






from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser




@dataclass
class 				__A     :





				"""simple docstring"""







				UpperCamelCase__ :      str			 =field(
				    metadata={"""help""": """The output directory where the model will be written."""}      ,     )
				UpperCamelCase__ :      str			 =field(
				    metadata={
				        """help""": (
				            """The encoder model checkpoint for weights initialization."""
				            """Don\'t set if you want to train an encoder model from scratch."""
				        )
				    }      ,     )
				UpperCamelCase__ :      str			 =field(
				    metadata={
				        """help""": (
				            """The decoder model checkpoint for weights initialization."""
				            """Don\'t set if you want to train a decoder model from scratch."""
				        )
				    }      ,     )
				UpperCamelCase__ :      Optional[str]			 =field(
				    default=_UpperCAmelCase      ,     metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""} )
				UpperCamelCase__ :      Optional[str]			 =field(
				    default=_UpperCAmelCase      ,     metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""} )






def 							A     (       )     ->						Any:
					__UpperCamelCase      :					Tuple       		=HfArgumentParser((ModelArguments,)					)
					((__UpperCamelCase     )		,				     )      :					List[str]       		=parser.parse_args_into_dataclasses()

					# Load pretrained model and tokenizer

					# Use explicit specified encoder config
					if model_args.encoder_config_name:
										__UpperCamelCase      :					Optional[int]       		=AutoConfig.from_pretrained(model_args.encoder_config_name					)
					# Use pretrained encoder model's config
					else:
										__UpperCamelCase      :					int       		=AutoConfig.from_pretrained(model_args.encoder_model_name_or_path					)

					# Use explicit specified decoder config
					if model_args.decoder_config_name:
										__UpperCamelCase      :					int       		=AutoConfig.from_pretrained(model_args.decoder_config_name					)
					# Use pretrained decoder model's config
					else:
										__UpperCamelCase      :					str       		=AutoConfig.from_pretrained(model_args.decoder_model_name_or_path					)

					# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
					__UpperCamelCase      :					List[str]       		=True
					__UpperCamelCase      :					List[str]       		=True

					__UpperCamelCase      :					Dict       		=FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
					    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path							,decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path							,encoder_config=a_							,decoder_config=a_							,)

					# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
					__UpperCamelCase      :					Union[str, Any]       		=decoder_config.decoder_start_token_id
					__UpperCamelCase      :					Any       		=decoder_config.pad_token_id
					if decoder_start_token_id is None:
										__UpperCamelCase      :					Any       		=decoder_config.bos_token_id
					if pad_token_id is None:
										__UpperCamelCase      :					List[str]       		=decoder_config.eos_token_id

					# This is necessary to make Flax's generate() work
					__UpperCamelCase      :					Optional[Any]       		=decoder_config.eos_token_id
					__UpperCamelCase      :					Tuple       		=decoder_start_token_id
					__UpperCamelCase      :					Optional[Any]       		=pad_token_id

					__UpperCamelCase      :					List[Any]       		=AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path					)

					__UpperCamelCase      :					Optional[int]       		=AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path					)
					__UpperCamelCase      :					Union[str, Any]       		=tokenizer.convert_ids_to_tokens(model.config.pad_token_id					)

					model.save_pretrained(model_args.output_dir					)
					image_processor.save_pretrained(model_args.output_dir					)
					tokenizer.save_pretrained(model_args.output_dir					)


if __name__ == "__main__":
							main()


import csv

import tweepy

# Twitter API credentials
__a  :     Union[str, Any]       =  """"""
__a  :     Union[str, Any]       =  """"""
__a  :     Union[str, Any]       =  """"""
__a  :     List[Any]       =  """"""





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							tweepy.OAuthHandler(lowercase							,			lowercase							)
				auth.set_access_token(lowercase							,			lowercase							)
				__lowercase 			=							tweepy.API(lowercase							)

				# initialize a list to hold all the tweepy Tweets
				__lowercase 			=							[]

				# make initial request for most recent tweets (200 is the maximum allowed count)
				__lowercase 			=							api.user_timeline(screen_name=lowercase							,			count=200							)

				# save most recent tweets
				alltweets.extend(lowercase							)

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

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

								# all subsequent requests use the max_id param to prevent duplicates
								__lowercase 			=							api.user_timeline(
								    screen_name=lowercase							,			count=200							,			max_id=lowercase							)

								# save most recent tweets
								alltweets.extend(lowercase							)

								# update the id of the oldest tweet less one
								__lowercase 			=							alltweets[-1].id - 1

								print(F"...{len(lowercase							)} tweets downloaded so far"							)

				# transform the tweepy tweets into a 2D array that will populate the csv
				__lowercase 			=							[[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:
								__lowercase 			=							csv.writer(lowercase							)
								writer.writerow(['''id''', '''created_at''', '''text''']							)
								writer.writerows(lowercase							)


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