"""simple docstring"""


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    __UpperCAmelCase						(	unittest.TestCase	):

				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	, ):
										lowerCamelCase__					=parent
										lowerCamelCase__					=batch_size
										lowerCamelCase__					=num_channels
										lowerCamelCase__					=image_size
										lowerCamelCase__					=min_resolution
										lowerCamelCase__					=max_resolution
										lowerCamelCase__					=do_resize
										lowerCamelCase__					=size if size is not None else {"height": 18, "width": 18}
										lowerCamelCase__					=do_center_crop
										lowerCamelCase__					=crop_size if crop_size is not None else {"height": 18, "width": 18}
										lowerCamelCase__					=do_normalize
										lowerCamelCase__					=image_mean
										lowerCamelCase__					=image_std
										lowerCamelCase__					=do_convert_rgb






				def 		_a	(      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    __UpperCAmelCase						(	__lowerCAmelCase      ,      unittest.TestCase	):
				A__      :   Tuple  							=       OwlViTImageProcessor if is_vision_available() else None

				def 		_a	(      self      ):
										lowerCamelCase__					=OwlViTImageProcessingTester(self      )

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

				def 		_a	(      self      ):
										lowerCamelCase__					=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 		_a	(      self      ):
										lowerCamelCase__					=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}      )

										lowerCamelCase__					=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 		_a	(      self      ):
										# Initialize image_processing
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										# create random PIL images
										lowerCamelCase__					=prepare_image_inputs(self.image_processor_tester	, equal_resolution=_lowerCamelCase      )

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

										# Test not batched input
										lowerCamelCase__					=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
										lowerCamelCase__					=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 		_a	(      self      ):
										# Initialize image_processing
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										# create random numpy tensors
										lowerCamelCase__					=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
										lowerCamelCase__					=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
										lowerCamelCase__					=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 		_a	(      self      ):
										# Initialize image_processing
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										# create random PyTorch tensors
										lowerCamelCase__					=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
										lowerCamelCase__					=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
										lowerCamelCase__					=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"],
										    )	, )



"""simple docstring"""


def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					str:




						'''simple docstring'''



						lowerCamelCase__					=1
						lowerCamelCase__					=0
						lowerCamelCase__					=1
						lowerCamelCase__					=1
						lowerCamelCase__					=3
						lowerCamelCase__					=3
						lowerCamelCase__					=limit
						lowerCamelCase__					=0

						lowerCamelCase__					=""
						while counter != decimal + 1:
												if 4 * q + r - t < n * t:
																		result += str(__lowerCAmelCase				)
																		if counter == 0:
																								result += "."

																		if decimal == counter:
																								break

																		counter += 1
																		lowerCamelCase__					=10 * (r - n * t)
																		lowerCamelCase__					=((10 * (3 * q + r)) // t) - 10 * n
																		q *= 10
																		lowerCamelCase__					=nr
												else:
																		lowerCamelCase__					=(2 * q + r) * l
																		lowerCamelCase__					=(q * (7 * k) + 2 + (r * l)) // (t * l)
																		q *= k
																		t *= l
																		l += 2
																		k += 1
																		lowerCamelCase__					=nn
																		lowerCamelCase__					=nr
						return result





def 			lowerCamelCase_ (					)							->					None:




						'''simple docstring'''



						print(F'''{calculate_pi(50				) = }'''				)
						import doctest

						doctest.testmod()


if __name__ == "__main__":
	main()


