

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 ViltImageProcessor

class __UpperCamelCase    (							unittest.TestCase		):






					'''simple docstring'''



					def __init__(							self							,			lowerCamelCase__							,			lowerCamelCase__=7							,			lowerCamelCase__=3							,			lowerCamelCase__=1_8							,			lowerCamelCase__=3_0							,			lowerCamelCase__=4_0_0							,			lowerCamelCase__=True							,			lowerCamelCase__=None							,			lowerCamelCase__=2							,			lowerCamelCase__=True							,			lowerCamelCase__=[0.5, 0.5, 0.5]							,			lowerCamelCase__=[0.5, 0.5, 0.5]							,			):
						UpperCAmelCase__:  str		       =   size if size is not None else {"shortest_edge": 3_0}
						UpperCAmelCase__:  Optional[Any]		       =   parent
						UpperCAmelCase__:  List[Any]		       =   batch_size
						UpperCAmelCase__:  str		       =   num_channels
						UpperCAmelCase__:  Optional[Any]		       =   image_size
						UpperCAmelCase__:  List[str]		       =   min_resolution
						UpperCAmelCase__:  List[Any]		       =   max_resolution
						UpperCAmelCase__:  int		       =   do_resize
						UpperCAmelCase__:  Tuple		       =   size
						UpperCAmelCase__:  int		       =   size_divisor
						UpperCAmelCase__:  Optional[Any]		       =   do_normalize
						UpperCAmelCase__:  Optional[int]		       =   image_mean
						UpperCAmelCase__:  Optional[int]		       =   image_std





					def       _UpperCAmelCase						(							self	):
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "size_divisor": self.size_divisor,
						}





					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__=False	):
						if not batched:
							UpperCAmelCase__:  Dict		       =   self.size["shortest_edge"]
							UpperCAmelCase__:  int		       =   image_inputs[0]
							if isinstance(lowerCamelCase__							,			Image.Image	):
								UpperCAmelCase__       ,	UpperCAmelCase__:  List[Any]		       =   image.size
							else:
								UpperCAmelCase__       ,	UpperCAmelCase__:  str		       =   image.shape[1], image.shape[2]
							UpperCAmelCase__:  int		       =   size / min(lowerCamelCase__							,			lowerCamelCase__	)
							if h < w:
								UpperCAmelCase__       ,	UpperCAmelCase__:  List[Any]		       =   size, scale * w
							else:
								UpperCAmelCase__       ,	UpperCAmelCase__:  List[Any]		       =   scale * h, size

							UpperCAmelCase__:  List[str]		       =   int((1_3_3_3 / 8_0_0) * size	)
							if max(lowerCamelCase__							,			lowerCamelCase__	) > max_size:
								UpperCAmelCase__:  str		       =   max_size / max(lowerCamelCase__							,			lowerCamelCase__	)
								UpperCAmelCase__:  Tuple		       =   newh * scale
								UpperCAmelCase__:  Any		       =   neww * scale

							UpperCAmelCase__       ,	UpperCAmelCase__:  List[str]		       =   int(newh + 0.5	), int(neww + 0.5	)
							UpperCAmelCase__       ,	UpperCAmelCase__:  int		       =   (
							    newh // self.size_divisor * self.size_divisor,
							    neww // self.size_divisor * self.size_divisor,
							)

						else:
							UpperCAmelCase__:  Tuple		       =   []
							for image in image_inputs:
								UpperCAmelCase__       ,	UpperCAmelCase__:  Union[str, Any]		       =   self.get_expected_values([image]	)
								expected_values.append((expected_height, expected_width)	)
							UpperCAmelCase__:  Optional[Any]		       =   max(lowerCamelCase__							,			key=lambda lowerCamelCase__	: item[0]	)[0]
							UpperCAmelCase__:  Optional[int]		       =   max(lowerCamelCase__							,			key=lambda lowerCamelCase__	: item[1]	)[1]

						return expected_height, expected_width

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






					'''simple docstring'''



					__magic_name__     			=      ViltImageProcessor if is_vision_available() else None





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  List[str]		       =   ViltImageProcessingTester(self	)





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





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  str		       =   self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(lowerCamelCase__							,			"image_mean"	)	)
						self.assertTrue(hasattr(lowerCamelCase__							,			"image_std"	)	)
						self.assertTrue(hasattr(lowerCamelCase__							,			"do_normalize"	)	)
						self.assertTrue(hasattr(lowerCamelCase__							,			"do_resize"	)	)
						self.assertTrue(hasattr(lowerCamelCase__							,			"size"	)	)
						self.assertTrue(hasattr(lowerCamelCase__							,			"size_divisor"	)	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Union[str, Any]		       =   self.image_processing_class.from_dict(self.image_processor_dict	)
						self.assertEqual(image_processor.size							,			{"shortest_edge": 3_0}	)

						UpperCAmelCase__:  Dict		       =   self.image_processing_class.from_dict(self.image_processor_dict							,			size=4_2	)
						self.assertEqual(image_processor.size							,			{"shortest_edge": 4_2}	)





					def       _UpperCAmelCase						(							self	):
						pass





					def       _UpperCAmelCase						(							self	):
						# Initialize image_processing
						UpperCAmelCase__:  Dict		       =   self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						UpperCAmelCase__:  Any		       =   prepare_image_inputs(self.image_processor_tester							,			equal_resolution=lowerCamelCase__	)
						for image in image_inputs:
							self.assertIsInstance(lowerCamelCase__							,			Image.Image	)

						# Test not batched input
						UpperCAmelCase__:  Any		       =   image_processing(image_inputs[0]							,			return_tensors="pt"	).pixel_values

						UpperCAmelCase__       ,	UpperCAmelCase__:  Union[str, Any]		       =   self.image_processor_tester.get_expected_values(lowerCamelCase__	)
						self.assertEqual(
						    encoded_images.shape							,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,			)

						# Test batched
						UpperCAmelCase__:  List[str]		       =   image_processing(lowerCamelCase__							,			return_tensors="pt"	).pixel_values

						UpperCAmelCase__       ,	UpperCAmelCase__:  List[Any]		       =   self.image_processor_tester.get_expected_values(lowerCamelCase__							,			batched=lowerCamelCase__	)
						self.assertEqual(
						    encoded_images.shape							,			(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,			)





					def       _UpperCAmelCase						(							self	):
						# Initialize image_processing
						UpperCAmelCase__:  Dict		       =   self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						UpperCAmelCase__:  List[str]		       =   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
						UpperCAmelCase__:  List[Any]		       =   image_processing(image_inputs[0]							,			return_tensors="pt"	).pixel_values

						UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   self.image_processor_tester.get_expected_values(lowerCamelCase__	)
						self.assertEqual(
						    encoded_images.shape							,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,			)

						# Test batched
						UpperCAmelCase__:  int		       =   image_processing(lowerCamelCase__							,			return_tensors="pt"	).pixel_values

						UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   self.image_processor_tester.get_expected_values(lowerCamelCase__							,			batched=lowerCamelCase__	)
						self.assertEqual(
						    encoded_images.shape							,			(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,			)





					def       _UpperCAmelCase						(							self	):
						# Initialize image_processing
						UpperCAmelCase__:  Tuple		       =   self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						UpperCAmelCase__:  str		       =   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
						UpperCAmelCase__:  Union[str, Any]		       =   image_processing(image_inputs[0]							,			return_tensors="pt"	).pixel_values

						UpperCAmelCase__       ,	UpperCAmelCase__:  List[Any]		       =   self.image_processor_tester.get_expected_values(lowerCamelCase__	)
						self.assertEqual(
						    encoded_images.shape							,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,			)

						# Test batched
						UpperCAmelCase__:  str		       =   image_processing(lowerCamelCase__							,			return_tensors="pt"	).pixel_values

						UpperCAmelCase__       ,	UpperCAmelCase__:  Dict		       =   self.image_processor_tester.get_expected_values(lowerCamelCase__							,			batched=lowerCamelCase__	)
						self.assertEqual(
						    encoded_images.shape							,			(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,			)


from __future__ import annotations



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	assert n > 1 and a > 0 and greatest_common_divisor(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			) == 1
	((UpperCAmelCase__)       ,	(UpperCAmelCase__)       ,	(UpperCAmelCase__)):  Optional[Any]		       =   extended_gcd(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)  # Implemented below
	UpperCAmelCase__:  List[str]		       =   (b * s) % n
	return x



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	((UpperCAmelCase__)       ,	(UpperCAmelCase__)):  int		       =   extended_euclid(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)  # Implemented below
	if b < 0:
		UpperCAmelCase__:  List[str]		       =   (b % n + n) % n
	return b



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	UpperCAmelCase__:  Union[str, Any]		       =   invert_modulo(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
	UpperCAmelCase__:  Any		       =   (b * s) % n
	return x



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	assert a >= 0 and b >= 0

	if b == 0:
		UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__:  List[Any]		       =   a, 1, 0
	else:
		((UpperCAmelCase__)       ,	(UpperCAmelCase__)       ,	(UpperCAmelCase__)):  Tuple		       =   extended_gcd(SCREAMING_SNAKE_CASE  ,a % b			)
		UpperCAmelCase__:  Union[str, Any]		       =   q
		UpperCAmelCase__:  Union[str, Any]		       =   p - q * (a // b)

	assert a % d == 0 and b % d == 0
	assert d == a * x + b * y

	return (d, x, y)



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	if b == 0:
		return (1, 0)
	((UpperCAmelCase__)       ,	(UpperCAmelCase__)):  List[str]		       =   extended_euclid(SCREAMING_SNAKE_CASE  ,a % b			)
	UpperCAmelCase__:  Optional[Any]		       =   a // b
	return (y, x - k * y)



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	if a < b:
		UpperCAmelCase__       ,	UpperCAmelCase__:  str		       =   b, a

	while a % b != 0:
		UpperCAmelCase__       ,	UpperCAmelCase__:  Union[str, Any]		       =   b, a % b

	return b


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

					testmod(name="""modular_division""", verbose=True)
					testmod(name="""modular_division2""", verbose=True)
					testmod(name="""invert_modulo""", verbose=True)
					testmod(name="""extended_gcd""", verbose=True)
					testmod(name="""extended_euclid""", verbose=True)
					testmod(name="""greatest_common_divisor""", verbose=True)