






"""simple docstring"""
import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
			from transformers import MarkupLMFeatureExtractor







class      UpperCamelCase_			(unittest.TestCase	):





	def __init__( self   :					str      ,      lowerCAmelCase_   :					Any		)		->     Tuple:
				UpperCAmelCase_	:					List[str]     	=   parent





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Optional[Any]:
				return {}





def 							snake_case (  ):
			UpperCAmelCase_	:					Dict     	=   "<HTML>\n\n    <HEAD>\n    <TITLE>sample document</TITLE>\n    </HEAD>\n\n    <BODY BGCOLOR=\"FFFFFF\">\n    <HR>\n    <a href=\"http://google.com\">Goog</a>\n    <H1>This is one header</H1>\n    <H2>This is a another Header</H2>\n    <P>Travel from\n        <P>\n        <B>SFO to JFK</B>\n        <BR>\n        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n        <HR>\n        <div style=\"color:#0000FF\">\n            <h3>Traveler <b> name </b> is\n            <p> John Doe </p>\n        </div>"

			UpperCAmelCase_	:					List[Any]     	=   "\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    "

			return [html_string_a, html_string_a]







@require_bsa
class      UpperCamelCase_			(__A							,		unittest.TestCase	):
	__magic_name__       =							MarkupLMFeatureExtractor if is_bsa_available() else None





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     Optional[int]:
				UpperCAmelCase_	:					List[str]     	=   MarkupLMFeatureExtractionTester(self		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     Optional[Any]:
				return self.feature_extract_tester.prepare_feat_extract_dict()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Optional[int]:
				# Initialize feature_extractor
				UpperCAmelCase_	:					Optional[Any]     	=   self.feature_extraction_class()

				# Test not batched input
				UpperCAmelCase_	:					Dict     	=   get_html_strings()[0]
				UpperCAmelCase_	:					Dict     	=   feature_extractor(lowerCAmelCase_		)

				# fmt: off
				UpperCAmelCase_	:					Union[str, Any]     	=   [["sample document", "Goog", "This is one header", "This is a another Header", "Travel from", "SFO to JFK", "on May 2, 2015 at 2:00 pm. For details go to confirm.com", "Traveler", "name", "is", "John Doe"]]
				UpperCAmelCase_	:					List[str]     	=   [["/html/head/title", "/html/body/a", "/html/body/h1", "/html/body/h2", "/html/body/p", "/html/body/p/p/b[1]", "/html/body/p/p/b[2]/i", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/b", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/p"]]
				# fmt: on

				self.assertEqual(encoding.nodes      ,      lowerCAmelCase_		)
				self.assertEqual(encoding.xpaths      ,      lowerCAmelCase_		)

				# Test batched
				UpperCAmelCase_	:					Any     	=   get_html_strings()
				UpperCAmelCase_	:					List[Any]     	=   feature_extractor(lowerCAmelCase_		)

				# fmt: off
				UpperCAmelCase_	:					List[Any]     	=   expected_nodes + [["My First Heading", "My first paragraph."]]
				UpperCAmelCase_	:					Tuple     	=   expected_xpaths + [["/html/body/h1", "/html/body/p"]]

				self.assertEqual(len(encoding.nodes		)      ,      2		)
				self.assertEqual(len(encoding.xpaths		)      ,      2		)

				self.assertEqual(encoding.nodes      ,      lowerCAmelCase_		)
				self.assertEqual(encoding.xpaths      ,      lowerCAmelCase_		)






'''simple docstring'''







import csv

import tweepy

# Twitter API credentials
__lowerCamelCase									=     ''''''
__lowerCamelCase									=     ''''''
__lowerCamelCase									=     ''''''
__lowerCamelCase									=     ''''''
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      None:
       # authorize twitter, initialize tweepy
       A_						       =   tweepy.OAuthHandler(UpperCAmelCase__,							UpperCAmelCase__					)
       auth.set_access_token(UpperCAmelCase__,							UpperCAmelCase__					)
       A_						       =   tweepy.API(UpperCAmelCase__					)

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

       # make initial request for most recent tweets (200 is the maximum allowed count)
       A_						       =   api.user_timeline(screen_name=UpperCAmelCase__,							count=2_00					)

       # save most recent tweets
       alltweets.extend(UpperCAmelCase__					)

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

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

              # all subsequent requests use the max_id param to prevent duplicates
              A_						       =   api.user_timeline(
                  screen_name=UpperCAmelCase__,							count=2_00,							max_id=UpperCAmelCase__					)

              # save most recent tweets
              alltweets.extend(UpperCAmelCase__					)

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

              print(F'''...{len(UpperCAmelCase__					)} tweets downloaded so far'''					)

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


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