
"""simple docstring"""







import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class  A__       (	_lowerCamelCase):
		A_						:       Any				   =	['image_processor', 'tokenizer']
		A_						:       Optional[Any]				   =	'AutoImageProcessor'
		A_						:       str				   =	'AutoTokenizer'

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE=None       ,		**_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Union[str, Any]									=      None
			if "feature_extractor" in kwargs:
				warnings.warn(
				    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
				    ' instead.'       ,		_SCREAMING_SNAKE_CASE       ,		)
				__lowerCAmelCase			:						Any									=      kwargs.pop('feature_extractor' )

			__lowerCAmelCase			:						str									=      image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
				raise ValueError('You need to specify an `image_processor`.' )
			if tokenizer is None:
				raise ValueError('You need to specify a `tokenizer`.' )

			super().__init__(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Any									=      self.image_processor
			__lowerCAmelCase			:						Tuple									=      False

		def __call__(      self       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			# For backward compatibility
			if self._in_target_context_manager:
				return self.current_processor(*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						List[Any]									=      kwargs.pop('images'       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Any									=      kwargs.pop('text'       ,		_SCREAMING_SNAKE_CASE )
			if len(_SCREAMING_SNAKE_CASE ) > 0:
				__lowerCAmelCase			:						Dict									=      args[0]
				__lowerCAmelCase			:						Union[str, Any]									=      args[1:]

			if images is None and text is None:
				raise ValueError('You need to specify either an `images` or `text` input to process.' )

			if images is not None:
				__lowerCAmelCase			:						Union[str, Any]									=      self.image_processor(_SCREAMING_SNAKE_CASE       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )
			if text is not None:
				__lowerCAmelCase			:						Dict									=      self.tokenizer(_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )

			if text is None:
				return inputs
			elif images is None:
				return encodings
			else:
				__lowerCAmelCase			:						Union[str, Any]									=      encodings['input_ids']
				return inputs

		def 							__lowerCamelCase							(      self       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )

		@contextmanager
		def 							__lowerCamelCase							(      self ):
			warnings.warn(
			    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
			    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
			    'your images inputs, or in a separate call.' )
			__lowerCAmelCase			:						Any									=      True
			__lowerCAmelCase			:						Dict									=      self.tokenizer
			yield
			__lowerCAmelCase			:						Optional[int]									=      self.image_processor
			__lowerCAmelCase			:						Optional[Any]									=      False

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=False       ,		_SCREAMING_SNAKE_CASE=None ):
			if added_vocab is None:
				__lowerCAmelCase			:						str									=      self.tokenizer.get_added_vocab()

			__lowerCAmelCase			:						List[Any]									=      {}

			while tokens:
				__lowerCAmelCase			:						int									=      re.search(R'<s_(.*?)>'       ,		_SCREAMING_SNAKE_CASE       ,		re.IGNORECASE )
				if start_token is None:
					break
				__lowerCAmelCase			:						Union[str, Any]									=      start_token.group(1 )
				__lowerCAmelCase			:						Tuple									=      re.search(Rf"</s_{key}>"       ,		_SCREAMING_SNAKE_CASE       ,		re.IGNORECASE )
				__lowerCAmelCase			:						str									=      start_token.group()
				if end_token is None:
					__lowerCAmelCase			:						Optional[int]									=      tokens.replace(_SCREAMING_SNAKE_CASE       ,		'' )
				else:
					__lowerCAmelCase			:						Optional[Any]									=      end_token.group()
					__lowerCAmelCase			:						Tuple									=      re.escape(_SCREAMING_SNAKE_CASE )
					__lowerCAmelCase			:						Tuple									=      re.escape(_SCREAMING_SNAKE_CASE )
					__lowerCAmelCase			:						Dict									=      re.search(f"{start_token_escaped}(.*?){end_token_escaped}"       ,		_SCREAMING_SNAKE_CASE       ,		re.IGNORECASE )
					if content is not None:
						__lowerCAmelCase			:						List[str]									=      content.group(1 ).strip()
						if r"<s_" in content and r"</s_" in content:  # non-leaf node
							__lowerCAmelCase			:						int									=      self.tokenajson(_SCREAMING_SNAKE_CASE       ,		is_inner_value=_SCREAMING_SNAKE_CASE       ,		added_vocab=_SCREAMING_SNAKE_CASE )
							if value:
								if len(_SCREAMING_SNAKE_CASE ) == 1:
									__lowerCAmelCase			:						Tuple									=      value[0]
								__lowerCAmelCase			:						Tuple									=      value
						else:  # leaf nodes
							__lowerCAmelCase			:						Any									=      []
							for leaf in content.split(R'<sep/>' ):
								__lowerCAmelCase			:						List[Any]									=      leaf.strip()
								if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
									__lowerCAmelCase			:						Dict									=      leaf[1:-2]  # for categorical special tokens
								output[key].append(_SCREAMING_SNAKE_CASE )
							if len(output[key] ) == 1:
								__lowerCAmelCase			:						str									=      output[key][0]

					__lowerCAmelCase			:						Dict									=      tokens[tokens.find(_SCREAMING_SNAKE_CASE ) + len(_SCREAMING_SNAKE_CASE ) :].strip()
					if tokens[:6] == r"<sep/>":  # non-leaf nodes
						return [output] + self.tokenajson(tokens[6:]       ,		is_inner_value=_SCREAMING_SNAKE_CASE       ,		added_vocab=_SCREAMING_SNAKE_CASE )

			if len(_SCREAMING_SNAKE_CASE ):
				return [output] if is_inner_value else output
			else:
				return [] if is_inner_value else {"text_sequence": tokens}

		@property
		def 							__lowerCamelCase							(      self ):
			warnings.warn(
			    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,		_SCREAMING_SNAKE_CASE       ,		)
			return self.image_processor_class







		@property
		def 							__lowerCamelCase							(      self ):
			warnings.warn(
			    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,		_SCREAMING_SNAKE_CASE       ,		)
			return self.image_processor
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""")