



'''simple docstring'''



import csv

import tweepy

# Twitter API credentials
a__  :     Dict     							=     ''
a__  :     List[str]     							=     ''
a__  :     Optional[Any]     							=     ''
a__  :     Any     							=     ''







def _lowercase				(					__A    ):



 '''simple docstring'''



 __UpperCamelCase			 =					tweepy.OAuthHandler(__A							,__A    )
 auth.set_access_token(__A							,__A    )
 __UpperCamelCase			 =					tweepy.API(__A    )

 # initialize a list to hold all the tweepy Tweets
 __UpperCamelCase			 =					[]

 # make initial request for most recent tweets (200 is the maximum allowed count)
 __UpperCamelCase			 =					api.user_timeline(screen_name=__A							,count=200    )

 # save most recent tweets
 alltweets.extend(__A    )

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

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

  # all subsequent requests use the max_id param to prevent duplicates
  __UpperCamelCase			 =					api.user_timeline(
      screen_name=__A							,count=200							,max_id=__A    )

  # save most recent tweets
  alltweets.extend(__A    )

  # update the id of the oldest tweet less one
  __UpperCamelCase			 =					alltweets[-1].id - 1

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

 # transform the tweepy tweets into a 2D array that will populate the csv
 __UpperCamelCase			 =					[[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:
  __UpperCamelCase			 =					csv.writer(__A    )
  writer.writerow(["""id""", """created_at""", """text"""]    )
  writer.writerows(__A    )


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










'''simple docstring'''



from PIL import Image







def _lowercase				(					__A    ):



 '''simple docstring'''



 __UpperCamelCase ,						__UpperCamelCase			 =					image.size
 __UpperCamelCase			 =					0
 __UpperCamelCase			 =					image.load()
 for i in range(__A    ):
  for j in range(__A    ):
   __UpperCamelCase			 =					pixels[j, i]
   mean += pixel
 mean //= width * height

 for j in range(__A    ):
  for i in range(__A    ):
   __UpperCamelCase			 =					255 if pixels[i, j] > mean else 0
 return image


if __name__ == "__main__":
 a__  :     Optional[int]     							=     mean_threshold(Image.open('path_to_image').convert('L'))
 image.save('output_image_path')





