



import csv

import tweepy

# Twitter API credentials
_snake_case	=						""
_snake_case	=						""
_snake_case	=						""
_snake_case	=						""






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Tuple				     =					tweepy.OAuthHandler(_lowerCamelCase       ,				_lowerCamelCase						)
							auth.set_access_token(_lowerCamelCase       ,				_lowerCamelCase						)
							_lowerCAmelCase  :							Tuple				     =					tweepy.API(_lowerCamelCase						)

							# initialize a list to hold all the tweepy Tweets
							_lowerCAmelCase  :							Any				     =					[]

							# make initial request for most recent tweets (200 is the maximum allowed count)
							_lowerCAmelCase  :							List[Any]				     =					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  :							Dict				     =					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  :							List[str]				     =					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  :							List[str]				     =					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  :							int				     =					[[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  :							Union[str, Any]				     =					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")





import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=False						):





							'''simple docstring'''




							_lowerCAmelCase  :							Dict				     =					OmegaConf.load(_lowerCamelCase						)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase						)						)						)
							return config






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=None       ,				_lowerCamelCase=None						):





							'''simple docstring'''




							if conf_path is None:
														_lowerCAmelCase  :							Union[str, Any]				     =					"./model_checkpoints/vqgan_only.yaml"
							_lowerCAmelCase  :							Tuple				     =					load_config(_lowerCamelCase       ,				display=_lowerCamelCase						)
							_lowerCAmelCase  :							str				     =					VQModel(**config.model.params						)
							if ckpt_path is None:
														_lowerCAmelCase  :							Optional[int]				     =					"./model_checkpoints/vqgan_only.pt"
							_lowerCAmelCase  :							int				     =					torch.load(_lowerCamelCase       ,				map_location=_lowerCamelCase						)
							if ".ckpt" in ckpt_path:
														_lowerCAmelCase  :							List[Any]				     =					sd["state_dict"]
							model.load_state_dict(_lowerCamelCase       ,				strict=_lowerCamelCase						)
							model.to(_lowerCamelCase						)
							del sd
							return model






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase  :							Tuple				     =					model.encode(_lowerCamelCase						)
							print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"						)
							_lowerCAmelCase  :							int				     =					model.decode(_lowerCamelCase						)
							return xrec






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=False						):





							'''simple docstring'''




							_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					string.rsplit("."       ,				1						)
							if reload:
														_lowerCAmelCase  :							Dict				     =					importlib.import_module(_lowerCamelCase						)
														importlib.reload(_lowerCamelCase						)
							return getattr(importlib.import_module(_lowerCamelCase       ,				package=_lowerCamelCase						)       ,				cls						)






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							if "target" not in config:
														raise KeyError("Expected key `target` to instantiate."						)
							return get_obj_from_str(config["target"]						)(**config.get("params"       ,				{}						)						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=True       ,				_lowerCamelCase=True						):





							'''simple docstring'''




							_lowerCAmelCase  :							str				     =					instantiate_from_config(_lowerCamelCase						)
							if sd is not None:
														model.load_state_dict(_lowerCamelCase						)
							if gpu:
														model.cuda()
							if eval_mode:
														model.eval()
							return {"model": model}






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							if ckpt:
														_lowerCAmelCase  :							Optional[int]				     =					torch.load(_lowerCamelCase       ,				map_location="cpu"						)
														_lowerCAmelCase  :							int				     =					pl_sd["global_step"]
														print(F"loaded model from global step {global_step}."						)
							else:
														_lowerCAmelCase  :							Optional[int]				     =					{"state_dict": None}
														_lowerCAmelCase  :							Any				     =					None
							_lowerCAmelCase  :							Optional[int]				     =					load_model_from_config(config.model       ,				pl_sd["state_dict"]       ,				gpu=_lowerCamelCase       ,				eval_mode=_lowerCamelCase						)["model"]
							return model, global_step
