


from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class 				snake_case__:







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Union[str, Any]					=							42  # [batch_size x 3]
						SCREAMING_SNAKE_CASE__      :  Any					=							42  # [batch_size x 3]
						SCREAMING_SNAKE_CASE__      :  int					=							42  # [batch_size x 3]
						SCREAMING_SNAKE_CASE__      :  Any					=							42  # [batch_size x 3]
						SCREAMING_SNAKE_CASE__      :  Union[str, Any]					=							42
						SCREAMING_SNAKE_CASE__      :  Dict					=							42
						SCREAMING_SNAKE_CASE__      :  List[str]					=							42
						SCREAMING_SNAKE_CASE__      :  Dict					=							42
						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							42




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




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




						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_ :      Union[str, Any]    				=				torch.arange(self.height * self.width							)
										lowerCAmelCase_ :      Tuple    				=				torch.stack(
										    [
										        pixel_indices % self.width,
										        torch.div(_lowercase   ,						self.width   ,						rounding_mode='''trunc'''							),
										    ]   ,						axis=1   ,						)
										return coords




						@property
						def 						lowercase_ (				self							)					->    Optional[Any]:
										lowerCAmelCase_ :      Union[str, Any]    				=				self.shape
										lowerCAmelCase_ :      int    				=				int(np.prod(_lowercase							)							)

										lowerCAmelCase_ :      Union[str, Any]    				=				self.get_image_coords()
										lowerCAmelCase_ :      List[Any]    				=				torch.broadcast_to(coords.unsqueeze(0							)   ,						[batch_size * inner_batch_size, *coords.shape]							)
										lowerCAmelCase_ :      List[str]    				=				self.get_camera_rays(_lowercase							)

										lowerCAmelCase_ :      Union[str, Any]    				=				rays.view(_lowercase   ,						inner_batch_size * self.height * self.width   ,						2   ,						3							)

										return rays




						def 						lowercase_ (				self   ,						__lowercase							)					->    List[str]:
										lowerCAmelCase_ :      str    				=				coords.shape
										assert n_coords == 2
										assert batch_size == self.origin.shape[0]

										lowerCAmelCase_ :      List[str]    				=				coords.view(_lowercase   ,						-1   ,						2							)

										lowerCAmelCase_ :      List[Any]    				=				self.resolution()
										lowerCAmelCase_ :      List[Any]    				=				self.fov()

										lowerCAmelCase_ :      List[str]    				=				(flat.float() / (res - 1)) * 2 - 1
										lowerCAmelCase_ :      int    				=				fracs * torch.tan(fov / 2							)

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




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase							)					->    int:
										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=_lowercase   ,						height=_lowercase   ,						x_fov=self.x_fov   ,						y_fov=self.y_fov   ,						)






def 			lowerCAmelCase (		lowerCAmelCase_ )-> Tuple:
				lowerCAmelCase_ :      int    				=				[]
				lowerCAmelCase_ :      str    				=				[]
				lowerCAmelCase_ :      Optional[int]    				=				[]
				lowerCAmelCase_ :      Optional[Any]    				=				[]
				for theta in np.linspace(0 ,   2 * np.pi ,   num=20 ):
								lowerCAmelCase_ :      str    				=				np.array([np.sin(_lowerCamelCase ), np.cos(_lowerCamelCase ), -0.5] )
								z /= np.sqrt(np.sum(z**2 ) )
								lowerCAmelCase_ :      Any    				=				-z * 4
								lowerCAmelCase_ :      Union[str, Any]    				=				np.array([np.cos(_lowerCamelCase ), -np.sin(_lowerCamelCase ), 0.0] )
								lowerCAmelCase_ :      Dict    				=				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 )) ,   )



import csv

import tweepy

# Twitter API credentials
_UpperCAmelCase			:  int     =""""""
_UpperCAmelCase			:  Optional[int]     =""""""
_UpperCAmelCase			:  Dict     =""""""
_UpperCAmelCase			:  str     =""""""



def 			lowerCAmelCase (		lowerCAmelCase_ )-> None:
				# authorize twitter, initialize tweepy
				lowerCAmelCase_ :      Optional[int]    				=				tweepy.OAuthHandler(lowerCAmelCase_ ,   lowerCAmelCase_ )
				auth.set_access_token(lowerCAmelCase_ ,   lowerCAmelCase_ )
				lowerCAmelCase_ :      Any    				=				tweepy.API(lowerCAmelCase_ )

				# initialize a list to hold all the tweepy Tweets
				lowerCAmelCase_ :      Dict    				=				[]

				# make initial request for most recent tweets (200 is the maximum allowed count)
				lowerCAmelCase_ :      Optional[int]    				=				api.user_timeline(screen_name=lowerCAmelCase_ ,   count=200 )

				# save most recent tweets
				alltweets.extend(lowerCAmelCase_ )

				# save the id of the oldest tweet less one
				lowerCAmelCase_ :      str    				=				alltweets[-1].id - 1

				# keep grabbing tweets until there are no tweets left to grab
				while len(lowerCAmelCase_ ) > 0:
								print(f"""getting tweets before {oldest}""" )

								# all subsequent requests use the max_id param to prevent duplicates
								lowerCAmelCase_ :      Optional[Any]    				=				api.user_timeline(
								    screen_name=lowerCAmelCase_ ,   count=200 ,   max_id=lowerCAmelCase_ )

								# save most recent tweets
								alltweets.extend(lowerCAmelCase_ )

								# update the id of the oldest tweet less one
								lowerCAmelCase_ :      Optional[Any]    				=				alltweets[-1].id - 1

								print(f"""...{len(lowerCAmelCase_ )} tweets downloaded so far""" )

				# transform the tweepy tweets into a 2D array that will populate the csv
				lowerCAmelCase_ :      Union[str, Any]    				=				[[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

				# write the csv
				with open(f"""new_{screen_name}_tweets.csv""" ,   '''w''' ) as f:
								lowerCAmelCase_ :      Optional[int]    				=				csv.writer(lowerCAmelCase_ )
								writer.writerow(['''id''', '''created_at''', '''text'''] )
								writer.writerows(lowerCAmelCase_ )


if __name__ == "__main__":
						# pass in the username of the account you want to download
						get_all_tweets("""FirePing32""")