




'''simple docstring'''





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




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




			def    lowerCAmelCase__				(					self							)				->				int:

										'''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							)				->				Any:

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self							)				->				torch.Tensor:

										'''simple docstring'''




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




			@property
			def    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




										snake_case_					,      *snake_case_						=      self.shape
										snake_case_						=      int(np.prod(a__							)							)

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

										snake_case_						=      rays.view(a__				,							inner_batch_size * self.height * self.width				,							2				,							3							)

										return rays




			def    lowerCAmelCase__				(					self				,							a__							)				->				torch.Tensor:

										'''simple docstring'''




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

										snake_case_						=      coords.view(a__				,							-1				,							2							)

										snake_case_						=      self.resolution()
										snake_case_						=      self.fov()

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

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




			def    lowerCAmelCase__				(					self				,							a__				,							a__							)				->				"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=a__				,							height=a__				,							x_fov=self.x_fov				,							y_fov=self.y_fov				,							)


def      UpperCamelCase_(	snake_case							:       int     ):

							'''simple docstring'''






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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ResNetForImageClassification",
					    "ResNetModel",
					    "ResNetPreTrainedModel",
					    "ResNetBackbone",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			List[Any]												=    [
					    "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFResNetForImageClassification",
					    "TFResNetModel",
					    "TFResNetPreTrainedModel",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    [
					    "FlaxResNetForImageClassification",
					    "FlaxResNetModel",
					    "FlaxResNetPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_resnet import (
										    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ResNetBackbone,
										    ResNetForImageClassification,
										    ResNetModel,
										    ResNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_resnet import (
										    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFResNetForImageClassification,
										    TFResNetModel,
										    TFResNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Any												=    _LazyModule(__name__, globals()["__file__"], _import_structure)



