


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     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  ['image_processor', 'tokenizer']
  A__=  'BlipImageProcessor'
  A__=  'AutoTokenizer'






  def __init__(					self      :					int		,						_lowercase      :					List[Any]		,						_lowercase      :					Optional[int]		,						_lowercase      :					Dict ):




         """simple docstring"""







         super().__init__(_lowercase		,						_lowercase )

         # add QFormer tokenizer
         UpperCAmelCase__										=     qformer_tokenizer






  def __call__(					self      :					Union[str, Any]		,						_lowercase      :					ImageInput = None		,						_lowercase      :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,						_lowercase      :					bool = True		,						_lowercase      :					Union[bool, str, PaddingStrategy] = False		,						_lowercase      :					Union[bool, str, TruncationStrategy] = None		,						_lowercase      :					Optional[int] = None		,						_lowercase      :					int = 0		,						_lowercase      :					Optional[int] = None		,						_lowercase      :					Optional[bool] = None		,						_lowercase      :					bool = False		,						_lowercase      :					bool = False		,						_lowercase      :					bool = False		,						_lowercase      :					bool = False		,						_lowercase      :					bool = False		,						_lowercase      :					bool = True		,						_lowercase      :					Optional[Union[str, TensorType]] = None		,						**_lowercase      :					Union[str, Any]		,						):




         """simple docstring"""







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

         UpperCAmelCase__										=     BatchFeature()

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

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

         return encoding






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						*_lowercase      :					Dict		,						**_lowercase      :					Tuple ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						*_lowercase      :					int		,						**_lowercase      :					Union[str, Any] ):




         """simple docstring"""







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






  @property
  # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					Any		,						**_lowercase      :					int ):




         """simple docstring"""







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






  @classmethod
  def 			_UpperCAmelCase  (					cls      :					List[Any]		,						_lowercase      :					List[Any]		,						**_lowercase      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     AutoTokenizer.from_pretrained(_lowercase		,						subfolder="qformer_tokenizer" )
         UpperCAmelCase__										=     cls._get_arguments_from_pretrained(_lowercase		,						**_lowercase )
         args.append(_lowercase )
         return cls(*_lowercase )









import csv

import tweepy

# Twitter API credentials
A      = ""
A      = ""
A      = ""
A      = ""





def __UpperCAmelCase       (		__A			)						->					None:



       '''simple docstring'''





       UpperCAmelCase__										=     tweepy.OAuthHandler(__A ,						__A			)
       auth.set_access_token(__A ,						__A			)
       UpperCAmelCase__										=     tweepy.API(__A			)

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

       # make initial request for most recent tweets (200 is the maximum allowed count)
       UpperCAmelCase__										=     api.user_timeline(screen_name=__A ,						count=2_0_0			)

       # save most recent tweets
       alltweets.extend(__A			)

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

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

              # all subsequent requests use the max_id param to prevent duplicates
              UpperCAmelCase__										=     api.user_timeline(
                  screen_name=__A ,						count=2_0_0 ,						max_id=__A			)

              # save most recent tweets
              alltweets.extend(__A			)

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

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

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


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





