


'''simple docstring'''






import csv

import tweepy

# Twitter API credentials
lowercase							=''
lowercase							=''
lowercase							=''
lowercase							=''





def lowerCamelCase__      (						__lowerCamelCase	:							str							):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =tweepy.OAuthHandler(__lowerCamelCase	,   __lowerCamelCase							)
   auth.set_access_token(__lowerCamelCase	,   __lowerCamelCase							)
   _UpperCAmelCase       :       Dict							   =tweepy.API(__lowerCamelCase							)

   # initialize a list to hold all the tweepy Tweets
   _UpperCAmelCase       :       List[Any]							   =[]

   # make initial request for most recent tweets (200 is the maximum allowed count)
   _UpperCAmelCase       :       Union[str, Any]							   =api.user_timeline(screen_name=__lowerCamelCase	,   count=2_0_0							)

   # save most recent tweets
   alltweets.extend(__lowerCamelCase							)

   # save the id of the oldest tweet less one
   _UpperCAmelCase       :       Optional[Any]							   =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
      _UpperCAmelCase       :       Tuple							   =api.user_timeline(
          screen_name=__lowerCamelCase	,   count=2_0_0	,   max_id=__lowerCamelCase							)

      # save most recent tweets
      alltweets.extend(__lowerCamelCase							)

      # update the id of the oldest tweet less one
      _UpperCAmelCase       :       List[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
   _UpperCAmelCase       :       List[str]							   =[[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       :       Any							   =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')





'''simple docstring'''






import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
     FlaxRobertaPreLayerNormForCausalLM,
     FlaxRobertaPreLayerNormForMaskedLM,
     FlaxRobertaPreLayerNormForMultipleChoice,
     FlaxRobertaPreLayerNormForQuestionAnswering,
     FlaxRobertaPreLayerNormForSequenceClassification,
     FlaxRobertaPreLayerNormForTokenClassification,
     FlaxRobertaPreLayerNormModel,
 )






class  __magic_name__							(		unittest.TestCase    ):






       def __init__(      self  ,							snake_case  ,							snake_case=1_3  ,							snake_case=7  ,							snake_case=True  ,							snake_case=True  ,							snake_case=True  ,							snake_case=True  ,							snake_case=9_9  ,							snake_case=3_2  ,							snake_case=5  ,							snake_case=4  ,							snake_case=3_7  ,							snake_case="gelu"  ,							snake_case=0.1  ,							snake_case=0.1  ,							snake_case=5_1_2  ,							snake_case=1_6  ,							snake_case=2  ,							snake_case=0.02  ,							snake_case=4  ,							)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =parent
          _UpperCAmelCase       :       Dict							   =batch_size
          _UpperCAmelCase       :       List[Any]							   =seq_length
          _UpperCAmelCase       :       List[str]							   =is_training
          _UpperCAmelCase       :       Optional[int]							   =use_attention_mask
          _UpperCAmelCase       :       Dict							   =use_token_type_ids
          _UpperCAmelCase       :       Dict							   =use_labels
          _UpperCAmelCase       :       Optional[Any]							   =vocab_size
          _UpperCAmelCase       :       str							   =hidden_size
          _UpperCAmelCase       :       Dict							   =num_hidden_layers
          _UpperCAmelCase       :       Tuple							   =num_attention_heads
          _UpperCAmelCase       :       List[str]							   =intermediate_size
          _UpperCAmelCase       :       List[str]							   =hidden_act
          _UpperCAmelCase       :       int							   =hidden_dropout_prob
          _UpperCAmelCase       :       Optional[int]							   =attention_probs_dropout_prob
          _UpperCAmelCase       :       Optional[Any]							   =max_position_embeddings
          _UpperCAmelCase       :       Union[str, Any]							   =type_vocab_size
          _UpperCAmelCase       :       Dict							   =type_sequence_label_size
          _UpperCAmelCase       :       Union[str, Any]							   =initializer_range
          _UpperCAmelCase       :       Any							   =num_choices






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size)

          _UpperCAmelCase       :       str							   =None
          if self.use_attention_mask:
             _UpperCAmelCase       :       Dict							   =random_attention_mask([self.batch_size, self.seq_length])

          _UpperCAmelCase       :       Optional[Any]							   =None
          if self.use_token_type_ids:
             _UpperCAmelCase       :       Tuple							   =ids_tensor([self.batch_size, self.seq_length]  ,							self.type_vocab_size)

          _UpperCAmelCase       :       Union[str, Any]							   =RobertaPreLayerNormConfig(
              vocab_size=self.vocab_size  ,							hidden_size=self.hidden_size  ,							num_hidden_layers=self.num_hidden_layers  ,							num_attention_heads=self.num_attention_heads  ,							intermediate_size=self.intermediate_size  ,							hidden_act=self.hidden_act  ,							hidden_dropout_prob=self.hidden_dropout_prob  ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,							max_position_embeddings=self.max_position_embeddings  ,							type_vocab_size=self.type_vocab_size  ,							is_decoder=snake_case  ,							initializer_range=self.initializer_range  ,							)

          return config, input_ids, token_type_ids, attention_mask






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =self.prepare_config_and_inputs()
          _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       str							   =config_and_inputs
          _UpperCAmelCase       :       List[Any]							   ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =self.prepare_config_and_inputs()
          _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[int]							   =config_and_inputs

          _UpperCAmelCase       :       Tuple							   =True
          _UpperCAmelCase       :       Any							   =floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          _UpperCAmelCase       :       Optional[Any]							   =ids_tensor([self.batch_size, self.seq_length]  ,							vocab_size=2)

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class  __magic_name__							(		lowerCAmelCase ,unittest.TestCase    ):
       UpperCAmelCase										=True

       UpperCAmelCase										=(
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =FlaxRobertaPreLayerNormModelTester(self)






       @slow
       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          for model_class_name in self.all_model_classes:
             _UpperCAmelCase       :       List[str]							   =model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'  ,							from_pt=snake_case)
             _UpperCAmelCase       :       Dict							   =model(np.ones((1, 1)))
             self.assertIsNotNone(snake_case)






@require_flax
class  __magic_name__							(		unittest.TestCase    ):






       @slow
       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'  ,							from_pt=snake_case)

          _UpperCAmelCase       :       Optional[int]							   =np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]  ,							dtype=jnp.intaa)
          _UpperCAmelCase       :       str							   =model(snake_case)[0]
          _UpperCAmelCase       :       int							   =[1, 1_1, 5_0_2_6_5]
          self.assertEqual(list(output.shape)  ,							snake_case)
          # compare the actual values for a slice.
          _UpperCAmelCase       :       List[str]							   =np.array(
              [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]  ,							dtype=np.floataa)
          self.assertTrue(np.allclose(output[:, :3, :3]  ,							snake_case  ,							atol=1E-4))






       @slow
       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'  ,							from_pt=snake_case)

          _UpperCAmelCase       :       List[str]							   =np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]  ,							dtype=jnp.intaa)
          _UpperCAmelCase       :       Tuple							   =model(snake_case)[0]
          # compare the actual values for a slice.
          _UpperCAmelCase       :       List[str]							   =np.array(
              [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]  ,							dtype=np.floataa)
          self.assertTrue(np.allclose(output[:, :3, :3]  ,							snake_case  ,							atol=1E-4))

