




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
    from PIL import Image




class       _A				(		lowerCAmelCase__     ):
  __a					    =       "dandelin/vilt-b32-finetuned-vqa"
  __a					    =       (
      "This is a tool that answers a question about an image. It takes an input named `image` which should be the "
      "image containing the information, as well as a `question` which should be the question in English. It "
      "returns a text that is the answer to the question."
  )
  __a					    =       "image_qa"
  __a					    =       AutoProcessor
  __a					    =       AutoModelForVisualQuestionAnswering

  __a					    =       ["image", "text"]
  __a					    =       ["text"]






  def __init__(    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
        requires_backends(self						,		["""vision"""]			)
        super().__init__(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)






  def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
        return self.pre_processor(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""			)






  def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
        with torch.no_grad():
              return self.model(**_SCREAMING_SNAKE_CASE			).logits






  def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
        _UpperCAmelCase      =			outputs.argmax(-1			).item()
        return self.model.config.idalabel[idx]





import csv

import tweepy

# Twitter API credentials
a													=			""
a													=			""
a													=			""
a													=			""




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				None:
						# authorize twitter, initialize tweepy
						_UpperCAmelCase      =			tweepy.OAuthHandler(snake_case			,     snake_case       )
						auth.set_access_token(snake_case			,     snake_case       )
						_UpperCAmelCase      =			tweepy.API(snake_case       )

						# 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=snake_case			,     count=2_0_0       )

						# save most recent tweets
						alltweets.extend(snake_case       )

						# 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(snake_case       ) > 0:
												print(f"getting tweets before {oldest}"       )

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

												# save most recent tweets
												alltweets.extend(snake_case       )

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

												print(f"...{len(snake_case       )} 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(snake_case       )
												writer.writerow(["""id""", """created_at""", """text"""]       )
												writer.writerows(snake_case       )


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