


from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_UpperCAmelCase			:  int     =logging.get_logger(__name__)

_UpperCAmelCase			:  Optional[Any]     ={
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  str					=							"""table-transformer"""
						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							["""past_key_values"""]
						SCREAMING_SNAKE_CASE__      :  Tuple					=							{
						    """hidden_size""": """d_model""",
						    """num_attention_heads""": """encoder_attention_heads""",
						}




						def __init__(				self   ,						__lowercase=True   ,						__lowercase=None   ,						__lowercase=3   ,						__lowercase=1_0_0   ,						__lowercase=6   ,						__lowercase=2_0_4_8   ,						__lowercase=8   ,						__lowercase=6   ,						__lowercase=2_0_4_8   ,						__lowercase=8   ,						__lowercase=0.0   ,						__lowercase=0.0   ,						__lowercase=True   ,						__lowercase="relu"   ,						__lowercase=2_5_6   ,						__lowercase=0.1   ,						__lowercase=0.0   ,						__lowercase=0.0   ,						__lowercase=0.02   ,						__lowercase=1.0   ,						__lowercase=False   ,						__lowercase="sine"   ,						__lowercase="resnet50"   ,						__lowercase=True   ,						__lowercase=False   ,						__lowercase=1   ,						__lowercase=5   ,						__lowercase=2   ,						__lowercase=1   ,						__lowercase=1   ,						__lowercase=5   ,						__lowercase=2   ,						__lowercase=0.1   ,						**__lowercase   ,						)					->    Union[str, Any]:
										if backbone_config is not None and use_timm_backbone:
														raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''							)

										if not use_timm_backbone:
														if backbone_config is None:
																		logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''							)
																		lowerCAmelCase_ :      List[str]    				=				CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']							)
														elif isinstance(__lowercase   ,						__lowercase							):
																		lowerCAmelCase_ :      Optional[Any]    				=				backbone_config.get('''model_type'''							)
																		lowerCAmelCase_ :      Any    				=				CONFIG_MAPPING[backbone_model_type]
																		lowerCAmelCase_ :      int    				=				config_class.from_dict(__lowercase							)
														# set timm attributes to None
														lowerCAmelCase_ :      Optional[Any]    				=				None, None, None

										lowerCAmelCase_ :      Tuple    				=				use_timm_backbone
										lowerCAmelCase_ :      str    				=				backbone_config
										lowerCAmelCase_ :      Dict    				=				num_channels
										lowerCAmelCase_ :      str    				=				num_queries
										lowerCAmelCase_ :      List[Any]    				=				d_model
										lowerCAmelCase_ :      int    				=				encoder_ffn_dim
										lowerCAmelCase_ :      Union[str, Any]    				=				encoder_layers
										lowerCAmelCase_ :      Dict    				=				encoder_attention_heads
										lowerCAmelCase_ :      str    				=				decoder_ffn_dim
										lowerCAmelCase_ :      Optional[Any]    				=				decoder_layers
										lowerCAmelCase_ :      Dict    				=				decoder_attention_heads
										lowerCAmelCase_ :      Optional[Any]    				=				dropout
										lowerCAmelCase_ :      List[Any]    				=				attention_dropout
										lowerCAmelCase_ :      Dict    				=				activation_dropout
										lowerCAmelCase_ :      Optional[Any]    				=				activation_function
										lowerCAmelCase_ :      Union[str, Any]    				=				init_std
										lowerCAmelCase_ :      Optional[Any]    				=				init_xavier_std
										lowerCAmelCase_ :      int    				=				encoder_layerdrop
										lowerCAmelCase_ :      Optional[int]    				=				decoder_layerdrop
										lowerCAmelCase_ :      Union[str, Any]    				=				encoder_layers
										lowerCAmelCase_ :      Dict    				=				auxiliary_loss
										lowerCAmelCase_ :      Optional[int]    				=				position_embedding_type
										lowerCAmelCase_ :      str    				=				backbone
										lowerCAmelCase_ :      Union[str, Any]    				=				use_pretrained_backbone
										lowerCAmelCase_ :      Union[str, Any]    				=				dilation
										# Hungarian matcher
										lowerCAmelCase_ :      int    				=				class_cost
										lowerCAmelCase_ :      Union[str, Any]    				=				bbox_cost
										lowerCAmelCase_ :      Any    				=				giou_cost
										# Loss coefficients
										lowerCAmelCase_ :      int    				=				mask_loss_coefficient
										lowerCAmelCase_ :      int    				=				dice_loss_coefficient
										lowerCAmelCase_ :      Dict    				=				bbox_loss_coefficient
										lowerCAmelCase_ :      List[str]    				=				giou_loss_coefficient
										lowerCAmelCase_ :      List[Any]    				=				eos_coefficient
										super().__init__(is_encoder_decoder=__lowercase   ,						**__lowercase							)




						@property
						def 						lowercase_ (				self							)					->    int:
										return self.encoder_attention_heads




						@property
						def 						lowercase_ (				self							)					->    int:
										return self.d_model







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  List[Any]					=							version.parse("""1.11"""							)




						@property
						def 						lowercase_ (				self							)					->    Mapping[str, Mapping[int, str]]:
										return OrderedDict(
										    [
										        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
										        ('''pixel_mask''', {0: '''batch'''}),
										    ]							)




						@property
						def 						lowercase_ (				self							)					->    float:
										return 1e-5




						@property
						def 						lowercase_ (				self							)					->    int:
										return 1_2



import csv

import tweepy

# Twitter API credentials
_UpperCAmelCase			:  int     =""""""
_UpperCAmelCase			:  Optional[int]     =""""""
_UpperCAmelCase			:  Dict     =""""""
_UpperCAmelCase			:  str     =""""""



def 			lowerCAmelCase (		lowerCAmelCase_ )-> None:
				# authorize twitter, initialize tweepy
				lowerCAmelCase_ :      Optional[int]    				=				tweepy.OAuthHandler(lowerCAmelCase_ ,   lowerCAmelCase_ )
				auth.set_access_token(lowerCAmelCase_ ,   lowerCAmelCase_ )
				lowerCAmelCase_ :      Any    				=				tweepy.API(lowerCAmelCase_ )

				# initialize a list to hold all the tweepy Tweets
				lowerCAmelCase_ :      Dict    				=				[]

				# make initial request for most recent tweets (200 is the maximum allowed count)
				lowerCAmelCase_ :      Optional[int]    				=				api.user_timeline(screen_name=lowerCAmelCase_ ,   count=200 )

				# save most recent tweets
				alltweets.extend(lowerCAmelCase_ )

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

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

								# all subsequent requests use the max_id param to prevent duplicates
								lowerCAmelCase_ :      Optional[Any]    				=				api.user_timeline(
								    screen_name=lowerCAmelCase_ ,   count=200 ,   max_id=lowerCAmelCase_ )

								# save most recent tweets
								alltweets.extend(lowerCAmelCase_ )

								# update the id of the oldest tweet less one
								lowerCAmelCase_ :      Optional[Any]    				=				alltweets[-1].id - 1

								print(f"""...{len(lowerCAmelCase_ )} tweets downloaded so far""" )

				# transform the tweepy tweets into a 2D array that will populate the csv
				lowerCAmelCase_ :      Union[str, Any]    				=				[[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:
								lowerCAmelCase_ :      Optional[int]    				=				csv.writer(lowerCAmelCase_ )
								writer.writerow(['''id''', '''created_at''', '''text'''] )
								writer.writerows(lowerCAmelCase_ )


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