


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import LevitImageProcessor







class 				snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase=7   ,						__lowercase=3   ,						__lowercase=1_8   ,						__lowercase=3_0   ,						__lowercase=4_0_0   ,						__lowercase=True   ,						__lowercase=None   ,						__lowercase=True   ,						__lowercase=None   ,						__lowercase=True   ,						__lowercase=[0.5, 0.5, 0.5]   ,						__lowercase=[0.5, 0.5, 0.5]   ,						)					->    List[Any]:
										lowerCAmelCase_ :      int    				=				size if size is not None else {'''shortest_edge''': 1_8}
										lowerCAmelCase_ :      List[Any]    				=				crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
										lowerCAmelCase_ :      Any    				=				parent
										lowerCAmelCase_ :      List[str]    				=				batch_size
										lowerCAmelCase_ :      Dict    				=				num_channels
										lowerCAmelCase_ :      Optional[Any]    				=				image_size
										lowerCAmelCase_ :      Optional[Any]    				=				min_resolution
										lowerCAmelCase_ :      int    				=				max_resolution
										lowerCAmelCase_ :      Tuple    				=				do_resize
										lowerCAmelCase_ :      Dict    				=				size
										lowerCAmelCase_ :      Optional[Any]    				=				do_center_crop
										lowerCAmelCase_ :      Tuple    				=				crop_size
										lowerCAmelCase_ :      int    				=				do_normalize
										lowerCAmelCase_ :      int    				=				image_mean
										lowerCAmelCase_ :      Any    				=				image_std




						def 						lowercase_ (				self							)					->    List[str]:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "do_center_crop": self.do_center_crop,
										    "size": self.size,
										    "crop_size": self.crop_size,
										}







@require_torch
@require_vision
class 				snake_case__(							UpperCAmelCase__,						unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  List[str]					=							LevitImageProcessor if is_vision_available() else None




						def 						lowercase_ (				self							)					->    Dict:
										lowerCAmelCase_ :      Optional[Any]    				=				LevitImageProcessingTester(self							)




						@property
						def 						lowercase_ (				self							)					->    Union[str, Any]:
										return self.image_processor_tester.prepare_image_processor_dict()




						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_ :      Tuple    				=				self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(__lowercase   ,						'''image_mean'''							)							)
										self.assertTrue(hasattr(__lowercase   ,						'''image_std'''							)							)
										self.assertTrue(hasattr(__lowercase   ,						'''do_normalize'''							)							)
										self.assertTrue(hasattr(__lowercase   ,						'''do_resize'''							)							)
										self.assertTrue(hasattr(__lowercase   ,						'''do_center_crop'''							)							)
										self.assertTrue(hasattr(__lowercase   ,						'''size'''							)							)




						def 						lowercase_ (				self							)					->    Any:
										lowerCAmelCase_ :      List[Any]    				=				self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size   ,						{'''shortest_edge''': 1_8}							)
										self.assertEqual(image_processor.crop_size   ,						{'''height''': 1_8, '''width''': 1_8}							)

										lowerCAmelCase_ :      int    				=				self.image_processing_class.from_dict(self.image_processor_dict   ,						size=4_2   ,						crop_size=8_4							)
										self.assertEqual(image_processor.size   ,						{'''shortest_edge''': 4_2}							)
										self.assertEqual(image_processor.crop_size   ,						{'''height''': 8_4, '''width''': 8_4}							)




						def 						lowercase_ (				self							)					->    Any:
										pass




						def 						lowercase_ (				self							)					->    Tuple:
										# Initialize image_processing
										lowerCAmelCase_ :      Optional[int]    				=				self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										lowerCAmelCase_ :      Dict    				=				prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase   ,						Image.Image							)

										# Test not batched input
										lowerCAmelCase_ :      Union[str, Any]    				=				image_processing(image_inputs[0]   ,						return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,						(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )   ,						)

										# Test batched
										lowerCAmelCase_ :      List[str]    				=				image_processing(__lowercase   ,						return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )   ,						)




						def 						lowercase_ (				self							)					->    Tuple:
										# Initialize image_processing
										lowerCAmelCase_ :      Optional[int]    				=				self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										lowerCAmelCase_ :      Tuple    				=				prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=__lowercase   ,						numpify=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase   ,						np.ndarray							)

										# Test not batched input
										lowerCAmelCase_ :      Tuple    				=				image_processing(image_inputs[0]   ,						return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,						(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )   ,						)

										# Test batched
										lowerCAmelCase_ :      Union[str, Any]    				=				image_processing(__lowercase   ,						return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )   ,						)




						def 						lowercase_ (				self							)					->    int:
										# Initialize image_processing
										lowerCAmelCase_ :      Dict    				=				self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										lowerCAmelCase_ :      Optional[Any]    				=				prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=__lowercase   ,						torchify=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase   ,						torch.Tensor							)

										# Test not batched input
										lowerCAmelCase_ :      Tuple    				=				image_processing(image_inputs[0]   ,						return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,						(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )   ,						)

										# Test batched
										lowerCAmelCase_ :      Optional[Any]    				=				image_processing(__lowercase   ,						return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )   ,						)



import csv

import tweepy

# Twitter API credentials
_UpperCAmelCase			:  int     =""""""
_UpperCAmelCase			:  Optional[int]     =""""""
_UpperCAmelCase			:  Dict     =""""""
_UpperCAmelCase			:  str     =""""""



def 			lowerCAmelCase (		lowerCAmelCase_ )-> None:
				# authorize twitter, initialize tweepy
				lowerCAmelCase_ :      Optional[int]    				=				tweepy.OAuthHandler(lowerCAmelCase_ ,   lowerCAmelCase_ )
				auth.set_access_token(lowerCAmelCase_ ,   lowerCAmelCase_ )
				lowerCAmelCase_ :      Any    				=				tweepy.API(lowerCAmelCase_ )

				# initialize a list to hold all the tweepy Tweets
				lowerCAmelCase_ :      Dict    				=				[]

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