from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase_								=							logging.get_logger(__name__)


if is_vision_available():
      import PIL





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     str		=["""pixel_values"""]



     def __init__(					self				,      lowerCAmelCase__ = True				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = PILImageResampling.BICUBIC				,      lowerCAmelCase__ = True				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = True				,      lowerCAmelCase__ = 1 / 255				,      lowerCAmelCase__ = True				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = True				,      **lowerCAmelCase__				,      ):
            super().__init__(**lowerCAmelCase__       )
            _A=	size if size is not None else {'shortest_edge': 224}
            _A=	get_size_dict(lowerCAmelCase__				,      default_to_square=lowerCAmelCase__       )
            _A=	crop_size if crop_size is not None else {'height': 224, 'width': 224}
            _A=	get_size_dict(lowerCAmelCase__				,      default_to_square=lowerCAmelCase__				,      param_name='crop_size'       )

            _A=	do_resize
            _A=	size
            _A=	resample
            _A=	do_center_crop
            _A=	crop_size
            _A=	do_rescale
            _A=	rescale_factor
            _A=	do_normalize
            _A=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
            _A=	image_std if image_std is not None else OPENAI_CLIP_STD
            _A=	do_convert_rgb



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__ = PILImageResampling.BICUBIC				,      lowerCAmelCase__ = None				,      **lowerCAmelCase__				,      ):
            _A=	get_size_dict(lowerCAmelCase__				,      default_to_square=lowerCAmelCase__       )
            if "shortest_edge" not in size:
                   raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"       )
            _A=	get_resize_output_image_size(lowerCAmelCase__				,      size=size['shortest_edge']				,      default_to_square=lowerCAmelCase__       )
            return resize(lowerCAmelCase__				,      size=lowerCAmelCase__				,      resample=lowerCAmelCase__				,      data_format=lowerCAmelCase__				,      **lowerCAmelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__ = None				,      **lowerCAmelCase__				,      ):
            _A=	get_size_dict(lowerCAmelCase__       )
            if "height" not in size or "width" not in size:
                   raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}"       )
            return center_crop(lowerCAmelCase__				,      size=(size['height'], size['width'])				,      data_format=lowerCAmelCase__				,      **lowerCAmelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__ = None				,      **lowerCAmelCase__				,      ):
            return rescale(lowerCAmelCase__				,      scale=lowerCAmelCase__				,      data_format=lowerCAmelCase__				,      **lowerCAmelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__ = None				,      **lowerCAmelCase__				,      ):
            return normalize(lowerCAmelCase__				,      mean=lowerCAmelCase__				,      std=lowerCAmelCase__				,      data_format=lowerCAmelCase__				,      **lowerCAmelCase__       )





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = ChannelDimension.FIRST				,      **lowerCAmelCase__				,      ):
            _A=	do_resize if do_resize is not None else self.do_resize
            _A=	size if size is not None else self.size
            _A=	get_size_dict(lowerCAmelCase__				,      param_name='size'				,      default_to_square=lowerCAmelCase__       )
            _A=	resample if resample is not None else self.resample
            _A=	do_center_crop if do_center_crop is not None else self.do_center_crop
            _A=	crop_size if crop_size is not None else self.crop_size
            _A=	get_size_dict(lowerCAmelCase__				,      param_name='crop_size'				,      default_to_square=lowerCAmelCase__       )
            _A=	do_rescale if do_rescale is not None else self.do_rescale
            _A=	rescale_factor if rescale_factor is not None else self.rescale_factor
            _A=	do_normalize if do_normalize is not None else self.do_normalize
            _A=	image_mean if image_mean is not None else self.image_mean
            _A=	image_std if image_std is not None else self.image_std
            _A=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

            _A=	make_list_of_images(lowerCAmelCase__       )

            if not valid_images(lowerCAmelCase__       ):
                   raise ValueError(
                       'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                       'torch.Tensor, tf.Tensor or jax.ndarray.'       )

            if do_resize and size is None:
                   raise ValueError('Size must be specified if do_resize is True.'       )

            if do_center_crop and crop_size is None:
                   raise ValueError('Crop size must be specified if do_center_crop is True.'       )

            if do_rescale and rescale_factor is None:
                   raise ValueError('Rescale factor must be specified if do_rescale is True.'       )

            if do_normalize and (image_mean is None or image_std is None):
                   raise ValueError('Image mean and std must be specified if do_normalize is True.'       )

            # PIL RGBA images are converted to RGB
            if do_convert_rgb:
                   _A=	[convert_to_rgb(lowerCAmelCase__       ) for image in images]

            # All transformations expect numpy arrays.
            _A=	[to_numpy_array(lowerCAmelCase__       ) for image in images]

            if do_resize:
                   _A=	[self.resize(image=lowerCAmelCase__				,      size=lowerCAmelCase__				,      resample=lowerCAmelCase__       ) for image in images]

            if do_center_crop:
                   _A=	[self.center_crop(image=lowerCAmelCase__				,      size=lowerCAmelCase__       ) for image in images]

            if do_rescale:
                   _A=	[self.rescale(image=lowerCAmelCase__				,      scale=lowerCAmelCase__       ) for image in images]

            if do_normalize:
                   _A=	[self.normalize(image=lowerCAmelCase__				,      mean=lowerCAmelCase__				,      std=lowerCAmelCase__       ) for image in images]

            _A=	[to_channel_dimension_format(lowerCAmelCase__				,      lowerCAmelCase__       ) for image in images]

            _A=	{'pixel_values': images}
            return BatchFeature(data=lowerCAmelCase__				,      tensor_type=lowerCAmelCase__       )
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





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



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



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



     def 			a__  (					self       ):
            _A=	torch.arange(self.height * self.width       )
            _A=	torch.stack(
                [
                    pixel_indices % self.width,
                    torch.div(lowerCAmelCase__				,      self.width				,      rounding_mode='trunc'       ),
                ]				,      axis=1				,      )
            return coords



     @property
     def 			a__  (					self       ):
            _A,			*_A=	self.shape
            _A=	int(np.prod(lowerCAmelCase__       )       )

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

            _A=	rays.view(lowerCAmelCase__				,      inner_batch_size * self.height * self.width				,      2				,      3       )

            return rays



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A,			*_A,			_A=	coords.shape
            assert n_coords == 2
            assert batch_size == self.origin.shape[0]

            _A=	coords.view(lowerCAmelCase__				,      -1				,      2       )

            _A=	self.resolution()
            _A=	self.fov()

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

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





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            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=lowerCAmelCase__				,      height=lowerCAmelCase__				,      x_fov=self.x_fov				,      y_fov=self.y_fov				,      )


def 							UpperCamelCase				(    lowerCAmelCase_   )		->						DifferentiableProjectiveCamera:




       '''simple docstring'''






       _A=	[]
       _A=	[]
       _A=	[]
       _A=	[]
       for theta in np.linspace(0       ,					2 * np.pi       ,					num=20   ):
              _A=	np.array([np.sin(lowerCAmelCase_   ), np.cos(lowerCAmelCase_   ), -0.5]   )
              z /= np.sqrt(np.sum(z**2   )   )
              _A=	-z * 4
              _A=	np.array([np.cos(lowerCAmelCase_   ), -np.sin(lowerCAmelCase_   ), 0.0]   )
              _A=	np.cross(lowerCAmelCase_       ,					lowerCAmelCase_   )
              origins.append(lowerCAmelCase_   )
              xs.append(lowerCAmelCase_   )
              ys.append(lowerCAmelCase_   )
              zs.append(lowerCAmelCase_   )
       return DifferentiableProjectiveCamera(
           origin=torch.from_numpy(np.stack(lowerCAmelCase_       ,					axis=0   )   ).float()       ,					x=torch.from_numpy(np.stack(lowerCAmelCase_       ,					axis=0   )   ).float()       ,					y=torch.from_numpy(np.stack(lowerCAmelCase_       ,					axis=0   )   ).float()       ,					z=torch.from_numpy(np.stack(lowerCAmelCase_       ,					axis=0   )   ).float()       ,					width=lowerCAmelCase_       ,					height=lowerCAmelCase_       ,					x_fov=0.7       ,					y_fov=0.7       ,					shape=(1, len(lowerCAmelCase_   ))       ,					)