
"""simple docstring"""

from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class __lowerCAmelCase	:
     lowercase		     =		42  # [batch_size x 3]
     lowercase		     =		42  # [batch_size x 3]
     lowercase		     =		42  # [batch_size x 3]
     lowercase		     =		42  # [batch_size x 3]
     lowercase		     =		42
     lowercase		     =		42
     lowercase		     =		42
     lowercase		     =		42
     lowercase		     =		42





     def 		UpperCAmelCase      (			self   ):

      '''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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= torch.arange(self.height * self.width   )
      __UpperCamelCase       						= torch.stack(
          [
              pixel_indices % self.width,
              torch.div(__UpperCAmelCase			,  self.width			,  rounding_mode='trunc'   ),
          ]			,  axis=1			,  )
      return coords





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase						,			*__UpperCamelCase       						= self.shape
      __UpperCamelCase       						= int(np.prod(__UpperCAmelCase   )   )

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

      __UpperCamelCase       						= rays.view(__UpperCAmelCase			,  inner_batch_size * self.height * self.width			,  2			,  3   )

      return rays





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




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

      __UpperCamelCase       						= coords.view(__UpperCAmelCase			,  -1			,  2   )

      __UpperCamelCase       						= self.resolution()
      __UpperCamelCase       						= self.fov()

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

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







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

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






def 							A		(       snake_case   :str	)   ->			Union[str, Any]:
 __UpperCamelCase       						= []
 __UpperCamelCase       						= []
 __UpperCamelCase       						= []
 __UpperCamelCase       						= []
 for theta in np.linspace(0       ,      2 * np.pi       ,      num=2_0	):
  __UpperCamelCase       						= np.array([np.sin(snake_case	), np.cos(snake_case	), -0.5]	)
  z /= np.sqrt(np.sum(z**2	)	)
  __UpperCamelCase       						= -z * 4
  __UpperCamelCase       						= np.array([np.cos(snake_case	), -np.sin(snake_case	), 0.0]	)
  __UpperCamelCase       						= 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	))       ,      )







"""simple docstring"""

import time
from contextlib import contextmanager
from pathlib import Path

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


UpperCamelCase			:       Union[str, Any]									=				"__DUMMY_TRANSFORMERS_USER__"
UpperCamelCase			:       List[Any]									=				"Dummy User"
UpperCamelCase			:       List[Any]									=				"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"

UpperCamelCase			:       Any									=				"https://hub-ci.huggingface.co"
UpperCamelCase			:       str									=				CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
UpperCamelCase			:       Optional[Any]									=				CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
UpperCamelCase			:       Optional[int]									=				Path("~/.huggingface/hub_ci_token").expanduser()






@pytest.fixture
def 							A		(       snake_case   :Optional[Any]	)   ->			Union[str, Any]:
 monkeypatch.setattr(
     'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE'       ,      snake_case	)






@pytest.fixture
def 							A		(       snake_case   :str	)   ->			List[Any]:
 monkeypatch.setattr('datasets.config.HF_ENDPOINT'       ,      snake_case	)
 monkeypatch.setattr('datasets.config.HUB_DATASETS_URL'       ,      snake_case	)






@pytest.fixture
def 							A		(       snake_case   :Optional[int]	)   ->			Dict:
 monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token'       ,      snake_case	)






@pytest.fixture
def 							A		(       snake_case   :List[Any]       ,      snake_case   :Tuple	)   ->			Tuple:
 HfFolder.save_token(snake_case	)
 yield
 HfFolder.delete_token()






@pytest.fixture(scope='session'	)
def 							A		(       )   ->			List[Any]:
 return HfApi(endpoint=snake_case	)






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi	)   ->			List[Any]:
 __UpperCamelCase       						= HfFolder.get_token()
 HfFolder.save_token(snake_case	)
 yield CI_HUB_USER_TOKEN
 if previous_token is not None:
  HfFolder.save_token(snake_case	)






@pytest.fixture
def 							A		(       snake_case   :str	)   ->			str:
 def _cleanup_repo(snake_case   :Union[str, Any]	):
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)

 return _cleanup_repo






@pytest.fixture
def 							A		(       snake_case   :List[str]	)   ->			Any:
 @contextmanager
 def _temporary_repo(snake_case   :Tuple	):
  try:
   yield repo_id
  finally:
   cleanup_repo(snake_case	)

 return _temporary_repo






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi       ,      snake_case   :Dict       ,      snake_case   :Dict	)   ->			List[str]:
 __UpperCamelCase       						= f'repo_txt_data-{int(time.time() * 10e3	)}'
 __UpperCamelCase       						= f'{CI_HUB_USER}/{repo_name}'
 hf_api.create_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'       ,      private=snake_case	)
 hf_api.upload_file(
     token=snake_case       ,      path_or_fileobj=str(snake_case	)       ,      path_in_repo='data/text_data.txt'       ,      repo_id=snake_case       ,      repo_type='dataset'       ,      )
 yield repo_id
 try:
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)
 except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
  pass






@pytest.fixture()
def 							A		(       snake_case   :Optional[int]       ,      snake_case   :str       ,      snake_case   :Tuple	)   ->			Any:
 return hf_private_dataset_repo_txt_data_






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi       ,      snake_case   :List[Any]       ,      snake_case   :str	)   ->			Optional[int]:
 __UpperCamelCase       						= f'repo_zipped_txt_data-{int(time.time() * 10e3	)}'
 __UpperCamelCase       						= f'{CI_HUB_USER}/{repo_name}'
 hf_api.create_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'       ,      private=snake_case	)
 hf_api.upload_file(
     token=snake_case       ,      path_or_fileobj=str(snake_case	)       ,      path_in_repo='data.zip'       ,      repo_id=snake_case       ,      repo_type='dataset'       ,      )
 yield repo_id
 try:
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)
 except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
  pass






@pytest.fixture()
def 							A		(       snake_case   :int       ,      snake_case   :Optional[int]       ,      snake_case   :Optional[int]	)   ->			str:
 return hf_private_dataset_repo_zipped_txt_data_






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi       ,      snake_case   :List[str]       ,      snake_case   :Any	)   ->			List[Any]:
 __UpperCamelCase       						= f'repo_zipped_img_data-{int(time.time() * 10e3	)}'
 __UpperCamelCase       						= f'{CI_HUB_USER}/{repo_name}'
 hf_api.create_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'       ,      private=snake_case	)
 hf_api.upload_file(
     token=snake_case       ,      path_or_fileobj=str(snake_case	)       ,      path_in_repo='data.zip'       ,      repo_id=snake_case       ,      repo_type='dataset'       ,      )
 yield repo_id
 try:
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)
 except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
  pass






@pytest.fixture()
def 							A		(       snake_case   :List[str]       ,      snake_case   :Optional[Any]       ,      snake_case   :Optional[int]	)   ->			Optional[int]:
 return hf_private_dataset_repo_zipped_img_data_



