




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


a													=			get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
				a													=			json.load(f)




@require_torch
class       _A				(		unittest.TestCase     ):






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								return FSMTTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			FSMTForConditionalGeneration.from_pretrained(_SCREAMING_SNAKE_CASE			).to(_SCREAMING_SNAKE_CASE			)
								if torch_device == "cuda":
														model.half()
								return model






		@parameterized.expand(
		    [
		        ["""en-ru""", 26.0],
		        ["""ru-en""", 22.0],
		        ["""en-de""", 22.0],
		        ["""de-en""", 29.0],
		    ]			)
		@slow
		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								# note: this test is not testing the best performance since it only evals a small batch
								# but it should be enough to detect a regression in the output quality
								_UpperCAmelCase      =			F"facebook/wmt19-{pair}"
								_UpperCAmelCase      =			self.get_tokenizer(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.get_model(_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			bleu_data[pair]["""src"""]
								_UpperCAmelCase      =			bleu_data[pair]["""tgt"""]

								_UpperCAmelCase      =			tokenizer(_SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""						,		truncation=_SCREAMING_SNAKE_CASE						,		padding="""longest"""			).to(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			model.generate(
								    input_ids=batch.input_ids						,		num_beams=8						,		)
								_UpperCAmelCase      =			tokenizer.batch_decode(
								    _SCREAMING_SNAKE_CASE						,		skip_special_tokens=_SCREAMING_SNAKE_CASE						,		clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			calculate_bleu(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								print(_SCREAMING_SNAKE_CASE			)
								self.assertGreaterEqual(scores["""bleu"""]						,		_SCREAMING_SNAKE_CASE			)





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")