

import unittest

import numpy as np

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

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 DPTImageProcessor





class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=7					,						_A=3					,						_A=1_8					,						_A=3_0					,						_A=4_0_0					,						_A=True					,						_A=None					,						_A=True					,						_A=[0.5, 0.5, 0.5]					,						_A=[0.5, 0.5, 0.5]					,						):
												__lowerCAmelCase					=							size if size is not None else {'height': 1_8, 'width': 1_8}
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							min_resolution
												__lowerCAmelCase					=							max_resolution
												__lowerCAmelCase					=							do_resize
												__lowerCAmelCase					=							size
												__lowerCAmelCase					=							do_normalize
												__lowerCAmelCase					=							image_mean
												__lowerCAmelCase					=							image_std




							def        A__  ( 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,
												}





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


							"""simple docstring"""





							__UpperCAmelCase       =DPTImageProcessor if is_vision_available() else None




							def        A__  ( self							):
												__lowerCAmelCase					=							DPTImageProcessingTester(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(lowercase_					,						'image_mean'							)							)
												self.assertTrue(hasattr(lowercase_					,						'image_std'							)							)
												self.assertTrue(hasattr(lowercase_					,						'do_normalize'							)							)
												self.assertTrue(hasattr(lowercase_					,						'do_resize'							)							)
												self.assertTrue(hasattr(lowercase_					,						'size'							)							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.image_processing_class.from_dict(self.image_processor_dict							)
												self.assertEqual(image_processor.size					,						{'height': 1_8, 'width': 1_8}							)

												__lowerCAmelCase					=							self.image_processing_class.from_dict(self.image_processor_dict					,						size=4_2							)
												self.assertEqual(image_processor.size					,						{'height': 4_2, 'width': 4_2}							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random PIL images
												__lowerCAmelCase					=							prepare_image_inputs(self.image_processor_tester					,						equal_resolution=lowercase_							)
												for image in image_inputs:
																	self.assertIsInstance(lowercase_					,						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.size['height'],
												        self.image_processor_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processing(lowercase_					,						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.size['height'],
												        self.image_processor_tester.size['width'],
												    )					,						)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random numpy tensors
												__lowerCAmelCase					=							prepare_image_inputs(self.image_processor_tester					,						equal_resolution=lowercase_					,						numpify=lowercase_							)
												for image in image_inputs:
																	self.assertIsInstance(lowercase_					,						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.size['height'],
												        self.image_processor_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processing(lowercase_					,						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.size['height'],
												        self.image_processor_tester.size['width'],
												    )					,						)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random PyTorch tensors
												__lowerCAmelCase					=							prepare_image_inputs(self.image_processor_tester					,						equal_resolution=lowercase_					,						torchify=lowercase_							)
												for image in image_inputs:
																	self.assertIsInstance(lowercase_					,						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.size['height'],
												        self.image_processor_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processing(lowercase_					,						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.size['height'],
												        self.image_processor_tester.size['width'],
												    )					,						)









from __future__ import annotations

from collections.abc import MutableSequence





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A							):
												if len(_A							) != degree + 1:
																	raise ValueError(
																	    'The number of coefficients should be equal to the degree + 1.'							)

												__lowerCAmelCase					=							list(_A							)
												__lowerCAmelCase					=							degree




							def __add__( self					,						_A							):

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




							def __sub__( self					,						_A							):
												return self + polynomial_a * Polynomial(0					,						[-1]							)




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




							def __mul__( self					,						_A							):
												__lowerCAmelCase					=							[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					,						_A							)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							0
												for i in range(self.degree + 1							):
																	result += self.coefficients[i] * (substitution**i)
												return result




							def __str__( self							):
												__lowerCAmelCase					=							''
												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(_A							)

												return polynomial




							def __repr__( self							):
												return self.__str__()




							def        A__  ( self							):
												__lowerCAmelCase					=							[0] * self.degree
												for i in range(self.degree							):
																	__lowerCAmelCase					=							self.coefficients[i + 1] * (i + 1)
												return Polynomial(self.degree - 1					,						_A							)




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




							def __eq__( self					,						_A							):
												if not isinstance(_A					,						_A							):
																	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					,						_A							):
												return not self.__eq__(_A							)






