


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


A      = logging.get_logger(__name__)

A      = {
    "Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  'dpt'






  def __init__(					self      :					List[Any]		,						_lowercase      :					Dict=7_68		,						_lowercase      :					Tuple=12		,						_lowercase      :					str=12		,						_lowercase      :					Optional[int]=30_72		,						_lowercase      :					Optional[int]="gelu"		,						_lowercase      :					int=0.0		,						_lowercase      :					Dict=0.0		,						_lowercase      :					Optional[int]=0.0_2		,						_lowercase      :					List[Any]=1E-12		,						_lowercase      :					Optional[Any]=3_84		,						_lowercase      :					Tuple=16		,						_lowercase      :					str=3		,						_lowercase      :					str=False		,						_lowercase      :					List[Any]=True		,						_lowercase      :					str=[2, 5, 8, 11]		,						_lowercase      :					str="project"		,						_lowercase      :					Any=[4, 2, 1, 0.5]		,						_lowercase      :					Optional[Any]=[96, 1_92, 3_84, 7_68]		,						_lowercase      :					List[str]=2_56		,						_lowercase      :					List[str]=-1		,						_lowercase      :					int=False		,						_lowercase      :					Union[str, Any]=True		,						_lowercase      :					Optional[int]=0.4		,						_lowercase      :					Union[str, Any]=2_55		,						_lowercase      :					str=0.1		,						_lowercase      :					str=[1, 10_24, 24, 24]		,						_lowercase      :					int=[0, 1]		,						_lowercase      :					Any=None		,						**_lowercase      :					Any		,						):




    """simple docstring"""







    super().__init__(**_lowercase )

    UpperCAmelCase__										=     hidden_size
    UpperCAmelCase__										=     is_hybrid

    if self.is_hybrid:
      if backbone_config is None:
        logger.info("Initializing the config with a `BiT` backbone." )
        UpperCAmelCase__										=     {
            "global_padding": "same",
            "layer_type": "bottleneck",
            "depths": [3, 4, 9],
            "out_features": ["stage1", "stage2", "stage3"],
            "embedding_dynamic_padding": True,
        }
        UpperCAmelCase__										=     BitConfig(**_lowercase )
      elif isinstance(_lowercase		,						_lowercase ):
        logger.info("Initializing the config with a `BiT` backbone." )
        UpperCAmelCase__										=     BitConfig(**_lowercase )
      elif isinstance(_lowercase		,						_lowercase ):
        UpperCAmelCase__										=     backbone_config
      else:
        raise ValueError(
            F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.""" )

      UpperCAmelCase__										=     backbone_featmap_shape
      UpperCAmelCase__										=     neck_ignore_stages

      if readout_type != "project":
        raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode." )
    else:
      UpperCAmelCase__										=     None
      UpperCAmelCase__										=     None
      UpperCAmelCase__										=     []

    UpperCAmelCase__										=     num_hidden_layers
    UpperCAmelCase__										=     num_attention_heads
    UpperCAmelCase__										=     intermediate_size
    UpperCAmelCase__										=     hidden_act
    UpperCAmelCase__										=     hidden_dropout_prob
    UpperCAmelCase__										=     attention_probs_dropout_prob
    UpperCAmelCase__										=     initializer_range
    UpperCAmelCase__										=     layer_norm_eps
    UpperCAmelCase__										=     image_size
    UpperCAmelCase__										=     patch_size
    UpperCAmelCase__										=     num_channels
    UpperCAmelCase__										=     qkv_bias
    UpperCAmelCase__										=     backbone_out_indices
    if readout_type not in ["ignore", "add", "project"]:
      raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']" )
    UpperCAmelCase__										=     readout_type
    UpperCAmelCase__										=     reassemble_factors
    UpperCAmelCase__										=     neck_hidden_sizes
    UpperCAmelCase__										=     fusion_hidden_size
    UpperCAmelCase__										=     head_in_index
    UpperCAmelCase__										=     use_batch_norm_in_fusion_residual
    # auxiliary head attributes (semantic segmentation)
    UpperCAmelCase__										=     use_auxiliary_head
    UpperCAmelCase__										=     auxiliary_loss_weight
    UpperCAmelCase__										=     semantic_loss_ignore_index
    UpperCAmelCase__										=     semantic_classifier_dropout






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




    """simple docstring"""







    UpperCAmelCase__										=     copy.deepcopy(self.__dict__ )

    if output["backbone_config"] is not None:
      UpperCAmelCase__										=     self.backbone_config.to_dict()

    UpperCAmelCase__										=     self.__class__.model_type
    return output









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")





