import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import OwlViTImageProcessor
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=7       ,  lowerCAmelCase_=3       ,  lowerCAmelCase_=18       ,  lowerCAmelCase_=30       ,  lowerCAmelCase_=400       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=[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]       ,  lowerCAmelCase_=[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]       ,  lowerCAmelCase_=True       ,  ):
		_lowercase   =parent
		_lowercase   =batch_size
		_lowercase   =num_channels
		_lowercase   =image_size
		_lowercase   =min_resolution
		_lowercase   =max_resolution
		_lowercase   =do_resize
		_lowercase   =size if size is not None else {"height": 18, "width": 18}
		_lowercase   =do_center_crop
		_lowercase   =crop_size if crop_size is not None else {"height": 18, "width": 18}
		_lowercase   =do_normalize
		_lowercase   =image_mean
		_lowercase   =image_std
		_lowercase   =do_convert_rgb


	def __lowerCAmelCase      (   self       ):
		return {
		    "do_resize": self.do_resize,
		    "size": self.size,
		    "do_center_crop": self.do_center_crop,
		    "crop_size": self.crop_size,
		    "do_normalize": self.do_normalize,
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_convert_rgb": self.do_convert_rgb,
		}

@require_torch
@require_vision
class 	_a  (							lowerCamelCase_							,		unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					OwlViTImageProcessor if is_vision_available() else None


	def __lowerCAmelCase      (   self       ):
		_lowercase   =OwlViTImageProcessingTester(self       )


	@property
	def __lowerCAmelCase      (   self       ):
		return self.image_processor_tester.prepare_image_processor_dict()


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.image_processing_class(**self.image_processor_dict       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "do_resize"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "size"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "do_center_crop"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "center_crop"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "do_normalize"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "image_mean"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "image_std"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "do_convert_rgb"       )       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.image_processing_class.from_dict(self.image_processor_dict       )
		self.assertEqual(image_processor.size       ,  {"height": 18, "width": 18}       )
		self.assertEqual(image_processor.crop_size       ,  {"height": 18, "width": 18}       )

		_lowercase   =self.image_processing_class.from_dict(self.image_processor_dict       ,  size=42       ,  crop_size=84       )
		self.assertEqual(image_processor.size       ,  {"height": 42, "width": 42}       )
		self.assertEqual(image_processor.crop_size       ,  {"height": 84, "width": 84}       )


	def __lowerCAmelCase      (   self       ):
		# Initialize image_processing
		_lowercase   =self.image_processing_class(**self.image_processor_dict       )
		# create random PIL images
		_lowercase   =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowerCAmelCase_       )

		for image in image_inputs:
			self.assertIsInstance(lowerCAmelCase_       ,  Image.Image       )

		# Test not batched input
		_lowercase   =image_processing(image_inputs[0]       ,  return_tensors="pt"       ).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,  (
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["height"],
		        self.image_processor_tester.crop_size["width"],
		    )       ,  )

		# Test batched
		_lowercase   =image_processing(lowerCAmelCase_       ,  return_tensors="pt"       ).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,  (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["height"],
		        self.image_processor_tester.crop_size["width"],
		    )       ,  )


	def __lowerCAmelCase      (   self       ):
		# Initialize image_processing
		_lowercase   =self.image_processing_class(**self.image_processor_dict       )
		# create random numpy tensors
		_lowercase   =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowerCAmelCase_       ,  numpify=lowerCAmelCase_       )
		for image in image_inputs:
			self.assertIsInstance(lowerCAmelCase_       ,  np.ndarray       )

		# Test not batched input
		_lowercase   =image_processing(image_inputs[0]       ,  return_tensors="pt"       ).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,  (
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["height"],
		        self.image_processor_tester.crop_size["width"],
		    )       ,  )

		# Test batched
		_lowercase   =image_processing(lowerCAmelCase_       ,  return_tensors="pt"       ).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,  (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["height"],
		        self.image_processor_tester.crop_size["width"],
		    )       ,  )


	def __lowerCAmelCase      (   self       ):
		# Initialize image_processing
		_lowercase   =self.image_processing_class(**self.image_processor_dict       )
		# create random PyTorch tensors
		_lowercase   =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowerCAmelCase_       ,  torchify=lowerCAmelCase_       )
		for image in image_inputs:
			self.assertIsInstance(lowerCAmelCase_       ,  torch.Tensor       )

		# Test not batched input
		_lowercase   =image_processing(image_inputs[0]       ,  return_tensors="pt"       ).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,  (
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["height"],
		        self.image_processor_tester.crop_size["width"],
		    )       ,  )

		# Test batched
		_lowercase   =image_processing(lowerCAmelCase_       ,  return_tensors="pt"       ).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,  (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["height"],
		        self.image_processor_tester.crop_size["width"],
		    )       ,  )










def       lowerCAmelCase_      (	lowerCamelCase:							str    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					str(lowerCamelCase    ).strip()
      if not bin_string:
            raise ValueError('Empty string was passed to the function'    )
      lowerCamelCase__   	=					bin_string[0] == '-'
      if is_negative:
            lowerCamelCase__   	=					bin_string[1:]
      if not all(char in '01' for char in bin_string    ):
            raise ValueError('Non-binary value was passed to the function'    )
      lowerCamelCase__   	=					0
      for char in bin_string:
            lowerCamelCase__   	=					2 * decimal_number + int(lowerCamelCase    )
      return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
      from doctest import testmod

      testmod()