




import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor



class       a_       (     unittest.TestCase     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = True      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = 32      ,     SCREAMING_SNAKE_CASE = True      ,     SCREAMING_SNAKE_CASE = 1 / 255      ,     SCREAMING_SNAKE_CASE = True      ,     SCREAMING_SNAKE_CASE = True      ,     SCREAMING_SNAKE_CASE = [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]      ,     SCREAMING_SNAKE_CASE = [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]      ,     SCREAMING_SNAKE_CASE = True      ,     SCREAMING_SNAKE_CASE=7      ,     SCREAMING_SNAKE_CASE=30      ,     SCREAMING_SNAKE_CASE=400      ,     SCREAMING_SNAKE_CASE=3      ,     )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						parent
				SCREAMING_SNAKE_CASE_								=						do_resize
				SCREAMING_SNAKE_CASE_								=						size if size is not None else {'shortest_edge': 288}
				SCREAMING_SNAKE_CASE_								=						size_divisor
				SCREAMING_SNAKE_CASE_								=						do_rescale
				SCREAMING_SNAKE_CASE_								=						rescale_factor
				SCREAMING_SNAKE_CASE_								=						do_normalize
				SCREAMING_SNAKE_CASE_								=						do_center_crop
				SCREAMING_SNAKE_CASE_								=						image_mean
				SCREAMING_SNAKE_CASE_								=						image_std
				SCREAMING_SNAKE_CASE_								=						do_pad
				SCREAMING_SNAKE_CASE_								=						batch_size
				SCREAMING_SNAKE_CASE_								=						num_channels
				SCREAMING_SNAKE_CASE_								=						min_resolution
				SCREAMING_SNAKE_CASE_								=						max_resolution





	def 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



				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 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=False      )  ->					List[str]:




				"""simple docstring"""



				if not batched:
							SCREAMING_SNAKE_CASE_								=						self.size['shortest_edge']
							SCREAMING_SNAKE_CASE_								=						image_inputs[0]
							if isinstance(SCREAMING_SNAKE_CASE      ,     Image.Image      ):
										SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						image.size
							else:
										SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						image.shape[1], image.shape[2]
							SCREAMING_SNAKE_CASE_								=						size / min(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
							if h < w:
										SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						size, scale * w
							else:
										SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						scale * h, size

							SCREAMING_SNAKE_CASE_								=						int((1333 / 800) * size      )
							if max(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ) > max_size:
										SCREAMING_SNAKE_CASE_								=						max_size / max(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
										SCREAMING_SNAKE_CASE_								=						newh * scale
										SCREAMING_SNAKE_CASE_								=						neww * scale

							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						int(newh + 0.5      ), int(neww + 0.5      )
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						(
							    newh // self.size_divisor * self.size_divisor,
							    neww // self.size_divisor * self.size_divisor,
							)

				else:
							SCREAMING_SNAKE_CASE_								=						[]
							for image in image_inputs:
										SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.get_expected_values([image]      )
										expected_values.append((expected_height, expected_width)      )
							SCREAMING_SNAKE_CASE_								=						max(SCREAMING_SNAKE_CASE      ,     key=lambda SCREAMING_SNAKE_CASE      : item[0]      )[0]
							SCREAMING_SNAKE_CASE_								=						max(SCREAMING_SNAKE_CASE      ,     key=lambda SCREAMING_SNAKE_CASE      : item[1]      )[1]

				return expected_height, expected_width




@require_torch
@require_vision
class       a_       (     SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= BridgeTowerImageProcessor if is_vision_available() else None





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						BridgeTowerImageProcessingTester(self      )





	@property
	def 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



				return self.image_processor_tester.prepare_image_processor_dict()





	def 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.image_processing_class(**self.image_processor_dict      )
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,     'image_mean'      )      )
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,     'image_std'      )      )
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,     'do_normalize'      )      )
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,     'do_resize'      )      )
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,     'size'      )      )
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,     'size_divisor'      )      )





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				pass





	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.image_processing_class(**self.image_processor_dict      )
				# create random PIL images
				SCREAMING_SNAKE_CASE_								=						prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=SCREAMING_SNAKE_CASE      )
				for image in image_inputs:
							self.assertIsInstance(SCREAMING_SNAKE_CASE      ,     Image.Image      )

				# Test not batched input
				SCREAMING_SNAKE_CASE_								=						image_processing(image_inputs[0]      ,     return_tensors='pt'      ).pixel_values

				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE      )
				self.assertEqual(
				    encoded_images.shape      ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,     )

				# Test batched
				SCREAMING_SNAKE_CASE_								=						image_processing(SCREAMING_SNAKE_CASE      ,     return_tensors='pt'      ).pixel_values

				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE      ,     batched=SCREAMING_SNAKE_CASE      )
				self.assertEqual(
				    encoded_images.shape      ,     (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )      ,     )





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.image_processing_class(**self.image_processor_dict      )
				# create random numpy tensors
				SCREAMING_SNAKE_CASE_								=						prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=SCREAMING_SNAKE_CASE      ,     numpify=SCREAMING_SNAKE_CASE      )
				for image in image_inputs:
							self.assertIsInstance(SCREAMING_SNAKE_CASE      ,     np.ndarray      )

				# Test not batched input
				SCREAMING_SNAKE_CASE_								=						image_processing(image_inputs[0]      ,     return_tensors='pt'      ).pixel_values

				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE      )
				self.assertEqual(
				    encoded_images.shape      ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,     )

				# Test batched
				SCREAMING_SNAKE_CASE_								=						image_processing(SCREAMING_SNAKE_CASE      ,     return_tensors='pt'      ).pixel_values

				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE      ,     batched=SCREAMING_SNAKE_CASE      )
				self.assertEqual(
				    encoded_images.shape      ,     (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )      ,     )





	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.image_processing_class(**self.image_processor_dict      )
				# create random PyTorch tensors
				SCREAMING_SNAKE_CASE_								=						prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=SCREAMING_SNAKE_CASE      ,     torchify=SCREAMING_SNAKE_CASE      )
				for image in image_inputs:
							self.assertIsInstance(SCREAMING_SNAKE_CASE      ,     torch.Tensor      )

				# Test not batched input
				SCREAMING_SNAKE_CASE_								=						image_processing(image_inputs[0]      ,     return_tensors='pt'      ).pixel_values

				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE      )
				self.assertEqual(
				    encoded_images.shape      ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,     )

				# Test batched
				SCREAMING_SNAKE_CASE_								=						image_processing(SCREAMING_SNAKE_CASE      ,     return_tensors='pt'      ).pixel_values

				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE      ,     batched=SCREAMING_SNAKE_CASE      )
				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

from collections.abc import MutableSequence



class       a_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				if len(SCREAMING_SNAKE_CASE      ) != degree + 1:
							raise ValueError(
							    'The number of coefficients should be equal to the degree + 1.'      )

				SCREAMING_SNAKE_CASE_								=						list(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						degree





	def __add__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				if self.degree > polynomial_a.degree:
							SCREAMING_SNAKE_CASE_								=						self.coefficients[:]
							for i in range(polynomial_a.degree + 1      ):
										coefficients[i] += polynomial_a.coefficients[i]
							return Polynomial(self.degree      ,     SCREAMING_SNAKE_CASE      )
				else:
							SCREAMING_SNAKE_CASE_								=						polynomial_a.coefficients[:]
							for i in range(self.degree + 1      ):
										coefficients[i] += self.coefficients[i]
							return Polynomial(polynomial_a.degree      ,     SCREAMING_SNAKE_CASE      )





	def __sub__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				return self + polynomial_a * Polynomial(0      ,     [-1]      )





	def __neg__(							self      )  ->					Polynomial:




				"""simple docstring"""



				return Polynomial(self.degree      ,     [-c for c in self.coefficients]      )





	def __mul__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * (self.degree + polynomial_a.degree + 1)
				for i in range(self.degree + 1      ):
							for j in range(polynomial_a.degree + 1      ):
										coefficients[i + j] += (
										    self.coefficients[i] * polynomial_a.coefficients[j]
										)

				return Polynomial(self.degree + polynomial_a.degree      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					int | float:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				for i in range(self.degree + 1      ):
							result += self.coefficients[i] * (substitution**i)
				return result





	def __str__(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						''
				for i in range(self.degree      ,     -1      ,     -1      ):
							if self.coefficients[i] == 0:
										continue
							elif self.coefficients[i] > 0:
										if polynomial:
													polynomial += " + "
							else:
										polynomial += " - "

							if i == 0:
										polynomial += str(abs(self.coefficients[i]      )      )
							elif i == 1:
										polynomial += str(abs(self.coefficients[i]      )      ) + "x"
							else:
										polynomial += str(abs(self.coefficients[i]      )      ) + "x^" + str(SCREAMING_SNAKE_CASE      )

				return polynomial





	def __repr__(							self      )  ->					str:




				"""simple docstring"""



				return self.__str__()





	def 						A_(							self      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * self.degree
				for i in range(self.degree      ):
							SCREAMING_SNAKE_CASE_								=						self.coefficients[i + 1] * (i + 1)
				return Polynomial(self.degree - 1      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE = 0      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * (self.degree + 2)
				SCREAMING_SNAKE_CASE_								=						constant
				for i in range(self.degree + 1      ):
							SCREAMING_SNAKE_CASE_								=						self.coefficients[i] / (i + 1)
				return Polynomial(self.degree + 1      ,     SCREAMING_SNAKE_CASE      )





	def __eq__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ):
							return False

				if self.degree != polynomial_a.degree:
							return False

				for i in range(self.degree + 1      ):
							if self.coefficients[i] != polynomial_a.coefficients[i]:
										return False

				return True





	def __ne__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				return not self.__eq__(SCREAMING_SNAKE_CASE      )
