


import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


_UpperCAmelCase			:  Any     =logging.getLogger(__name__)







@dataclass(frozen=UpperCAmelCase__							)
class 				snake_case__:







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  str
						SCREAMING_SNAKE_CASE__      :  str
						SCREAMING_SNAKE_CASE__      :  Optional[str]					=							None
						SCREAMING_SNAKE_CASE__      :  Optional[str]					=							None
						SCREAMING_SNAKE_CASE__      :  Optional[str]					=							None







@dataclass(frozen=UpperCAmelCase__							)
class 				snake_case__:







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  List[int]
						SCREAMING_SNAKE_CASE__      :  Optional[List[int]]					=							None
						SCREAMING_SNAKE_CASE__      :  Optional[List[int]]					=							None
						SCREAMING_SNAKE_CASE__      :  Optional[Union[int, float]]					=							None
						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							None


if is_torch_available():
						import torch
						from torch.utils.data import Dataset







						class 				snake_case__(							UpperCAmelCase__							):







												'''simple docstring'''




												SCREAMING_SNAKE_CASE__      :  List[InputFeatures]




												def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase = None   ,						__lowercase=False   ,						__lowercase = False   ,						)					->    Tuple:
																lowerCAmelCase_ :      Optional[int]    				=				hans_processors[task]()

																lowerCAmelCase_ :      str    				=				os.path.join(
																    __lowercase   ,						'''cached_{}_{}_{}_{}'''.format(
																        '''dev''' if evaluate else '''train'''   ,						tokenizer.__class__.__name__   ,						str(__lowercase							)   ,						__lowercase   ,						)   ,						)
																lowerCAmelCase_ :      Any    				=				processor.get_labels()
																if tokenizer.__class__ in (
																    RobertaTokenizer,
																    RobertaTokenizerFast,
																    XLMRobertaTokenizer,
																    BartTokenizer,
																    BartTokenizerFast,
																):
																				# HACK(label indices are swapped in RoBERTa pretrained model)
																				lowerCAmelCase_				,  lowerCAmelCase_ :      Union[str, Any]    				=				label_list[2], label_list[1]
																lowerCAmelCase_ :      Union[str, Any]    				=				label_list

																# Make sure only the first process in distributed training processes the dataset,
																# and the others will use the cache.
																lowerCAmelCase_ :      Dict    				=				cached_features_file + '''.lock'''
																with FileLock(__lowercase							):
																				if os.path.exists(__lowercase							) and not overwrite_cache:
																								logger.info(f"""Loading features from cached file {cached_features_file}"""							)
																								lowerCAmelCase_ :      Optional[int]    				=				torch.load(__lowercase							)
																				else:
																								logger.info(f"""Creating features from dataset file at {data_dir}"""							)

																								lowerCAmelCase_ :      Optional[int]    				=				(
																								    processor.get_dev_examples(__lowercase							) if evaluate else processor.get_train_examples(__lowercase							)
																								)

																								logger.info('''Training examples: %s'''   ,						len(__lowercase							)							)
																								lowerCAmelCase_ :      Any    				=				hans_convert_examples_to_features(__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)
																								logger.info('''Saving features into cached file %s'''   ,						__lowercase							)
																								torch.save(self.features   ,						__lowercase							)




												def __len__(				self							)					->    List[str]:
																return len(self.features							)




												def __getitem__(				self   ,						__lowercase							)					->    InputFeatures:
																return self.features[i]




												def 						lowercase_ (				self							)					->    List[str]:
																return self.label_list


if is_tf_available():
						import tensorflow as tf







						class 				snake_case__:







												'''simple docstring'''




												SCREAMING_SNAKE_CASE__      :  List[InputFeatures]




												def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase = 1_2_8   ,						__lowercase=False   ,						__lowercase = False   ,						)					->    Optional[Any]:
																lowerCAmelCase_ :      Dict    				=				hans_processors[task]()
																lowerCAmelCase_ :      int    				=				processor.get_labels()
																if tokenizer.__class__ in (
																    RobertaTokenizer,
																    RobertaTokenizerFast,
																    XLMRobertaTokenizer,
																    BartTokenizer,
																    BartTokenizerFast,
																):
																				# HACK(label indices are swapped in RoBERTa pretrained model)
																				lowerCAmelCase_				,  lowerCAmelCase_ :      Tuple    				=				label_list[2], label_list[1]
																lowerCAmelCase_ :      Dict    				=				label_list

																lowerCAmelCase_ :      List[str]    				=				processor.get_dev_examples(__lowercase							) if evaluate else processor.get_train_examples(__lowercase							)
																lowerCAmelCase_ :      str    				=				hans_convert_examples_to_features(__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)

																def gen():
																				for ex_index, ex in tqdm.tqdm(enumerate(self.features							)   ,						desc='''convert examples to features'''							):
																								if ex_index % 1_0_0_0_0 == 0:
																												logger.info('''Writing example %d of %d''' % (ex_index, len(__lowercase							))							)

																								yield (
																								    {
																								        "example_id": 0,
																								        "input_ids": ex.input_ids,
																								        "attention_mask": ex.attention_mask,
																								        "token_type_ids": ex.token_type_ids,
																								    },
																								    ex.label,
																								)

																lowerCAmelCase_ :      Tuple    				=				tf.data.Dataset.from_generator(
																    __lowercase   ,						(
																        {
																            '''example_id''': tf.intaa,
																            '''input_ids''': tf.intaa,
																            '''attention_mask''': tf.intaa,
																            '''token_type_ids''': tf.intaa,
																        },
																        tf.intaa,
																    )   ,						(
																        {
																            '''example_id''': tf.TensorShape([]							),
																            '''input_ids''': tf.TensorShape([None, None]							),
																            '''attention_mask''': tf.TensorShape([None, None]							),
																            '''token_type_ids''': tf.TensorShape([None, None]							),
																        },
																        tf.TensorShape([]							),
																    )   ,						)




												def 						lowercase_ (				self							)					->    Optional[int]:
																return self.dataset




												def __len__(				self							)					->    str:
																return len(self.features							)




												def __getitem__(				self   ,						__lowercase							)					->    InputFeatures:
																return self.features[i]




												def 						lowercase_ (				self							)					->    Any:
																return self.label_list







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						def 						lowercase_ (				self   ,						__lowercase							)					->    Optional[Any]:
										return self._create_examples(self._read_tsv(os.path.join(__lowercase   ,						'''heuristics_train_set.txt'''							)							)   ,						'''train'''							)




						def 						lowercase_ (				self   ,						__lowercase							)					->    Any:
										return self._create_examples(self._read_tsv(os.path.join(__lowercase   ,						'''heuristics_evaluation_set.txt'''							)							)   ,						'''dev'''							)




						def 						lowercase_ (				self							)					->    int:
										return ["contradiction", "entailment", "neutral"]




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase							)					->    List[str]:
										lowerCAmelCase_ :      Union[str, Any]    				=				[]
										for i, line in enumerate(__lowercase							):
														if i == 0:
																		continue
														lowerCAmelCase_ :      Tuple    				=				'''%s-%s''' % (set_type, line[0])
														lowerCAmelCase_ :      List[str]    				=				line[5]
														lowerCAmelCase_ :      List[Any]    				=				line[6]
														lowerCAmelCase_ :      int    				=				line[7][2:] if line[7].startswith('''ex'''							) else line[7]
														lowerCAmelCase_ :      Any    				=				line[0]
														examples.append(InputExample(guid=__lowercase   ,						text_a=__lowercase   ,						text_b=__lowercase   ,						label=__lowercase   ,						pairID=__lowercase							)							)
										return examples






def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   )-> Any:
				lowerCAmelCase_ :      Optional[Any]    				=				{label: i for i, label in enumerate(lowerCAmelCase_ )}

				lowerCAmelCase_ :      Union[str, Any]    				=				[]
				for ex_index, example in tqdm.tqdm(enumerate(lowerCAmelCase_ ) ,   desc='''convert examples to features''' ):
								if ex_index % 10_000 == 0:
												logger.info('''Writing example %d''' % (ex_index) )

								lowerCAmelCase_ :      Union[str, Any]    				=				tokenizer(
								    example.text_a ,   example.text_b ,   add_special_tokens=lowerCAmelCase_ ,   max_length=lowerCAmelCase_ ,   padding='''max_length''' ,   truncation=lowerCAmelCase_ ,   return_overflowing_tokens=lowerCAmelCase_ ,   )

								lowerCAmelCase_ :      Optional[int]    				=				label_map[example.label] if example.label in label_map else 0

								lowerCAmelCase_ :      List[str]    				=				int(example.pairID )

								features.append(InputFeatures(**lowerCAmelCase_ ,   label=lowerCAmelCase_ ,   pairID=lowerCAmelCase_ ) )

				for i, example in enumerate(examples[:5] ):
								logger.info('''*** Example ***''' )
								logger.info(f"""guid: {example}""" )
								logger.info(f"""features: {features[i]}""" )

				return features


_UpperCAmelCase			:  List[Any]     ={
    """hans""": 3,
}




_UpperCAmelCase			:  str     ={
    """hans""": HansProcessor,
}



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""")