



import csv

import tweepy

# Twitter API credentials
SCREAMING_SNAKE_CASE__   			=   ''''''
SCREAMING_SNAKE_CASE__   			=   ''''''
SCREAMING_SNAKE_CASE__   			=   ''''''
SCREAMING_SNAKE_CASE__   			=   ''''''


def A    (							__UpperCamelCase       )    ->   None:
			# authorize twitter, initialize tweepy
			A__	  =     tweepy.OAuthHandler(__UpperCamelCase			,						__UpperCamelCase       )
			auth.set_access_token(__UpperCamelCase			,						__UpperCamelCase       )
			A__	  =     tweepy.API(__UpperCamelCase       )

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

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

			# save most recent tweets
			alltweets.extend(__UpperCamelCase       )

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

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

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

						# save most recent tweets
						alltweets.extend(__UpperCamelCase       )

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

						print(f'''...{len(__UpperCamelCase       )} tweets downloaded so far'''       )

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


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









SCREAMING_SNAKE_CASE__   			=   {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   list[str]:
			A__	  =     set()
			# keep track of all the paths to be checked
			A__	  =     [[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						A__	  =     queue.pop(0       )
						# get the last node from the path
						A__	  =     path[-1]
						if node not in explored:
									A__	  =     graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												A__	  =     list(__UpperCamelCase       )
												new_path.append(__UpperCamelCase       )
												queue.append(__UpperCamelCase       )
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(__UpperCamelCase       )

    # in case there's no path between the 2 nodes
			return []


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			A__	  =     [start]
			A__	  =     set(__UpperCamelCase       )
			# Keep tab on distances from `start` node.
			A__	  =     {start: 0, target: -1}
			while queue:
						A__	  =     queue.pop(0       )
						if node == target:
									A__	  =     (
									    dist[node] if dist[target] == -1 else min(dist[target]			,						dist[node]       )
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(__UpperCamelCase       )
												queue.append(__UpperCamelCase       )
												A__	  =     dist[node] + 1
			return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4




