import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


UpperCAmelCase_								=							'''__DUMMY_TRANSFORMERS_USER__'''
UpperCAmelCase_								=							'''Dummy User'''
UpperCAmelCase_								=							'''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'''

UpperCAmelCase_								=							'''https://hub-ci.huggingface.co'''
UpperCAmelCase_								=							CI_HUB_ENDPOINT + '''/datasets/{repo_id}/resolve/{revision}/{path}'''
UpperCAmelCase_								=							CI_HUB_ENDPOINT + '''/{repo_id}/resolve/{revision}/{filename}'''
UpperCAmelCase_								=							Path('''~/.huggingface/hub_ci_token''').expanduser()

@pytest.fixture
def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Any:




       '''simple docstring'''






       monkeypatch.setattr(
           'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE'       ,					_UpperCAmelCase   )

@pytest.fixture
def 							UpperCamelCase				(    lowerCAmelCase_   )		->						List[str]:




       '''simple docstring'''






       monkeypatch.setattr('datasets.config.HF_ENDPOINT'       ,					_UpperCAmelCase   )
       monkeypatch.setattr('datasets.config.HUB_DATASETS_URL'       ,					_UpperCAmelCase   )

@pytest.fixture
def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Tuple:




       '''simple docstring'''






       monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token'       ,					_UpperCAmelCase   )

@pytest.fixture
def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       HfFolder.save_token(_UpperCAmelCase   )
       yield
       HfFolder.delete_token()

@pytest.fixture(scope='session'   )
def 							UpperCamelCase				(    )		->						int:




       '''simple docstring'''






       return HfApi(endpoint=_UpperCAmelCase   )

@pytest.fixture(scope='session'   )
def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Optional[int]:




       '''simple docstring'''






       _A=	HfFolder.get_token()
       HfFolder.save_token(_UpperCAmelCase   )
       yield CI_HUB_USER_TOKEN
       if previous_token is not None:
              HfFolder.save_token(_UpperCAmelCase   )

@pytest.fixture
def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Optional[Any]:




       '''simple docstring'''






       def _cleanup_repo(lowerCAmelCase_   ):
              hf_api.delete_repo(_UpperCAmelCase       ,					token=_UpperCAmelCase       ,					repo_type='dataset'   )

       return _cleanup_repo

@pytest.fixture
def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Optional[Any]:




       '''simple docstring'''






       @contextmanager
       def _temporary_repo(lowerCAmelCase_   ):
              try:
                     yield repo_id
              finally:
                     cleanup_repo(_UpperCAmelCase   )

       return _temporary_repo

@pytest.fixture(scope='session'   )
def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Optional[Any]:




       '''simple docstring'''






       _A=	F"repo_txt_data-{int(time.time() * 10E3   )}"
       _A=	F"{CI_HUB_USER}/{repo_name}"
       hf_api.create_repo(_UpperCAmelCase       ,					token=_UpperCAmelCase       ,					repo_type='dataset'       ,					private=_UpperCAmelCase   )
       hf_api.upload_file(
           token=_UpperCAmelCase       ,					path_or_fileobj=str(_UpperCAmelCase   )       ,					path_in_repo='data/text_data.txt'       ,					repo_id=_UpperCAmelCase       ,					repo_type='dataset'       ,					)
       yield repo_id
       try:
              hf_api.delete_repo(_UpperCAmelCase       ,					token=_UpperCAmelCase       ,					repo_type='dataset'   )
       except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
              pass

@pytest.fixture()
def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       return hf_private_dataset_repo_txt_data_

@pytest.fixture(scope='session'   )
def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						str:




       '''simple docstring'''






       _A=	F"repo_zipped_txt_data-{int(time.time() * 10E3   )}"
       _A=	F"{CI_HUB_USER}/{repo_name}"
       hf_api.create_repo(_UpperCAmelCase       ,					token=_UpperCAmelCase       ,					repo_type='dataset'       ,					private=_UpperCAmelCase   )
       hf_api.upload_file(
           token=_UpperCAmelCase       ,					path_or_fileobj=str(_UpperCAmelCase   )       ,					path_in_repo='data.zip'       ,					repo_id=_UpperCAmelCase       ,					repo_type='dataset'       ,					)
       yield repo_id
       try:
              hf_api.delete_repo(_UpperCAmelCase       ,					token=_UpperCAmelCase       ,					repo_type='dataset'   )
       except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
              pass

@pytest.fixture()
def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						str:




       '''simple docstring'''






       return hf_private_dataset_repo_zipped_txt_data_

@pytest.fixture(scope='session'   )
def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						List[Any]:




       '''simple docstring'''






       _A=	F"repo_zipped_img_data-{int(time.time() * 10E3   )}"
       _A=	F"{CI_HUB_USER}/{repo_name}"
       hf_api.create_repo(_UpperCAmelCase       ,					token=_UpperCAmelCase       ,					repo_type='dataset'       ,					private=_UpperCAmelCase   )
       hf_api.upload_file(
           token=_UpperCAmelCase       ,					path_or_fileobj=str(_UpperCAmelCase   )       ,					path_in_repo='data.zip'       ,					repo_id=_UpperCAmelCase       ,					repo_type='dataset'       ,					)
       yield repo_id
       try:
              hf_api.delete_repo(_UpperCAmelCase       ,					token=_UpperCAmelCase       ,					repo_type='dataset'   )
       except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
              pass





@pytest.fixture()
def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						List[Any]:




       '''simple docstring'''






       return hf_private_dataset_repo_zipped_img_data_






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_   ))       ,					)