


from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A      = logging.get_logger(__name__)

A      = {
    "google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  'efficientnet'






  def __init__(					self      :					int		,						_lowercase      :					int = 3		,						_lowercase      :					int = 6_00		,						_lowercase      :					float = 2.0		,						_lowercase      :					float = 3.1		,						_lowercase      :					int = 8		,						_lowercase      :					List[int] = [3, 3, 5, 3, 5, 5, 3]		,						_lowercase      :					List[int] = [32, 16, 24, 40, 80, 1_12, 1_92]		,						_lowercase      :					List[int] = [16, 24, 40, 80, 1_12, 1_92, 3_20]		,						_lowercase      :					List[int] = []		,						_lowercase      :					List[int] = [1, 2, 2, 2, 1, 2, 1]		,						_lowercase      :					List[int] = [1, 2, 2, 3, 3, 4, 1]		,						_lowercase      :					List[int] = [1, 6, 6, 6, 6, 6, 6]		,						_lowercase      :					float = 0.2_5		,						_lowercase      :					str = "swish"		,						_lowercase      :					int = 25_60		,						_lowercase      :					str = "mean"		,						_lowercase      :					float = 0.0_2		,						_lowercase      :					float = 0.0_0_1		,						_lowercase      :					float = 0.9_9		,						_lowercase      :					float = 0.5		,						_lowercase      :					float = 0.2		,						**_lowercase      :					List[Any]		,						):




    """simple docstring"""







    super().__init__(**_lowercase )

    UpperCAmelCase__										=     num_channels
    UpperCAmelCase__										=     image_size
    UpperCAmelCase__										=     width_coefficient
    UpperCAmelCase__										=     depth_coefficient
    UpperCAmelCase__										=     depth_divisor
    UpperCAmelCase__										=     kernel_sizes
    UpperCAmelCase__										=     in_channels
    UpperCAmelCase__										=     out_channels
    UpperCAmelCase__										=     depthwise_padding
    UpperCAmelCase__										=     strides
    UpperCAmelCase__										=     num_block_repeats
    UpperCAmelCase__										=     expand_ratios
    UpperCAmelCase__										=     squeeze_expansion_ratio
    UpperCAmelCase__										=     hidden_act
    UpperCAmelCase__										=     hidden_dim
    UpperCAmelCase__										=     pooling_type
    UpperCAmelCase__										=     initializer_range
    UpperCAmelCase__										=     batch_norm_eps
    UpperCAmelCase__										=     batch_norm_momentum
    UpperCAmelCase__										=     dropout_rate
    UpperCAmelCase__										=     drop_connect_rate
    UpperCAmelCase__										=     sum(_lowercase ) * 4




class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  version.parse('1.11'		)






  @property
  def 			_UpperCAmelCase  (					self      :					Dict ):




    """simple docstring"""







    return OrderedDict(
        [
            ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
        ] )






  @property
  def 			_UpperCAmelCase  (					self      :					int ):




    """simple docstring"""







    return 1E-5









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")





