


from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
						import timm


if is_torch_available():
						from torch import Tensor







class 				snake_case__(							UpperCAmelCase__,						UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Dict					=							"""pixel_values"""
						SCREAMING_SNAKE_CASE__      :  Tuple					=							False
						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							TimmBackboneConfig




						def __init__(				self   ,						__lowercase   ,						**__lowercase							)					->    Dict:
										requires_backends(self   ,						'''timm'''							)
										super().__init__(__lowercase							)
										lowerCAmelCase_ :      List[Any]    				=				config

										if config.backbone is None:
														raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''							)

										if config.backbone not in timm.list_models():
														raise ValueError(f"""backbone {config.backbone} is not supported by timm."""							)

										if hasattr(__lowercase   ,						'''out_features'''							) and config.out_features is not None:
														raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''							)

										lowerCAmelCase_ :      Any    				=				getattr(__lowercase   ,						'''use_pretrained_backbone'''   ,						__lowercase							)
										if pretrained is None:
														raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''							)

										# We just take the final layer by default. This matches the default for the transformers models.
										lowerCAmelCase_ :      List[Any]    				=				config.out_indices if getattr(__lowercase   ,						'''out_indices'''   ,						__lowercase							) is not None else (-1,)

										lowerCAmelCase_ :      List[Any]    				=				timm.create_model(
										    config.backbone   ,						pretrained=__lowercase   ,						features_only=config.features_only   ,						in_chans=config.num_channels   ,						out_indices=__lowercase   ,						**__lowercase   ,						)
										# These are used to control the output of the model when called. If output_hidden_states is True, then
										# return_layers is modified to include all layers.
										lowerCAmelCase_ :      Optional[int]    				=				self._backbone.return_layers
										lowerCAmelCase_ :      List[str]    				=				{layer['''module''']: str(__lowercase							) for i, layer in enumerate(self._backbone.feature_info.info							)}
										super()._init_backbone(__lowercase							)




						@classmethod
						def 						lowercase_ (				cls   ,						__lowercase   ,						*__lowercase   ,						**__lowercase							)					->    List[Any]:
										requires_backends(cls   ,						['''vision''', '''timm''']							)
										from ...models.timm_backbone import TimmBackboneConfig

										lowerCAmelCase_ :      List[Any]    				=				kwargs.pop('''config'''   ,						TimmBackboneConfig()							)

										lowerCAmelCase_ :      str    				=				kwargs.pop('''use_timm_backbone'''   ,						__lowercase							)
										if not use_timm:
														raise ValueError('''use_timm_backbone must be True for timm backbones'''							)

										lowerCAmelCase_ :      int    				=				kwargs.pop('''num_channels'''   ,						config.num_channels							)
										lowerCAmelCase_ :      List[str]    				=				kwargs.pop('''features_only'''   ,						config.features_only							)
										lowerCAmelCase_ :      Union[str, Any]    				=				kwargs.pop('''use_pretrained_backbone'''   ,						config.use_pretrained_backbone							)
										lowerCAmelCase_ :      Dict    				=				kwargs.pop('''out_indices'''   ,						config.out_indices							)
										lowerCAmelCase_ :      Any    				=				TimmBackboneConfig(
										    backbone=__lowercase   ,						num_channels=__lowercase   ,						features_only=__lowercase   ,						use_pretrained_backbone=__lowercase   ,						out_indices=__lowercase   ,						)
										return super()._from_config(__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase							)					->    Any:
										pass




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase=None   ,						__lowercase=None   ,						__lowercase=None   ,						**__lowercase							)					->    Union[BackboneOutput, Tuple[Tensor, ...]]:
										lowerCAmelCase_ :      List[Any]    				=				return_dict if return_dict is not None else self.config.use_return_dict
										lowerCAmelCase_ :      Dict    				=				(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										lowerCAmelCase_ :      Any    				=				output_attentions if output_attentions is not None else self.config.output_attentions

										if output_attentions:
														raise ValueError('''Cannot output attentions for timm backbones at the moment'''							)

										if output_hidden_states:
														# We modify the return layers to include all the stages of the backbone
														lowerCAmelCase_ :      Optional[int]    				=				self._all_layers
														lowerCAmelCase_ :      List[str]    				=				self._backbone(__lowercase   ,						**__lowercase							)
														lowerCAmelCase_ :      Optional[Any]    				=				self._return_layers
														lowerCAmelCase_ :      Dict    				=				tuple(hidden_states[i] for i in self.out_indices							)
										else:
														lowerCAmelCase_ :      Optional[Any]    				=				self._backbone(__lowercase   ,						**__lowercase							)
														lowerCAmelCase_ :      int    				=				None

										lowerCAmelCase_ :      Optional[Any]    				=				tuple(__lowercase							)
										lowerCAmelCase_ :      Tuple    				=				tuple(__lowercase							) if hidden_states is not None else None

										if not return_dict:
														lowerCAmelCase_ :      List[Any]    				=				(feature_maps,)
														if output_hidden_states:
																		lowerCAmelCase_ :      Any    				=				output + (hidden_states,)
														return output

										return BackboneOutput(feature_maps=__lowercase   ,						hidden_states=__lowercase   ,						attentions=__lowercase							)



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""")