





'''simple docstring'''

from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch


@dataclass
class 					a_     :

    '''simple docstring'''
    UpperCamelCase     = 42  # [batch_size x 3]
    UpperCamelCase     = 42  # [batch_size x 3]
    UpperCamelCase     = 42  # [batch_size x 3]
    UpperCamelCase     = 42  # [batch_size x 3]
    UpperCamelCase     = 42
    UpperCamelCase     = 42
    UpperCamelCase     = 42
    UpperCamelCase     = 42
    UpperCamelCase     = 42


    def 			snake_case_(		self       )		->   int:
        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       )		->   str:
        return torch.from_numpy(np.array([self.width, self.height] ,		dtype=np.floataa       )       )


    def 			snake_case_(		self       )		->   List[str]:
        return torch.from_numpy(np.array([self.x_fov, self.y_fov] ,		dtype=np.floataa       )       )


    def 			snake_case_(		self       )		->   torch.Tensor:
        _SCREAMING_SNAKE_CASE									=			torch.arange(self.height * self.width       )
        _SCREAMING_SNAKE_CASE									=			torch.stack(
            [
                pixel_indices % self.width,
                torch.div(A ,		self.width ,		rounding_mode="""trunc"""       ),
            ] ,		axis=1 ,		)
        return coords


    @property
    def 			snake_case_(		self       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE				,       *_SCREAMING_SNAKE_CASE									=			self.shape
        _SCREAMING_SNAKE_CASE									=			int(np.prod(A       )       )

        _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(A       )

        _SCREAMING_SNAKE_CASE									=			rays.view(A ,		inner_batch_size * self.height * self.width ,		2 ,		3       )

        return rays


    def 			snake_case_(		self ,		A       )		->   torch.Tensor:
        _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(A ,		-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(A ,		-1 ,		2       )
        _SCREAMING_SNAKE_CASE									=			(
            self.z.view(A ,		1 ,		3       )
            + self.x.view(A ,		1 ,		3       ) * fracs[:, :, :1]
            + self.y.view(A ,		1 ,		3       ) * fracs[:, :, 1:]
        )
        _SCREAMING_SNAKE_CASE									=			directions / directions.norm(dim=-1 ,		keepdim=A       )
        _SCREAMING_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 			snake_case_(		self ,		A ,		A       )		->   "DifferentiableProjectiveCamera":
        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 lowerCamelCase						(     __lowerCamelCase :    int    )						->DifferentiableProjectiveCamera:
    _SCREAMING_SNAKE_CASE									=			[]
    _SCREAMING_SNAKE_CASE									=			[]
    _SCREAMING_SNAKE_CASE									=			[]
    _SCREAMING_SNAKE_CASE									=			[]
    for theta in np.linspace(0					,       2 * np.pi					,       num=20    ):
        _SCREAMING_SNAKE_CASE									=			np.array([np.sin(__lowerCamelCase    ), np.cos(__lowerCamelCase    ), -0.5]    )
        z /= np.sqrt(np.sum(z**2    )    )
        _SCREAMING_SNAKE_CASE									=			-z * 4
        _SCREAMING_SNAKE_CASE									=			np.array([np.cos(__lowerCamelCase    ), -np.sin(__lowerCamelCase    ), 0.0]    )
        _SCREAMING_SNAKE_CASE									=			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    ))					,       )










'''simple docstring'''

import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowercase_					= HUGGINGFACE_HUB_CACHE


lowercase_					= """config.json"""
lowercase_					= """diffusion_pytorch_model.bin"""
lowercase_					= """diffusion_flax_model.msgpack"""
lowercase_					= """model.onnx"""
lowercase_					= """diffusion_pytorch_model.safetensors"""
lowercase_					= """weights.pb"""
lowercase_					= """https://huggingface.co"""
lowercase_					= default_cache_path
lowercase_					= """diffusers_modules"""
lowercase_					= os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
lowercase_					= ["""fp16""", """non-ema"""]
lowercase_					= """.self_attn"""



