




import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
    import jax.numpy as jnp

if is_tf_available():
    import tensorflow as tf

if is_torch_available():
    import torch




class       _A				(		unittest.TestCase     ):






  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			{
            """task_specific_params""": {
                """summarization""": {"""length_penalty""": 1.0, """max_length""": 128, """min_length""": 12, """num_beams""": 4},
                """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 142, """min_length""": 56, """num_beams""": 4},
                """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
            }
        }
        _UpperCAmelCase      =			{
            """task_specific_params.summarization.length_penalty""": 1.0,
            """task_specific_params.summarization.max_length""": 128,
            """task_specific_params.summarization.min_length""": 12,
            """task_specific_params.summarization.num_beams""": 4,
            """task_specific_params.summarization_cnn.length_penalty""": 2.0,
            """task_specific_params.summarization_cnn.max_length""": 142,
            """task_specific_params.summarization_cnn.min_length""": 56,
            """task_specific_params.summarization_cnn.num_beams""": 4,
            """task_specific_params.summarization_xsum.length_penalty""": 1.0,
            """task_specific_params.summarization_xsum.max_length""": 62,
            """task_specific_params.summarization_xsum.min_length""": 11,
            """task_specific_params.summarization_xsum.num_beams""": 6,
        }

        self.assertEqual(flatten_dict(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE			)






  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			np.random.randn(3						,		4			)
        self.assertTrue(np.allclose(transpose(_SCREAMING_SNAKE_CASE			)						,		x.transpose()			)			)

        _UpperCAmelCase      =			np.random.randn(3						,		4						,		5			)
        self.assertTrue(np.allclose(transpose(_SCREAMING_SNAKE_CASE						,		axes=(1, 2, 0)			)						,		x.transpose((1, 2, 0)			)			)			)






  @require_torch
  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			np.random.randn(3						,		4			)
        _UpperCAmelCase      =			torch.tensor(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(transpose(_SCREAMING_SNAKE_CASE			)						,		transpose(_SCREAMING_SNAKE_CASE			).numpy()			)			)

        _UpperCAmelCase      =			np.random.randn(3						,		4						,		5			)
        _UpperCAmelCase      =			torch.tensor(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(transpose(_SCREAMING_SNAKE_CASE						,		axes=(1, 2, 0)			)						,		transpose(_SCREAMING_SNAKE_CASE						,		axes=(1, 2, 0)			).numpy()			)			)






  @require_tf
  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			np.random.randn(3						,		4			)
        _UpperCAmelCase      =			tf.constant(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(transpose(_SCREAMING_SNAKE_CASE			)						,		transpose(_SCREAMING_SNAKE_CASE			).numpy()			)			)

        _UpperCAmelCase      =			np.random.randn(3						,		4						,		5			)
        _UpperCAmelCase      =			tf.constant(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(transpose(_SCREAMING_SNAKE_CASE						,		axes=(1, 2, 0)			)						,		transpose(_SCREAMING_SNAKE_CASE						,		axes=(1, 2, 0)			).numpy()			)			)






  @require_flax
  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			np.random.randn(3						,		4			)
        _UpperCAmelCase      =			jnp.array(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(transpose(_SCREAMING_SNAKE_CASE			)						,		np.asarray(transpose(_SCREAMING_SNAKE_CASE			)			)			)			)

        _UpperCAmelCase      =			np.random.randn(3						,		4						,		5			)
        _UpperCAmelCase      =			jnp.array(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(transpose(_SCREAMING_SNAKE_CASE						,		axes=(1, 2, 0)			)						,		np.asarray(transpose(_SCREAMING_SNAKE_CASE						,		axes=(1, 2, 0)			)			)			)			)






  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			np.random.randn(3						,		4			)
        self.assertTrue(np.allclose(reshape(_SCREAMING_SNAKE_CASE						,		(4, 3)			)						,		np.reshape(_SCREAMING_SNAKE_CASE						,		(4, 3)			)			)			)

        _UpperCAmelCase      =			np.random.randn(3						,		4						,		5			)
        self.assertTrue(np.allclose(reshape(_SCREAMING_SNAKE_CASE						,		(12, 5)			)						,		np.reshape(_SCREAMING_SNAKE_CASE						,		(12, 5)			)			)			)






  @require_torch
  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			np.random.randn(3						,		4			)
        _UpperCAmelCase      =			torch.tensor(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(reshape(_SCREAMING_SNAKE_CASE						,		(4, 3)			)						,		reshape(_SCREAMING_SNAKE_CASE						,		(4, 3)			).numpy()			)			)

        _UpperCAmelCase      =			np.random.randn(3						,		4						,		5			)
        _UpperCAmelCase      =			torch.tensor(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(reshape(_SCREAMING_SNAKE_CASE						,		(12, 5)			)						,		reshape(_SCREAMING_SNAKE_CASE						,		(12, 5)			).numpy()			)			)






  @require_tf
  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			np.random.randn(3						,		4			)
        _UpperCAmelCase      =			tf.constant(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(reshape(_SCREAMING_SNAKE_CASE						,		(4, 3)			)						,		reshape(_SCREAMING_SNAKE_CASE						,		(4, 3)			).numpy()			)			)

        _UpperCAmelCase      =			np.random.randn(3						,		4						,		5			)
        _UpperCAmelCase      =			tf.constant(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(reshape(_SCREAMING_SNAKE_CASE						,		(12, 5)			)						,		reshape(_SCREAMING_SNAKE_CASE						,		(12, 5)			).numpy()			)			)






  @require_flax
  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			np.random.randn(3						,		4			)
        _UpperCAmelCase      =			jnp.array(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(reshape(_SCREAMING_SNAKE_CASE						,		(4, 3)			)						,		np.asarray(reshape(_SCREAMING_SNAKE_CASE						,		(4, 3)			)			)			)			)

        _UpperCAmelCase      =			np.random.randn(3						,		4						,		5			)
        _UpperCAmelCase      =			jnp.array(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(reshape(_SCREAMING_SNAKE_CASE						,		(12, 5)			)						,		np.asarray(reshape(_SCREAMING_SNAKE_CASE						,		(12, 5)			)			)			)			)






  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			np.random.randn(1						,		3						,		4			)
        self.assertTrue(np.allclose(squeeze(_SCREAMING_SNAKE_CASE			)						,		np.squeeze(_SCREAMING_SNAKE_CASE			)			)			)

        _UpperCAmelCase      =			np.random.randn(1						,		4						,		1						,		5			)
        self.assertTrue(np.allclose(squeeze(_SCREAMING_SNAKE_CASE						,		axis=2			)						,		np.squeeze(_SCREAMING_SNAKE_CASE						,		axis=2			)			)			)






  @require_torch
  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			np.random.randn(1						,		3						,		4			)
        _UpperCAmelCase      =			torch.tensor(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(squeeze(_SCREAMING_SNAKE_CASE			)						,		squeeze(_SCREAMING_SNAKE_CASE			).numpy()			)			)

        _UpperCAmelCase      =			np.random.randn(1						,		4						,		1						,		5			)
        _UpperCAmelCase      =			torch.tensor(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(squeeze(_SCREAMING_SNAKE_CASE						,		axis=2			)						,		squeeze(_SCREAMING_SNAKE_CASE						,		axis=2			).numpy()			)			)






  @require_tf
  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			np.random.randn(1						,		3						,		4			)
        _UpperCAmelCase      =			tf.constant(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(squeeze(_SCREAMING_SNAKE_CASE			)						,		squeeze(_SCREAMING_SNAKE_CASE			).numpy()			)			)

        _UpperCAmelCase      =			np.random.randn(1						,		4						,		1						,		5			)
        _UpperCAmelCase      =			tf.constant(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(squeeze(_SCREAMING_SNAKE_CASE						,		axis=2			)						,		squeeze(_SCREAMING_SNAKE_CASE						,		axis=2			).numpy()			)			)






  @require_flax
  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			np.random.randn(1						,		3						,		4			)
        _UpperCAmelCase      =			jnp.array(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(squeeze(_SCREAMING_SNAKE_CASE			)						,		np.asarray(squeeze(_SCREAMING_SNAKE_CASE			)			)			)			)

        _UpperCAmelCase      =			np.random.randn(1						,		4						,		1						,		5			)
        _UpperCAmelCase      =			jnp.array(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(squeeze(_SCREAMING_SNAKE_CASE						,		axis=2			)						,		np.asarray(squeeze(_SCREAMING_SNAKE_CASE						,		axis=2			)			)			)			)






  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			np.random.randn(3						,		4			)
        self.assertTrue(np.allclose(expand_dims(_SCREAMING_SNAKE_CASE						,		axis=1			)						,		np.expand_dims(_SCREAMING_SNAKE_CASE						,		axis=1			)			)			)






  @require_torch
  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			np.random.randn(3						,		4			)
        _UpperCAmelCase      =			torch.tensor(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(expand_dims(_SCREAMING_SNAKE_CASE						,		axis=1			)						,		expand_dims(_SCREAMING_SNAKE_CASE						,		axis=1			).numpy()			)			)






  @require_tf
  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			np.random.randn(3						,		4			)
        _UpperCAmelCase      =			tf.constant(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(expand_dims(_SCREAMING_SNAKE_CASE						,		axis=1			)						,		expand_dims(_SCREAMING_SNAKE_CASE						,		axis=1			).numpy()			)			)






  @require_flax
  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			np.random.randn(3						,		4			)
        _UpperCAmelCase      =			jnp.array(_SCREAMING_SNAKE_CASE			)
        self.assertTrue(np.allclose(expand_dims(_SCREAMING_SNAKE_CASE						,		axis=1			)						,		np.asarray(expand_dims(_SCREAMING_SNAKE_CASE						,		axis=1			)			)			)			)





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")