


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_video_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import VivitImageProcessor





class  lowerCAmelCase__(    unittest.TestCase						):






				'''simple docstring'''





				def __init__(	self				:   List[Any]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Tuple=7       ,   __snake_case				:   int=3       ,   __snake_case				:   str=10       ,   __snake_case				:   Union[str, Any]=18       ,   __snake_case				:   Dict=30       ,   __snake_case				:   Union[str, Any]=400       ,   __snake_case				:   Optional[Any]=True       ,   __snake_case				:   Dict=None       ,   __snake_case				:   List[str]=True       ,   __snake_case				:   List[Any]=[0.5, 0.5, 0.5]       ,   __snake_case				:   int=[0.5, 0.5, 0.5]       ,   __snake_case				:   List[Any]=None       ,   ):





							'''simple docstring'''
							UpperCAmelCase_							:      Union[str, Any]						=   size if size is not None else {'''shortest_edge''': 18}
							UpperCAmelCase_							:      Any						=   crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

							UpperCAmelCase_							:      Optional[Any]						=   parent
							UpperCAmelCase_							:      Optional[Any]						=   batch_size
							UpperCAmelCase_							:      Optional[int]						=   num_channels
							UpperCAmelCase_							:      List[str]						=   num_frames
							UpperCAmelCase_							:      str						=   image_size
							UpperCAmelCase_							:      Dict						=   min_resolution
							UpperCAmelCase_							:      List[Any]						=   max_resolution
							UpperCAmelCase_							:      Any						=   do_resize
							UpperCAmelCase_							:      int						=   size
							UpperCAmelCase_							:      Tuple						=   do_normalize
							UpperCAmelCase_							:      List[str]						=   image_mean
							UpperCAmelCase_							:      Dict						=   image_std
							UpperCAmelCase_							:      str						=   crop_size





				def 					_lowerCamelCase (	self				:   Tuple	):





							'''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,
							    "crop_size": self.crop_size,
							}





@require_torch
@require_vision
class  lowerCAmelCase__(    snake_case__    ,    unittest.TestCase						):






				'''simple docstring'''





				A_  :     Optional[Any]			= VivitImageProcessor if is_vision_available() else None





				def 					_lowerCamelCase (	self				:   Tuple	):





							'''simple docstring'''
							UpperCAmelCase_							:      int						=   VivitImageProcessingTester(self	)





				@property
				def 					_lowerCamelCase (	self				:   int	):





							'''simple docstring'''
							return self.image_processor_tester.prepare_image_processor_dict()





				def 					_lowerCamelCase (	self				:   List[str]	):





							'''simple docstring'''
							UpperCAmelCase_							:      Optional[Any]						=   self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(__snake_case       ,   '''image_mean'''	)	)
							self.assertTrue(hasattr(__snake_case       ,   '''image_std'''	)	)
							self.assertTrue(hasattr(__snake_case       ,   '''do_normalize'''	)	)
							self.assertTrue(hasattr(__snake_case       ,   '''do_resize'''	)	)
							self.assertTrue(hasattr(__snake_case       ,   '''do_center_crop'''	)	)
							self.assertTrue(hasattr(__snake_case       ,   '''size'''	)	)





				def 					_lowerCamelCase (	self				:   int	):





							'''simple docstring'''
							UpperCAmelCase_							:      str						=   self.image_processing_class.from_dict(self.image_processor_dict	)
							self.assertEqual(image_processor.size       ,   {'''shortest_edge''': 18}	)
							self.assertEqual(image_processor.crop_size       ,   {'''height''': 18, '''width''': 18}	)

							UpperCAmelCase_							:      str						=   self.image_processing_class.from_dict(self.image_processor_dict       ,   size=42       ,   crop_size=84	)
							self.assertEqual(image_processor.size       ,   {'''shortest_edge''': 42}	)
							self.assertEqual(image_processor.crop_size       ,   {'''height''': 84, '''width''': 84}	)





				def 					_lowerCamelCase (	self				:   Dict	):





							'''simple docstring'''
							UpperCAmelCase_							:      List[Any]						=   self.image_processing_class(**self.image_processor_dict	)
							# create random PIL videos
							UpperCAmelCase_							:      Optional[int]						=   prepare_video_inputs(self.image_processor_tester       ,   equal_resolution=__snake_case	)
							for video in video_inputs:
										self.assertIsInstance(__snake_case       ,   __snake_case	)
										self.assertIsInstance(video[0]       ,   Image.Image	)

							# Test not batched input
							UpperCAmelCase_							:      List[Any]						=   image_processing(video_inputs[0]       ,   return_tensors='''pt'''	).pixel_values
							self.assertEqual(
							    encoded_videos.shape       ,   (
							        1,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )       ,   )

							# Test batched
							UpperCAmelCase_							:      int						=   image_processing(__snake_case       ,   return_tensors='''pt'''	).pixel_values
							self.assertEqual(
							    encoded_videos.shape       ,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )       ,   )





				def 					_lowerCamelCase (	self				:   Optional[int]	):





							'''simple docstring'''
							UpperCAmelCase_							:      Any						=   self.image_processing_class(**self.image_processor_dict	)
							# create random numpy tensors
							UpperCAmelCase_							:      Optional[int]						=   prepare_video_inputs(self.image_processor_tester       ,   equal_resolution=__snake_case       ,   numpify=__snake_case	)
							for video in video_inputs:
										self.assertIsInstance(__snake_case       ,   __snake_case	)
										self.assertIsInstance(video[0]       ,   np.ndarray	)

							# Test not batched input
							UpperCAmelCase_							:      str						=   image_processing(video_inputs[0]       ,   return_tensors='''pt'''	).pixel_values
							self.assertEqual(
							    encoded_videos.shape       ,   (
							        1,
							        self.image_processor_tester.num_frames,
							        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(__snake_case       ,   return_tensors='''pt'''	).pixel_values
							self.assertEqual(
							    encoded_videos.shape       ,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )       ,   )





				def 					_lowerCamelCase (	self				:   int	):





							'''simple docstring'''
							UpperCAmelCase_							:      Optional[Any]						=   self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							UpperCAmelCase_							:      List[Any]						=   prepare_video_inputs(self.image_processor_tester       ,   equal_resolution=__snake_case       ,   torchify=__snake_case	)
							for video in video_inputs:
										self.assertIsInstance(__snake_case       ,   __snake_case	)
										self.assertIsInstance(video[0]       ,   torch.Tensor	)

							# Test not batched input
							UpperCAmelCase_							:      Tuple						=   image_processing(video_inputs[0]       ,   return_tensors='''pt'''	).pixel_values
							self.assertEqual(
							    encoded_videos.shape       ,   (
							        1,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )       ,   )

							# Test batched
							UpperCAmelCase_							:      Dict						=   image_processing(__snake_case       ,   return_tensors='''pt'''	).pixel_values
							self.assertEqual(
							    encoded_videos.shape       ,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_frames,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )       ,   )



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





@dataclass
class  lowerCAmelCase__:






    '''simple docstring'''





    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     torch.Tensor  # [batch_size x 3]
    A_  :     int
    A_  :     int
    A_  :     float
    A_  :     float
    A_  :     Tuple[int]





    def 					_lowerCamelCase (	self				:   List[Any]	):





       '''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 					_lowerCamelCase (	self				:   Tuple	):





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





    def 					_lowerCamelCase (	self				:   Tuple	):





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





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   torch.arange(self.height * self.width	)
       UpperCAmelCase_							:      Any						=   torch.stack(
           [
               pixel_indices % self.width,
               torch.div(__snake_case       ,   self.width       ,   rounding_mode='''trunc'''	),
           ]       ,   axis=1       ,   )
       return coords





    @property
    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_		,      *UpperCAmelCase_							:      List[Any]						=   self.shape
       UpperCAmelCase_							:      Optional[Any]						=   int(np.prod(__snake_case	)	)

       UpperCAmelCase_							:      str						=   self.get_image_coords()
       UpperCAmelCase_							:      List[str]						=   torch.broadcast_to(coords.unsqueeze(0	)       ,   [batch_size * inner_batch_size, *coords.shape]	)
       UpperCAmelCase_							:      Tuple						=   self.get_camera_rays(__snake_case	)

       UpperCAmelCase_							:      Union[str, Any]						=   rays.view(__snake_case       ,   inner_batch_size * self.height * self.width       ,   2       ,   3	)

       return rays





    def 					_lowerCamelCase (	self				:   Dict       ,   __snake_case				:   torch.Tensor	):





       '''simple docstring'''
       UpperCAmelCase_		,      *UpperCAmelCase_		,      UpperCAmelCase_							:      int						=   coords.shape
       assert n_coords == 2
       assert batch_size == self.origin.shape[0]

       UpperCAmelCase_							:      str						=   coords.view(__snake_case       ,   -1       ,   2	)

       UpperCAmelCase_							:      List[Any]						=   self.resolution()
       UpperCAmelCase_							:      Optional[Any]						=   self.fov()

       UpperCAmelCase_							:      int						=   (flat.float() / (res - 1)) * 2 - 1
       UpperCAmelCase_							:      Optional[Any]						=   fracs * torch.tan(fov / 2	)

       UpperCAmelCase_							:      Optional[Any]						=   fracs.view(__snake_case       ,   -1       ,   2	)
       UpperCAmelCase_							:      List[Any]						=   (
           self.z.view(__snake_case       ,   1       ,   3	)
           + self.x.view(__snake_case       ,   1       ,   3	) * fracs[:, :, :1]
           + self.y.view(__snake_case       ,   1       ,   3	) * fracs[:, :, 1:]
       )
       UpperCAmelCase_							:      Union[str, Any]						=   directions / directions.norm(dim=-1       ,   keepdim=__snake_case	)
       UpperCAmelCase_							:      Optional[int]						=   torch.stack(
           [
               torch.broadcast_to(self.origin.view(__snake_case       ,   1       ,   3	)       ,   [batch_size, directions.shape[1], 3]	),
               directions,
           ]       ,   dim=2       ,   )
       return rays.view(__snake_case       ,   *__snake_case       ,   2       ,   3	)





    def 					_lowerCamelCase (	self				:   Any       ,   __snake_case				:   int       ,   __snake_case				:   int	):





       '''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=__snake_case       ,   height=__snake_case       ,   x_fov=self.x_fov       ,   y_fov=self.y_fov       ,   )





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