

'''simple docstring'''







import csv

import tweepy

# Twitter API credentials
__lowerCamelCase									=     ''''''
__lowerCamelCase									=     ''''''
__lowerCamelCase									=     ''''''
__lowerCamelCase									=     ''''''
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      None:
       # authorize twitter, initialize tweepy
       A_						       =   tweepy.OAuthHandler(UpperCAmelCase__,							UpperCAmelCase__					)
       auth.set_access_token(UpperCAmelCase__,							UpperCAmelCase__					)
       A_						       =   tweepy.API(UpperCAmelCase__					)

       # initialize a list to hold all the tweepy Tweets
       A_						       =   []

       # make initial request for most recent tweets (200 is the maximum allowed count)
       A_						       =   api.user_timeline(screen_name=UpperCAmelCase__,							count=2_00					)

       # save most recent tweets
       alltweets.extend(UpperCAmelCase__					)

       # save the id of the oldest tweet less one
       A_						       =   alltweets[-1].id - 1

       # keep grabbing tweets until there are no tweets left to grab
       while len(UpperCAmelCase__					) > 0:
              print(F'''getting tweets before {oldest}'''					)

              # all subsequent requests use the max_id param to prevent duplicates
              A_						       =   api.user_timeline(
                  screen_name=UpperCAmelCase__,							count=2_00,							max_id=UpperCAmelCase__					)

              # save most recent tweets
              alltweets.extend(UpperCAmelCase__					)

              # update the id of the oldest tweet less one
              A_						       =   alltweets[-1].id - 1

              print(F'''...{len(UpperCAmelCase__					)} tweets downloaded so far'''					)

       # transform the tweepy tweets into a 2D array that will populate the csv
       A_						       =   [[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:
              A_						       =   csv.writer(UpperCAmelCase__					)
              writer.writerow(["""id""", """created_at""", """text"""]					)
              writer.writerows(UpperCAmelCase__					)


if __name__ == "__main__":
      # pass in the username of the account you want to download
      get_all_tweets('''FirePing32''')



'''simple docstring'''







import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DistilBertForMaskedLM,
          DistilBertForMultipleChoice,
          DistilBertForQuestionAnswering,
          DistilBertForSequenceClassification,
          DistilBertForTokenClassification,
          DistilBertModel,
      )


class        A__   (   _snake_case      ):



    def __init__(       self		,					UpperCamelCase__		,					UpperCamelCase__=13		,					UpperCamelCase__=7		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=False		,					UpperCamelCase__=True		,					UpperCamelCase__=99		,					UpperCamelCase__=32		,					UpperCamelCase__=5		,					UpperCamelCase__=4		,					UpperCamelCase__=37		,					UpperCamelCase__="gelu"		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=16		,					UpperCamelCase__=2		,					UpperCamelCase__=0.02		,					UpperCamelCase__=3		,					UpperCamelCase__=4		,					UpperCamelCase__=None		,					)    ->   Dict:





           '''simple docstring'''
           A_						       =   parent
           A_						       =   batch_size
           A_						       =   seq_length
           A_						       =   is_training
           A_						       =   use_input_mask
           A_						       =   use_token_type_ids
           A_						       =   use_labels
           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   intermediate_size
           A_						       =   hidden_act
           A_						       =   hidden_dropout_prob
           A_						       =   attention_probs_dropout_prob
           A_						       =   max_position_embeddings
           A_						       =   type_vocab_size
           A_						       =   type_sequence_label_size
           A_						       =   initializer_range
           A_						       =   num_labels
           A_						       =   num_choices
           A_						       =   scope



    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size							)

           A_						       =   None
           if self.use_input_mask:
                  A_						       =   random_attention_mask([self.batch_size, self.seq_length]							)

           A_						       =   None
           A_						       =   None
           A_						       =   None
           if self.use_labels:
                  A_						       =   ids_tensor([self.batch_size]		,					self.type_sequence_label_size							)
                  A_						       =   ids_tensor([self.batch_size, self.seq_length]		,					self.num_labels							)
                  A_						       =   ids_tensor([self.batch_size]		,					self.num_choices							)

           A_						       =   self.get_config()

           return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



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





           '''simple docstring'''
           return DistilBertConfig(
               vocab_size=self.vocab_size		,					dim=self.hidden_size		,					n_layers=self.num_hidden_layers		,					n_heads=self.num_attention_heads		,					hidden_dim=self.intermediate_size		,					hidden_act=self.hidden_act		,					dropout=self.hidden_dropout_prob		,					attention_dropout=self.attention_probs_dropout_prob		,					max_position_embeddings=self.max_position_embeddings		,					initializer_range=self.initializer_range		,					)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Union[str, Any]:





           '''simple docstring'''
           A_						       =   DistilBertModel(config=UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()
           A_						       =   model(UpperCamelCase__		,					UpperCamelCase__							)
           A_						       =   model(UpperCamelCase__							)
           self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size)							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Union[str, Any]:





           '''simple docstring'''
           A_						       =   DistilBertForMaskedLM(config=UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()
           A_						       =   model(UpperCamelCase__		,					attention_mask=UpperCamelCase__		,					labels=UpperCamelCase__							)
           self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.vocab_size)							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Dict:





           '''simple docstring'''
           A_						       =   DistilBertForQuestionAnswering(config=UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()
           A_						       =   model(
               UpperCamelCase__		,					attention_mask=UpperCamelCase__		,					start_positions=UpperCamelCase__		,					end_positions=UpperCamelCase__							)
           self.parent.assertEqual(result.start_logits.shape		,					(self.batch_size, self.seq_length)							)
           self.parent.assertEqual(result.end_logits.shape		,					(self.batch_size, self.seq_length)							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Union[str, Any]:





           '''simple docstring'''
           A_						       =   self.num_labels
           A_						       =   DistilBertForSequenceClassification(UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()
           A_						       =   model(UpperCamelCase__		,					attention_mask=UpperCamelCase__		,					labels=UpperCamelCase__							)
           self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_labels)							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Any:





           '''simple docstring'''
           A_						       =   self.num_labels
           A_						       =   DistilBertForTokenClassification(config=UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()

           A_						       =   model(UpperCamelCase__		,					attention_mask=UpperCamelCase__		,					labels=UpperCamelCase__							)
           self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.num_labels)							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   self.num_choices
           A_						       =   DistilBertForMultipleChoice(config=UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()
           A_						       =   input_ids.unsqueeze(1							).expand(-1		,					self.num_choices		,					-1							).contiguous()
           A_						       =   input_mask.unsqueeze(1							).expand(-1		,					self.num_choices		,					-1							).contiguous()
           A_						       =   model(
               UpperCamelCase__		,					attention_mask=UpperCamelCase__		,					labels=UpperCamelCase__		,					)
           self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_choices)							)







    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   self.prepare_config_and_inputs()
           ((A_)       ,      (A_)       ,      (A_)       ,      (A_)       ,      (A_)       ,      (A_))						       =   config_and_inputs
           A_						       =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
           return config, inputs_dict


@require_torch
class        A__   (   _snake_case    ,    _snake_case    ,    unittest.TestCase      ):
    lowercase						       =		(
        (
            DistilBertModel,
            DistilBertForMaskedLM,
            DistilBertForMultipleChoice,
            DistilBertForQuestionAnswering,
            DistilBertForSequenceClassification,
            DistilBertForTokenClassification,
        )
        if is_torch_available()
        else None
    )
    lowercase						       =		(
        {
            "feature-extraction": DistilBertModel,
            "fill-mask": DistilBertForMaskedLM,
            "question-answering": DistilBertForQuestionAnswering,
            "text-classification": DistilBertForSequenceClassification,
            "token-classification": DistilBertForTokenClassification,
            "zero-shot": DistilBertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowercase						       =		True
    lowercase						       =		True
    lowercase						       =		True
    lowercase						       =		True



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   DistilBertModelTester(self							)
           A_						       =   ConfigTester(self		,					config_class=UpperCamelCase__		,					dim=37							)



    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           self.config_tester.run_common_tests()



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_model(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_masked_lm(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   Union[str, Any]:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_question_answering(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_sequence_classification(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_token_classification(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   Union[str, Any]:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_multiple_choice(*UpperCamelCase__							)



    @slow
    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_						       =   DistilBertModel.from_pretrained(UpperCamelCase__							)
                  self.assertIsNotNone(UpperCamelCase__							)







    @slow
    @require_torch_gpu
    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_       ,      A_						       =   self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                  # BertForMultipleChoice behaves incorrectly in JIT environments.
                  if model_class == DistilBertForMultipleChoice:
                         return

                  A_						       =   True
                  A_						       =   model_class(config=UpperCamelCase__							)

                  A_						       =   self._prepare_for_class(UpperCamelCase__		,					UpperCamelCase__							)
                  A_						       =   torch.jit.trace(
                      UpperCamelCase__		,					(inputs_dict["""input_ids"""].to("""cpu"""							), inputs_dict["""attention_mask"""].to("""cpu"""							))							)

                  with tempfile.TemporaryDirectory() as tmp:
                         torch.jit.save(UpperCamelCase__		,					os.path.join(UpperCamelCase__		,					"""traced_model.pt"""							)							)
                         A_						       =   torch.jit.load(os.path.join(UpperCamelCase__		,					"""traced_model.pt"""							)		,					map_location=UpperCamelCase__							)
                         loaded(inputs_dict["""input_ids"""].to(UpperCamelCase__							)		,					inputs_dict["""attention_mask"""].to(UpperCamelCase__							)							)


@require_torch
class        A__   (   unittest.TestCase      ):



    @slow
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           A_						       =   DistilBertModel.from_pretrained("""distilbert-base-uncased"""							)
           A_						       =   torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]							)
           A_						       =   torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
           with torch.no_grad():
                  A_						       =   model(UpperCamelCase__		,					attention_mask=UpperCamelCase__							)[0]
           A_						       =   torch.Size((1, 11, 768)							)
           self.assertEqual(output.shape		,					UpperCamelCase__							)
           A_						       =   torch.tensor(
               [[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]							)

           self.assertTrue(torch.allclose(output[:, 1:4, 1:4]		,					UpperCamelCase__		,					atol=1e-4							)							)
