
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 MobileViTImageProcessor




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def __init__(					self		:Tuple     ,   a		:str     ,   a		:Tuple=7     ,   a		:Optional[int]=3     ,   a		:Dict=1_8     ,   a		:List[Any]=3_0     ,   a		:Tuple=4_0_0     ,   a		:int=True     ,   a		:List[Any]=None     ,   a		:Optional[Any]=True     ,   a		:Union[str, Any]=None     ,   a		:Any=True     ,   )		-> List[Any]:
								__UpperCamelCase	:   str			   =					size if size is not None else {"shortest_edge": 2_0}
								__UpperCamelCase	:   Optional[Any]			   =					crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
								__UpperCamelCase	:   Tuple			   =					parent
								__UpperCamelCase	:   Union[str, Any]			   =					batch_size
								__UpperCamelCase	:   Union[str, Any]			   =					num_channels
								__UpperCamelCase	:   Tuple			   =					image_size
								__UpperCamelCase	:   List[str]			   =					min_resolution
								__UpperCamelCase	:   Union[str, Any]			   =					max_resolution
								__UpperCamelCase	:   int			   =					do_resize
								__UpperCamelCase	:   Optional[int]			   =					size
								__UpperCamelCase	:   int			   =					do_center_crop
								__UpperCamelCase	:   int			   =					crop_size
								__UpperCamelCase	:   List[Any]			   =					do_flip_channel_order







	def    _lowerCamelCase				(					self		:List[Any]						)		-> Optional[Any]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_flip_channel_order": self.do_flip_channel_order,
								}




@require_torch
@require_vision
class  lowerCamelCase__							(				__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     MobileViTImageProcessor if is_vision_available() else None







	def    _lowerCamelCase				(					self		:int						)		-> List[str]:
								__UpperCamelCase	:   List[Any]			   =					MobileViTImageProcessingTester(self						)







	@property
	def    _lowerCamelCase				(					self		:Dict						)		-> int:
								return self.image_processor_tester.prepare_image_processor_dict()







	def    _lowerCamelCase				(					self		:Tuple						)		-> Union[str, Any]:
								__UpperCamelCase	:   int			   =					self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(a     ,   "do_resize"						)						)
								self.assertTrue(hasattr(a     ,   "size"						)						)
								self.assertTrue(hasattr(a     ,   "do_center_crop"						)						)
								self.assertTrue(hasattr(a     ,   "center_crop"						)						)
								self.assertTrue(hasattr(a     ,   "do_flip_channel_order"						)						)







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Union[str, Any]:
								__UpperCamelCase	:   List[Any]			   =					self.image_processing_class.from_dict(self.image_processor_dict						)
								self.assertEqual(image_processor.size     ,   {"shortest_edge": 2_0}						)
								self.assertEqual(image_processor.crop_size     ,   {"height": 1_8, "width": 1_8}						)

								__UpperCamelCase	:   List[str]			   =					self.image_processing_class.from_dict(self.image_processor_dict     ,   size=4_2     ,   crop_size=8_4						)
								self.assertEqual(image_processor.size     ,   {"shortest_edge": 4_2}						)
								self.assertEqual(image_processor.crop_size     ,   {"height": 8_4, "width": 8_4}						)







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







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> str:
								# Initialize image_processing
								__UpperCamelCase	:   List[Any]			   =					self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								__UpperCamelCase	:   List[str]			   =					prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=a						)
								for image in image_inputs:
															self.assertIsInstance(a     ,   Image.Image						)

								# Test not batched input
								__UpperCamelCase	:   Tuple			   =					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
								__UpperCamelCase	:   Tuple			   =					image_processing(a     ,   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		:Union[str, Any]						)		-> Dict:
								# Initialize image_processing
								__UpperCamelCase	:   Union[str, Any]			   =					self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								__UpperCamelCase	:   Tuple			   =					prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=a     ,   numpify=a						)
								for image in image_inputs:
															self.assertIsInstance(a     ,   np.ndarray						)

								# Test not batched input
								__UpperCamelCase	:   Dict			   =					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
								__UpperCamelCase	:   Optional[Any]			   =					image_processing(a     ,   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		:Optional[Any]						)		-> int:
								# Initialize image_processing
								__UpperCamelCase	:   Optional[int]			   =					self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								__UpperCamelCase	:   Optional[int]			   =					prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=a     ,   torchify=a						)
								for image in image_inputs:
															self.assertIsInstance(a     ,   torch.Tensor						)

								# Test not batched input
								__UpperCamelCase	:   Optional[int]			   =					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
								__UpperCamelCase	:   Optional[int]			   =					image_processing(a     ,   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"],
								    )     ,   )

from __future__ import annotations

import math

lowercase  :					Any													=       '2020.9.26'
lowercase  :					Union[str, Any]													=       'xcodz-dot, cclaus, dhruvmanila'




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			float						,  _lowerCamelCase :			float						,  _lowerCamelCase :			float						,  _lowerCamelCase :			float						,  _lowerCamelCase :			float)					->    tuple[float, float]:






							'''simple docstring'''





							if not all(isinstance(_lowerCamelCase						,  (float, int)) for val in locals().values()):
														__UpperCamelCase	:   str			   =					F'Input values must either be float or int: {list(locals().values())}'
														raise TypeError(_lowerCamelCase)
							__UpperCamelCase	:   List[str]			   =					((x * distance) / (z + distance)) * scale
							__UpperCamelCase	:   List[Any]			   =					((y * distance) / (z + distance)) * scale
							return projected_x, projected_y




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			float						,  _lowerCamelCase :			float						,  _lowerCamelCase :			float						,  _lowerCamelCase :			str						,  _lowerCamelCase :			float)					->    tuple[float, float, float]:






							'''simple docstring'''





							if not isinstance(_lowerCamelCase						,  _lowerCamelCase):
														raise TypeError("Axis must be a str")
							__UpperCamelCase	:   str			   =					locals()
							del input_variables["axis"]
							if not all(isinstance(_lowerCamelCase						,  (float, int)) for val in input_variables.values()):
														__UpperCamelCase	:   Dict			   =					(
														    "Input values except axis must either be float or int: "
														    F'{list(input_variables.values())}'
														)
														raise TypeError(_lowerCamelCase)
							__UpperCamelCase	:   Optional[Any]			   =					(angle % 360) / 450 * 180 / math.pi
							if axis == "z":
														__UpperCamelCase	:   Tuple			   =					x * math.cos(_lowerCamelCase) - y * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   Union[str, Any]			   =					y * math.cos(_lowerCamelCase) + x * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   Any			   =					z
							elif axis == "x":
														__UpperCamelCase	:   Dict			   =					y * math.cos(_lowerCamelCase) - z * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   Any			   =					z * math.cos(_lowerCamelCase) + y * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   List[str]			   =					x
							elif axis == "y":
														__UpperCamelCase	:   Any			   =					x * math.cos(_lowerCamelCase) - z * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   Any			   =					z * math.cos(_lowerCamelCase) + x * math.sin(_lowerCamelCase)
														__UpperCamelCase	:   Dict			   =					y
							else:
														raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'")

							return new_x, new_y, new_z


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f"{convert_to_ad(1.0, 2.0, 3.0, 1_0.0, 1_0.0) = }")
		print(f"{rotate(1.0, 2.0, 3.0, 'y', 9_0.0) = }")