






import csv

import tweepy

# Twitter API credentials
__A  			=					''
__A  			=					''
__A  			=					''
__A  			=					''




def   __A       (  _lowercase			):

					'''simple docstring'''







					_A										=			tweepy.OAuthHandler(lowercase__       ,      lowercase__			)
					auth.set_access_token(lowercase__       ,      lowercase__			)
					_A										=			tweepy.API(lowercase__			)

					# 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=lowercase__       ,      count=2_00			)

					# save most recent tweets
					alltweets.extend(lowercase__			)

					# 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(lowercase__			) > 0:
										print(f"""getting tweets before {oldest}"""			)

										# all subsequent requests use the max_id param to prevent duplicates
										_A										=			api.user_timeline(
										    screen_name=lowercase__       ,      count=2_00       ,      max_id=lowercase__			)

										# save most recent tweets
										alltweets.extend(lowercase__			)

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

										print(f"""...{len(lowercase__			)} 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(lowercase__			)
										writer.writerow(['''id''', '''created_at''', '''text''']			)
										writer.writerows(lowercase__			)


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





'''simple docstring'''





import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute







def snake_case_     (					lowercase__ = 3		):
  if isinstance(lowercase__   ,       lowercase__		):
    raise TypeError("number of qubits must be a integer."		)
  if number_of_qubits <= 0:
    raise ValueError("number of qubits must be > 0."		)
  if math.floor(lowercase__		) != number_of_qubits:
    raise ValueError("number of qubits must be exact integer."		)
  if number_of_qubits > 1_0:
    raise ValueError("number of qubits too large to simulate(>10)."		)

  UpperCAmelCase__      :					Optional[int]				       =							QuantumRegister(lowercase__   ,       "qr"		)
  UpperCAmelCase__      :					List[Any]				       =							ClassicalRegister(lowercase__   ,       "cr"		)

  UpperCAmelCase__      :					List[str]				       =							QuantumCircuit(lowercase__   ,       lowercase__		)

  UpperCAmelCase__      :					Union[str, Any]				       =							number_of_qubits

  for i in range(lowercase__		):
    quantum_circuit.h(number_of_qubits - i - 1		)
    counter -= 1
    for j in range(lowercase__		):
      quantum_circuit.cp(np.pi / 2 ** (counter - j)   ,       lowercase__   ,       lowercase__		)

  for k in range(number_of_qubits // 2		):
    quantum_circuit.swap(lowercase__   ,       number_of_qubits - k - 1		)

  # measure all the qubits
  quantum_circuit.measure(lowercase__   ,       lowercase__		)
  # simulate with 10000 shots
  UpperCAmelCase__      :					Union[str, Any]				       =							Aer.get_backend("qasm_simulator"		)
  UpperCAmelCase__      :					Optional[int]				       =							execute(lowercase__   ,       lowercase__   ,       shots=1_0_0_0_0		)

  return job.result().get_counts(lowercase__		)


if __name__ == "__main__":
      print(
          F'Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'
      )



