
'''simple docstring'''







import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       ["""image_processor""", """tokenizer"""]
							__lowercase      		=       """BlipImageProcessor"""
							__lowercase      		=       """AutoTokenizer"""


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								super().__init__(lowerCAmelCase_    ,   lowerCAmelCase_  )

								# add QFormer tokenizer
								_snake_case   						=		qformer_tokenizer


							def __call__(       self    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = True    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = 0    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = False    ,   lowerCAmelCase_ = True    ,   lowerCAmelCase_ = None    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								if images is None and text is None:
									raise ValueError('You have to specify at least images or text.'  )

								_snake_case   						=		BatchFeature()

								if text is not None:
									_snake_case   						=		self.tokenizer(
									    text=lowerCAmelCase_    ,   add_special_tokens=lowerCAmelCase_    ,   padding=lowerCAmelCase_    ,   truncation=lowerCAmelCase_    ,   max_length=lowerCAmelCase_    ,   stride=lowerCAmelCase_    ,   pad_to_multiple_of=lowerCAmelCase_    ,   return_attention_mask=lowerCAmelCase_    ,   return_overflowing_tokens=lowerCAmelCase_    ,   return_special_tokens_mask=lowerCAmelCase_    ,   return_offsets_mapping=lowerCAmelCase_    ,   return_token_type_ids=lowerCAmelCase_    ,   return_length=lowerCAmelCase_    ,   verbose=lowerCAmelCase_    ,   return_tensors=lowerCAmelCase_    ,   **lowerCAmelCase_    ,   )
									encoding.update(lowerCAmelCase_  )
									_snake_case   						=		self.qformer_tokenizer(
									    text=lowerCAmelCase_    ,   add_special_tokens=lowerCAmelCase_    ,   padding=lowerCAmelCase_    ,   truncation=lowerCAmelCase_    ,   max_length=lowerCAmelCase_    ,   stride=lowerCAmelCase_    ,   pad_to_multiple_of=lowerCAmelCase_    ,   return_attention_mask=lowerCAmelCase_    ,   return_overflowing_tokens=lowerCAmelCase_    ,   return_special_tokens_mask=lowerCAmelCase_    ,   return_offsets_mapping=lowerCAmelCase_    ,   return_token_type_ids=lowerCAmelCase_    ,   return_length=lowerCAmelCase_    ,   verbose=lowerCAmelCase_    ,   return_tensors=lowerCAmelCase_    ,   **lowerCAmelCase_    ,   )
									_snake_case   						=		qformer_text_encoding.pop('input_ids'  )
									_snake_case   						=		qformer_text_encoding.pop('attention_mask'  )

								if images is not None:
									_snake_case   						=		self.image_processor(lowerCAmelCase_    ,   return_tensors=lowerCAmelCase_  )
									encoding.update(lowerCAmelCase_  )

								return encoding


							def  lowerCamelCase				(       self    ,   *lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								return self.tokenizer.batch_decode(*lowerCAmelCase_    ,   **lowerCAmelCase_  )


							def  lowerCamelCase				(       self    ,   *lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								return self.tokenizer.decode(*lowerCAmelCase_    ,   **lowerCAmelCase_  )


							@property
							# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.tokenizer.model_input_names
								_snake_case   						=		self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								if os.path.isfile(lowerCAmelCase_  ):
									raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'  )
								os.makedirs(lowerCAmelCase_    ,   exist_ok=lowerCAmelCase_  )
								_snake_case   						=		os.path.join(lowerCAmelCase_    ,   'qformer_tokenizer'  )
								self.qformer_tokenizer.save_pretrained(lowerCAmelCase_  )
								return super().save_pretrained(lowerCAmelCase_    ,   **lowerCAmelCase_  )




							@classmethod
							def  lowerCamelCase				(       cls    ,   lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_    ,   subfolder='qformer_tokenizer'  )
								_snake_case   						=		cls._get_arguments_from_pretrained(lowerCAmelCase_    ,   **lowerCAmelCase_  )
								args.append(lowerCAmelCase_  )
								return cls(*lowerCAmelCase_  )


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""")