from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch


@dataclass
class 						lowercase    :
							lowercase__						:       torch.Tensor  # [batch_size x 3]
							lowercase__						:       torch.Tensor  # [batch_size x 3]
							lowercase__						:       torch.Tensor  # [batch_size x 3]
							lowercase__						:       torch.Tensor  # [batch_size x 3]
							lowercase__						:       int
							lowercase__						:       int
							lowercase__						:       float
							lowercase__						:       float
							lowercase__						:       Tuple[int]




							def 	__snake_case(						self   :					Optional[int]      )					->				Dict:



												'''simple docstring'''

												assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
												assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
												assert len(self.x.shape      ) == len(self.y.shape      ) == len(self.z.shape      ) == len(self.origin.shape      ) == 2




							def 	__snake_case(						self   :					Any      )					->				Tuple:



												'''simple docstring'''

												return torch.from_numpy(np.array([self.width, self.height]	,							dtype=np.floataa      )      )




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



												'''simple docstring'''

												return torch.from_numpy(np.array([self.x_fov, self.y_fov]	,							dtype=np.floataa      )      )




							def 	__snake_case(						self   :					Dict      )					->				torch.Tensor:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       torch.arange(self.height * self.width      )
												SCREAMING_SNAKE_CASE    			=       torch.stack(
												    [
												        pixel_indices % self.width,
												        torch.div(_UpperCamelCase	,							self.width	,							rounding_mode="trunc"      ),
												    ]	,							axis=1	,							)
												return coords




							@property
							def 	__snake_case(						self   :					List[Any]      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   *SCREAMING_SNAKE_CASE    			=       self.shape
												SCREAMING_SNAKE_CASE    			=       int(np.prod(_UpperCamelCase      )      )

												SCREAMING_SNAKE_CASE    			=       self.get_image_coords()
												SCREAMING_SNAKE_CASE    			=       torch.broadcast_to(coords.unsqueeze(0      )	,							[batch_size * inner_batch_size, *coords.shape]      )
												SCREAMING_SNAKE_CASE    			=       self.get_camera_rays(_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       rays.view(_UpperCamelCase	,							inner_batch_size * self.height * self.width	,							2	,							3      )

												return rays




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					torch.Tensor      )					->				torch.Tensor:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   *SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       coords.shape
												assert n_coords == 2
												assert batch_size == self.origin.shape[0]

												SCREAMING_SNAKE_CASE    			=       coords.view(_UpperCamelCase	,							-1	,							2      )

												SCREAMING_SNAKE_CASE    			=       self.resolution()
												SCREAMING_SNAKE_CASE    			=       self.fov()

												SCREAMING_SNAKE_CASE    			=       (flat.float() / (res - 1)) * 2 - 1
												SCREAMING_SNAKE_CASE    			=       fracs * torch.tan(fov / 2      )

												SCREAMING_SNAKE_CASE    			=       fracs.view(_UpperCamelCase	,							-1	,							2      )
												SCREAMING_SNAKE_CASE    			=       (
												    self.z.view(_UpperCamelCase	,							1	,							3      )
												    + self.x.view(_UpperCamelCase	,							1	,							3      ) * fracs[:, :, :1]
												    + self.y.view(_UpperCamelCase	,							1	,							3      ) * fracs[:, :, 1:]
												)
												SCREAMING_SNAKE_CASE    			=       directions / directions.norm(dim=-1	,							keepdim=_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       torch.stack(
												    [
												        torch.broadcast_to(self.origin.view(_UpperCamelCase	,							1	,							3      )	,							[batch_size, directions.shape[1], 3]      ),
												        directions,
												    ]	,							dim=2	,							)
												return rays.view(_UpperCamelCase	,							*_UpperCamelCase	,							2	,							3      )





							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					int      )					->				"DifferentiableProjectiveCamera":



												'''simple docstring'''

												assert width * self.height == height * self.width, "The aspect ratio should not change."
												return DifferentiableProjectiveCamera(
												    origin=self.origin	,							x=self.x	,							y=self.y	,							z=self.z	,							width=_UpperCamelCase	,							height=_UpperCamelCase	,							x_fov=self.x_fov	,							y_fov=self.y_fov	,							)





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					SCREAMING_SNAKE_CASE    			=       []
					SCREAMING_SNAKE_CASE    			=       []
					SCREAMING_SNAKE_CASE    			=       []
					SCREAMING_SNAKE_CASE    			=       []
					for theta in np.linspace(0      , 2 * np.pi      , num=2_0  ):
										SCREAMING_SNAKE_CASE    			=       np.array([np.sin(UpperCAmelCase__  ), np.cos(UpperCAmelCase__  ), -0.5]  )
										z /= np.sqrt(np.sum(z**2  )  )
										SCREAMING_SNAKE_CASE    			=       -z * 4
										SCREAMING_SNAKE_CASE    			=       np.array([np.cos(UpperCAmelCase__  ), -np.sin(UpperCAmelCase__  ), 0.0]  )
										SCREAMING_SNAKE_CASE    			=       np.cross(UpperCAmelCase__      , UpperCAmelCase__  )
										origins.append(UpperCAmelCase__  )
										xs.append(UpperCAmelCase__  )
										ys.append(UpperCAmelCase__  )
										zs.append(UpperCAmelCase__  )
					return DifferentiableProjectiveCamera(
					    origin=torch.from_numpy(np.stack(UpperCAmelCase__      , axis=0  )  ).float()      , x=torch.from_numpy(np.stack(UpperCAmelCase__      , axis=0  )  ).float()      , y=torch.from_numpy(np.stack(UpperCAmelCase__      , axis=0  )  ).float()      , z=torch.from_numpy(np.stack(UpperCAmelCase__      , axis=0  )  ).float()      , width=UpperCAmelCase__      , height=UpperCAmelCase__      , x_fov=0.7      , y_fov=0.7      , shape=(1, len(UpperCAmelCase__  ))      , )





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 						lowercase    (     unittest.TestCase     ):




							def __init__(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					List[Any]=7	,							_UpperCamelCase   :					Any=3	,							_UpperCamelCase   :					str=18	,							_UpperCamelCase   :					Tuple=30	,							_UpperCamelCase   :					Optional[int]=400	,							_UpperCamelCase   :					Optional[int]=True	,							_UpperCamelCase   :					Optional[Any]=None	,							_UpperCamelCase   :					int=True	,							_UpperCamelCase   :					Optional[int]=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					List[str]=[0.5, 0.5, 0.5]	,							)					->				str:



												'''simple docstring'''

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





							def 	__snake_case(						self   :					List[Any]      )					->				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,
												}



@require_torch
@require_vision
class 						lowercase    (     a				,       unittest.TestCase     ):
							lowercase__						:       Any								=							DPTImageProcessor if is_vision_available() else None




							def 	__snake_case(						self   :					List[str]      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       DPTImageProcessingTester(self      )




							@property
							def 	__snake_case(						self   :					List[Any]      )					->				List[str]:



												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def 	__snake_case(						self   :					str      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"image_mean"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"image_std"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_normalize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_resize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"size"      )      )




							def 	__snake_case(						self   :					Dict      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class.from_dict(self.image_processor_dict      )
												self.assertEqual(image_processor.size	,							{"height": 18, "width": 18}      )

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class.from_dict(self.image_processor_dict	,							size=42      )
												self.assertEqual(image_processor.size	,							{"height": 42, "width": 42}      )




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



												'''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=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							Image.Image      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							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 	__snake_case(						self   :					Optional[Any]      )					->				Optional[Any]:



												'''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=_UpperCamelCase	,							numpify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							np.ndarray      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							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 	__snake_case(						self   :					Optional[Any]      )					->				str:



												'''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=_UpperCamelCase	,							torchify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							torch.Tensor      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							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"],
												    )	,							)




