


import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np





def __UpperCAmelCase       (		__A ,						__A			)						->					np.array:



       '''simple docstring'''





       UpperCAmelCase__										=     F"""{sampling_rate}"""
       UpperCAmelCase__										=     "1"
       UpperCAmelCase__										=     "f32le"
       UpperCAmelCase__										=     [
           "ffmpeg",
           "-i",
           "pipe:0",
           "-ac",
           ac,
           "-ar",
           ar,
           "-f",
           format_for_conversion,
           "-hide_banner",
           "-loglevel",
           "quiet",
           "pipe:1",
       ]

       try:
              with subprocess.Popen(__A ,						stdin=subprocess.PIPE ,						stdout=subprocess.PIPE			) as ffmpeg_process:
                     UpperCAmelCase__										=     ffmpeg_process.communicate(__A			)
       except FileNotFoundError as error:
              raise ValueError("ffmpeg was not found but is required to load audio files from filename"			) from error
       UpperCAmelCase__										=     output_stream[0]
       UpperCAmelCase__										=     np.frombuffer(__A ,						np.floataa			)
       if audio.shape[0] == 0:
              raise ValueError("Malformed soundfile"			)
       return audio





def __UpperCAmelCase       (		__A ,						__A ,						__A = "f32le" ,						)						->					Optional[int]:



       '''simple docstring'''





       UpperCAmelCase__										=     F"""{sampling_rate}"""
       UpperCAmelCase__										=     "1"
       if format_for_conversion == "s16le":
              UpperCAmelCase__										=     2
       elif format_for_conversion == "f32le":
              UpperCAmelCase__										=     4
       else:
              raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""			)

       UpperCAmelCase__										=     platform.system()
       if system == "Linux":
              UpperCAmelCase__										=     "alsa"
              UpperCAmelCase__										=     "default"
       elif system == "Darwin":
              UpperCAmelCase__										=     "avfoundation"
              UpperCAmelCase__										=     ":0"
       elif system == "Windows":
              UpperCAmelCase__										=     "dshow"
              UpperCAmelCase__										=     "default"

       UpperCAmelCase__										=     [
           "ffmpeg",
           "-f",
           format_,
           "-i",
           input_,
           "-ac",
           ac,
           "-ar",
           ar,
           "-f",
           format_for_conversion,
           "-fflags",
           "nobuffer",
           "-hide_banner",
           "-loglevel",
           "quiet",
           "pipe:1",
       ]
       UpperCAmelCase__										=     int(round(sampling_rate * chunk_length_s			)			) * size_of_sample
       UpperCAmelCase__										=     _ffmpeg_stream(__A ,						__A			)
       for item in iterator:
              yield item





def __UpperCAmelCase       (		__A ,						__A ,						__A = None ,						__A = None ,						__A = "f32le" ,						)						->					Union[str, Any]:



       '''simple docstring'''





       if stream_chunk_s is not None:
              UpperCAmelCase__										=     stream_chunk_s
       else:
              UpperCAmelCase__										=     chunk_length_s

       UpperCAmelCase__										=     ffmpeg_microphone(__A ,						__A ,						format_for_conversion=__A			)
       if format_for_conversion == "s16le":
              UpperCAmelCase__										=     np.intaa
              UpperCAmelCase__										=     2
       elif format_for_conversion == "f32le":
              UpperCAmelCase__										=     np.floataa
              UpperCAmelCase__										=     4
       else:
              raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""			)

       if stride_length_s is None:
              UpperCAmelCase__										=     chunk_length_s / 6
       UpperCAmelCase__										=     int(round(sampling_rate * chunk_length_s			)			) * size_of_sample
       if isinstance(__A ,						(int, float)			):
              UpperCAmelCase__										=     [stride_length_s, stride_length_s]

       UpperCAmelCase__										=     int(round(sampling_rate * stride_length_s[0]			)			) * size_of_sample
       UpperCAmelCase__										=     int(round(sampling_rate * stride_length_s[1]			)			) * size_of_sample
       UpperCAmelCase__										=     datetime.datetime.now()
       UpperCAmelCase__										=     datetime.timedelta(seconds=__A			)
       for item in chunk_bytes_iter(__A ,						__A ,						stride=(stride_left, stride_right) ,						stream=__A			):
              # Put everything back in numpy scale
              UpperCAmelCase__										=     np.frombuffer(item["raw"] ,						dtype=__A			)
              UpperCAmelCase__										=     (
                  item["stride"][0] // size_of_sample,
                  item["stride"][1] // size_of_sample,
              )
              UpperCAmelCase__										=     sampling_rate
              audio_time += delta
              if datetime.datetime.now() > audio_time + 1_0 * delta:
                     # We're late !! SKIP
                     continue
              yield item





def __UpperCAmelCase       (		__A ,						__A ,						__A ,						__A = False			)						->					List[str]:



       '''simple docstring'''





       UpperCAmelCase__										=     b""
       UpperCAmelCase__						,			UpperCAmelCase__										=     stride
       if stride_left + stride_right >= chunk_len:
              raise ValueError(
                  F"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"""			)
       UpperCAmelCase__										=     0
       for raw in iterator:
              acc += raw
              if stream and len(__A			) < chunk_len:
                     UpperCAmelCase__										=     (_stride_left, 0)
                     yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
              else:
                     while len(__A			) >= chunk_len:
                            # We are flushing the accumulator
                            UpperCAmelCase__										=     (_stride_left, stride_right)
                            UpperCAmelCase__										=     {"raw": acc[:chunk_len], "stride": stride}
                            if stream:
                                   UpperCAmelCase__										=     False
                            yield item
                            UpperCAmelCase__										=     stride_left
                            UpperCAmelCase__										=     acc[chunk_len - stride_left - stride_right :]
    # Last chunk
       if len(__A			) > stride_left:
              UpperCAmelCase__										=     {"raw": acc, "stride": (_stride_left, 0)}
              if stream:
                     UpperCAmelCase__										=     False
              yield item





def __UpperCAmelCase       (		__A ,						__A			)						->					Optional[Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     2**2_4  # 16Mo
       try:
              with subprocess.Popen(__A ,						stdout=subprocess.PIPE ,						bufsize=__A			) as ffmpeg_process:
                     while True:
                            UpperCAmelCase__										=     ffmpeg_process.stdout.read(__A			)
                            if raw == b"":
                                   break
                            yield raw
       except FileNotFoundError as error:
              raise ValueError("ffmpeg was not found but is required to stream audio files from filename"			) from error









import csv

import tweepy

# Twitter API credentials
A      = ""
A      = ""
A      = ""
A      = ""





def __UpperCAmelCase       (		__A			)						->					None:



       '''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=2_0_0			)

       # 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=2_0_0 ,						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")





