do_resize""": True,
							    """size""": 20,
							    """do_center_crop""": True,
							    """crop_size""": 18,
							    """do_normalize""": True,
							    """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
							    """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
							}
							__magic_name__      :       List[str]       						= os.path.join(self.tmpdirname      ,       lowerCAmelCase__			)
							with open(self.image_processor_file      ,       """w"""      ,       encoding="""utf-8"""			) as fp:
													json.dump(lowerCAmelCase__      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				List[str]:
							return CLIPTokenizer.from_pretrained(self.tmpdirname      ,       pad_token="""!"""      ,       **lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				Any:
							return CLIPTokenizerFast.from_pretrained(self.tmpdirname      ,       pad_token="""!"""      ,       **lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				Optional[Any]:
							return OwlViTImageProcessor.from_pretrained(self.tmpdirname      ,       **lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				int:
							shutil.rmtree(self.tmpdirname			)
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       str       						= [np.random.randint(2_55      ,       size=(3, 30, 4_00)      ,       dtype=np.uinta			)]

							__magic_name__      :       Any       						= [Image.fromarray(np.moveaxis(lowerCAmelCase__      ,       0      ,       -1			)			) for x in image_inputs]

							return image_inputs
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       Any       						= self.get_tokenizer()
							__magic_name__      :       str       						= self.get_rust_tokenizer()
							__magic_name__      :       Tuple       						= self.get_image_processor()

							__magic_name__      :       List[str]       						= OwlViTProcessor(tokenizer=lowerCAmelCase__      ,       image_processor=lowerCAmelCase__			)
							processor_slow.save_pretrained(self.tmpdirname			)
							__magic_name__      :       Optional[int]       						= OwlViTProcessor.from_pretrained(self.tmpdirname      ,       use_fast=lowerCAmelCase__			)

							__magic_name__      :       int       						= OwlViTProcessor(tokenizer=lowerCAmelCase__      ,       image_processor=lowerCAmelCase__			)
							processor_fast.save_pretrained(self.tmpdirname			)
							__magic_name__      :       List[Any]       						= OwlViTProcessor.from_pretrained(self.tmpdirname			)

							self.assertEqual(processor_slow.tokenizer.get_vocab()      ,       tokenizer_slow.get_vocab()			)
							self.assertEqual(processor_fast.tokenizer.get_vocab()      ,       tokenizer_fast.get_vocab()			)
							self.assertEqual(tokenizer_slow.get_vocab()      ,       tokenizer_fast.get_vocab()			)
							self.assertIsInstance(processor_slow.tokenizer      ,       lowerCAmelCase__			)
							self.assertIsInstance(processor_fast.tokenizer      ,       lowerCAmelCase__			)

							self.assertEqual(processor_slow.image_processor.to_json_string()      ,       image_processor.to_json_string()			)
							self.assertEqual(processor_fast.image_processor.to_json_string()      ,       image_processor.to_json_string()			)
							self.assertIsInstance(processor_slow.image_processor      ,       lowerCAmelCase__			)
							self.assertIsInstance(processor_fast.image_processor      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__      :       Optional[Any]       						= OwlViTProcessor(tokenizer=self.get_tokenizer()      ,       image_processor=self.get_image_processor()			)
							processor.save_pretrained(self.tmpdirname			)

							__magic_name__      :       str       						= self.get_tokenizer(bos_token="""(BOS)"""      ,       eos_token="""(EOS)"""			)
							__magic_name__      :       Any       						= self.get_image_processor(do_normalize=lowerCAmelCase__			)

							__magic_name__      :       Tuple       						= OwlViTProcessor.from_pretrained(
							    self.tmpdirname      ,       bos_token="""(BOS)"""      ,       eos_token="""(EOS)"""      ,       do_normalize=lowerCAmelCase__			)

							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__			)
	def       __magic_name__    (    self			)							->				Dict:
							__magic_name__      :       int       						= self.get_image_processor()
							__magic_name__      :       int       						= self.get_tokenizer()

							__magic_name__      :       Union[str, Any]       						= OwlViTProcessor(tokenizer=lowerCAmelCase__      ,       image_processor=lowerCAmelCase__			)

							__magic_name__      :       Dict       						= self.prepare_image_inputs()

							__magic_name__      :       Any       						= image_processor(lowerCAmelCase__      ,       return_tensors="""np"""			)
							__magic_name__      :       str       						= processor(images=lowerCAmelCase__      ,       return_tensors="""np"""			)

							for key in input_image_proc.keys():
													self.assertAlmostEqual(input_image_proc[key].sum()      ,       input_processor[key].sum()      ,       delta=1e-2			)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Union[str, Any]       						= self.get_image_processor()
							__magic_name__      :       int       						= self.get_tokenizer()

							__magic_name__      :       int       						= OwlViTProcessor(tokenizer=lowerCAmelCase__      ,       image_processor=lowerCAmelCase__			)

							__magic_name__      :       Optional[int]       						= """lower newer"""

							__magic_name__      :       Tuple       						= processor(text=lowerCAmelCase__      ,       return_tensors="""np"""			)

							__magic_name__      :       Optional[int]       						= tokenizer(lowerCAmelCase__      ,       return_tensors="""np"""			)

							for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key][0].tolist()      ,       encoded_processor[key][0].tolist()			)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Tuple       						= self.get_image_processor()
							__magic_name__      :       Union[str, Any]       						= self.get_tokenizer()

							__magic_name__      :       List[str]       						= OwlViTProcessor(tokenizer=lowerCAmelCase__      ,       image_processor=lowerCAmelCase__			)

							__magic_name__      :       Any       						= """lower newer"""
							__magic_name__      :       Union[str, Any]       						= self.prepare_image_inputs()

							__magic_name__      :       int       						= processor(text=lowerCAmelCase__      ,       images=lowerCAmelCase__			)

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

							# test if it raises when no input is passed
							with pytest.raises(lowerCAmelCase__			):
													processor()
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Dict       						= """google/owlvit-base-patch32"""
							__magic_name__      :       int       						= OwlViTProcessor.from_pretrained(lowerCAmelCase__			)

							__magic_name__      :       List[Any]       						= ["""cat""", """nasa badge"""]
							__magic_name__      :       Any       						= processor(text=lowerCAmelCase__			)

							__magic_name__      :       Dict       						= 16
							self.assertListEqual(list(inputs.keys()			)      ,       ["""input_ids""", """attention_mask"""]			)
							self.assertEqual(inputs["""input_ids"""].shape      ,       (2, seq_length)			)

							# test if it raises when no input is passed
							with pytest.raises(lowerCAmelCase__			):
													processor()
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       List[str]       						= """google/owlvit-base-patch32"""
							__magic_name__      :       Optional[Any]       						= OwlViTProcessor.from_pretrained(lowerCAmelCase__			)

							__magic_name__      :       Tuple       						= [["""cat""", """nasa badge"""], ["""person"""]]
							__magic_name__      :       Tuple       						= processor(text=lowerCAmelCase__			)

							__magic_name__      :       str       						= 16
							__magic_name__      :       str       						= len(lowerCAmelCase__			)
							__magic_name__      :       int       						= max([len(lowerCAmelCase__			) for texts in input_texts]			)

							self.assertListEqual(list(inputs.keys()			)      ,       ["""input_ids""", """attention_mask"""]			)
							self.assertEqual(inputs["""input_ids"""].shape      ,       (batch_size * num_max_text_queries, seq_length)			)

							# test if it raises when no input is passed
							with pytest.raises(lowerCAmelCase__			):
													processor()
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Optional[int]       						= """google/owlvit-base-patch32"""
							__magic_name__      :       Any       						= OwlViTProcessor.from_pretrained(lowerCAmelCase__			)

							__magic_name__      :       str       						= ["""cat""", """nasa badge"""]
							__magic_name__      :       List[str]       						= processor(text=lowerCAmelCase__			)

							__magic_name__      :       List[Any]       						= 16
							__magic_name__      :       Any       						= inputs["""input_ids"""]
							__magic_name__      :       Optional[Any]       						= [
							    [4_94_06, 23_68, 4_94_07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
							    [4_94_06, 68_41, 1_13_01, 4_94_07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
							]

							self.assertListEqual(list(inputs.keys()			)      ,       ["""input_ids""", """attention_mask"""]			)
							self.assertEqual(inputs["""input_ids"""].shape      ,       (2, seq_length)			)
							self.assertListEqual(list(input_ids[0]			)      ,       predicted_ids[0]			)
							self.assertListEqual(list(input_ids[1]			)      ,       predicted_ids[1]			)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       List[str]       						= self.get_image_processor()
							__magic_name__      :       Dict       						= self.get_tokenizer()

							__magic_name__      :       Tuple       						= OwlViTProcessor(tokenizer=lowerCAmelCase__      ,       image_processor=lowerCAmelCase__			)

							__magic_name__      :       Tuple       						= self.prepare_image_inputs()
							__magic_name__      :       List[Any]       						= self.prepare_image_inputs()

							__magic_name__      :       List[str]       						= processor(images=lowerCAmelCase__      ,       query_images=lowerCAmelCase__			)

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

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







	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Optional[Any]       						= self.get_image_processor()
							__magic_name__      :       List[Any]       						= self.get_tokenizer()

							__magic_name__      :       Tuple       						= OwlViTProcessor(tokenizer=lowerCAmelCase__      ,       image_processor=lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							__magic_name__      :       Optional[Any]       						= processor.batch_decode(lowerCAmelCase__			)
							__magic_name__      :       Optional[int]       						= tokenizer.batch_decode(lowerCAmelCase__			)

							self.assertListEqual(lowerCAmelCase__      ,       lowerCAmelCase__			)
