





"""simple docstring"""





import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import MaskFormerSwinBackbone
  from transformers.models.maskformer import MaskFormerSwinModel






class    _lowerCAmelCase  :
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=1_3       ,					__UpperCAmelCase=3_2       ,					__UpperCAmelCase=2       ,					__UpperCAmelCase=3       ,					__UpperCAmelCase=1_6       ,					__UpperCAmelCase=[1, 2, 1]       ,					__UpperCAmelCase=[2, 2, 4]       ,					__UpperCAmelCase=2       ,					__UpperCAmelCase=2.0       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=0.0       ,					__UpperCAmelCase=0.0       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase="gelu"       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=0.02       ,					__UpperCAmelCase=1E-5       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=1_0       ,					__UpperCAmelCase=8       ,					__UpperCAmelCase=["stage1", "stage2", "stage3"]       ,					__UpperCAmelCase=[1, 2, 3]       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				parent
         lowerCAmelCase__     :str											=				batch_size
         lowerCAmelCase__     :List[str]											=				image_size
         lowerCAmelCase__     :Optional[int]											=				patch_size
         lowerCAmelCase__     :List[str]											=				num_channels
         lowerCAmelCase__     :Tuple											=				embed_dim
         lowerCAmelCase__     :Optional[int]											=				depths
         lowerCAmelCase__     :Optional[Any]											=				num_heads
         lowerCAmelCase__     :List[str]											=				window_size
         lowerCAmelCase__     :int											=				mlp_ratio
         lowerCAmelCase__     :Union[str, Any]											=				qkv_bias
         lowerCAmelCase__     :Any											=				hidden_dropout_prob
         lowerCAmelCase__     :str											=				attention_probs_dropout_prob
         lowerCAmelCase__     :Union[str, Any]											=				drop_path_rate
         lowerCAmelCase__     :List[Any]											=				hidden_act
         lowerCAmelCase__     :str											=				use_absolute_embeddings
         lowerCAmelCase__     :int											=				patch_norm
         lowerCAmelCase__     :Tuple											=				layer_norm_eps
         lowerCAmelCase__     :Optional[int]											=				initializer_range
         lowerCAmelCase__     :List[Any]											=				is_training
         lowerCAmelCase__     :Optional[Any]											=				scope
         lowerCAmelCase__     :int											=				use_labels
         lowerCAmelCase__     :str											=				type_sequence_label_size
         lowerCAmelCase__     :List[str]											=				encoder_stride
         lowerCAmelCase__     :Tuple											=				out_features
         lowerCAmelCase__     :Tuple											=				out_indices






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         lowerCAmelCase__     :Any											=				None
         if self.use_labels:
           lowerCAmelCase__     :Optional[Any]											=				ids_tensor([self.batch_size]       ,					self.type_sequence_label_size )

         lowerCAmelCase__     :Any											=				self.get_config()

         return config, pixel_values, labels






       def 	snake_case		(       self ):



         '''simple docstring'''

         return MaskFormerSwinConfig(
             image_size=self.image_size       ,					patch_size=self.patch_size       ,					num_channels=self.num_channels       ,					embed_dim=self.embed_dim       ,					depths=self.depths       ,					num_heads=self.num_heads       ,					window_size=self.window_size       ,					mlp_ratio=self.mlp_ratio       ,					qkv_bias=self.qkv_bias       ,					hidden_dropout_prob=self.hidden_dropout_prob       ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,					drop_path_rate=self.drop_path_rate       ,					hidden_act=self.hidden_act       ,					use_absolute_embeddings=self.use_absolute_embeddings       ,					path_norm=self.patch_norm       ,					layer_norm_eps=self.layer_norm_eps       ,					initializer_range=self.initializer_range       ,					encoder_stride=self.encoder_stride       ,					out_features=self.out_features       ,					out_indices=self.out_indices       ,					)






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				MaskFormerSwinModel(config=__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()
         lowerCAmelCase__     :int											=				model(__UpperCAmelCase )

         lowerCAmelCase__     :List[str]											=				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
         lowerCAmelCase__     :Tuple											=				int(config.embed_dim * 2 ** (len(config.depths ) - 1) )

         self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, expected_seq_len, expected_dim) )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				MaskFormerSwinBackbone(config=__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()
         lowerCAmelCase__     :int											=				model(__UpperCAmelCase )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps )       ,					len(config.out_features ) )
         self.parent.assertListEqual(list(result.feature_maps[0].shape )       ,					[1_3, 1_6, 1_6, 1_6] )

         # verify channels
         self.parent.assertEqual(len(model.channels )       ,					len(config.out_features ) )
         self.parent.assertListEqual(model.channels       ,					[1_6, 3_2, 6_4] )

         # verify ValueError
         with self.parent.assertRaises(__UpperCAmelCase ):
           lowerCAmelCase__     :List[str]											=				['stem']
           lowerCAmelCase__     :Dict											=				MaskFormerSwinBackbone(config=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				self.prepare_config_and_inputs()
         lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :List[Any]											=				config_and_inputs
         lowerCAmelCase__     :Any											=				{'pixel_values': pixel_values}
         return config, inputs_dict






@require_torch
class    _lowerCAmelCase  (	a    ,  a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Dict						    =   (
           (
               MaskFormerSwinModel,
               MaskFormerSwinBackbone,
           )
           if is_torch_available()
           else ()
       )
       __magic_name__	:List[Any]						    =   {"""feature-extraction""": MaskFormerSwinModel} if is_torch_available() else {}
       __magic_name__	:List[str]						    =   False
       __magic_name__	:Optional[Any]						    =   False
       __magic_name__	:Union[str, Any]						    =   False
       __magic_name__	:Optional[Any]						    =   False
       __magic_name__	:str						    =   False






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				MaskFormerSwinModelTester(self )
         lowerCAmelCase__     :Any											=				ConfigTester(self       ,					config_class=__UpperCAmelCase       ,					embed_dim=3_7 )






       @require_torch_multi_gpu
       @unittest.skip(
           reason=(
               '`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
               ' `nn.DataParallel`'
           ) )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       def 	snake_case		(       self ):



         '''simple docstring'''

         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()






       def 	snake_case		(       self ):



         '''simple docstring'''

         return






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*__UpperCAmelCase )






       @unittest.skip('Swin does not use inputs_embeds' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       @unittest.skip('Swin does not support feedforward chunking' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :List[Any]											=				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           lowerCAmelCase__     :str											=				model_class(__UpperCAmelCase )
           self.assertIsInstance(model.get_input_embeddings()       ,					(nn.Module) )
           lowerCAmelCase__     :Optional[Any]											=				model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(__UpperCAmelCase       ,					nn.Linear ) )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :List[Any]											=				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           lowerCAmelCase__     :Union[str, Any]											=				model_class(__UpperCAmelCase )
           lowerCAmelCase__     :List[str]											=				inspect.signature(model.forward )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           lowerCAmelCase__     :Union[str, Any]											=				[*signature.parameters.keys()]

           lowerCAmelCase__     :Union[str, Any]											=				['pixel_values']
           self.assertListEqual(arg_names[:1]       ,					__UpperCAmelCase )






       @unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       @unittest.skip(reason='MaskFormerSwin is only used as an internal backbone' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				model_class(__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()

         with torch.no_grad():
           lowerCAmelCase__     :Dict											=				model(**self._prepare_for_class(__UpperCAmelCase       ,					__UpperCAmelCase ) )

         lowerCAmelCase__     :Tuple											=				outputs.hidden_states

         lowerCAmelCase__     :Any											=				getattr(
             self.model_tester       ,					'expected_num_hidden_layers'       ,					len(self.model_tester.depths ) + 1 )
         self.assertEqual(len(__UpperCAmelCase )       ,					__UpperCAmelCase )

         # Swin has a different seq_length
         lowerCAmelCase__     :Dict											=				(
             config.patch_size
             if isinstance(config.patch_size       ,					collections.abc.Iterable )
             else (config.patch_size, config.patch_size)
         )

         lowerCAmelCase__     :Any											=				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

         self.assertListEqual(
             list(hidden_states[0].shape[-2:] )       ,					[num_patches, self.model_tester.embed_dim]       ,					)






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :str											=				self.model_tester.prepare_config_and_inputs_for_common()

         lowerCAmelCase__     :List[Any]											=				(
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size       ,					collections.abc.Iterable )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )

         for model_class in self.all_model_classes:
           lowerCAmelCase__     :Dict											=				True
           self.check_hidden_states_output(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           lowerCAmelCase__     :Dict											=				True

           self.check_hidden_states_output(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :Any											=				self.model_tester.prepare_config_and_inputs_for_common()
         lowerCAmelCase__     :Any											=				3

         lowerCAmelCase__     :List[Any]											=				(
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size       ,					collections.abc.Iterable )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )
         lowerCAmelCase__     :str											=				(
             config.patch_size
             if isinstance(config.patch_size       ,					collections.abc.Iterable )
             else (config.patch_size, config.patch_size)
         )

         lowerCAmelCase__     :Union[str, Any]											=				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         lowerCAmelCase__     :List[str]											=				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
           lowerCAmelCase__     :Tuple											=				True
           self.check_hidden_states_output(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					(padded_height, padded_width) )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           lowerCAmelCase__     :str											=				True
           self.check_hidden_states_output(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					(padded_height, padded_width) )






       @unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :List[str]											=				self.model_tester.prepare_config_and_inputs_for_common()

         def set_nan_tensor_to_zero(__UpperCAmelCase ):
           lowerCAmelCase__     :List[Any]											=				0
           return t

         def check_equivalence(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase={} ):
           with torch.no_grad():
             lowerCAmelCase__     :List[str]											=				model(**__UpperCAmelCase       ,					return_dict=__UpperCAmelCase       ,					**__UpperCAmelCase )
             lowerCAmelCase__     :Any											=				model(**__UpperCAmelCase       ,					return_dict=__UpperCAmelCase       ,					**__UpperCAmelCase ).to_tuple()

             def recursive_check(__UpperCAmelCase       ,					__UpperCAmelCase ):
               if isinstance(__UpperCAmelCase       ,					(List, Tuple) ):
                 for tuple_iterable_value, dict_iterable_value in zip(__UpperCAmelCase       ,					__UpperCAmelCase ):
                   recursive_check(__UpperCAmelCase       ,					__UpperCAmelCase )
               elif isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ):
                 for tuple_iterable_value, dict_iterable_value in zip(
                     tuple_object.values()       ,					dict_object.values() ):
                   recursive_check(__UpperCAmelCase       ,					__UpperCAmelCase )
               elif tuple_object is None:
                 return
               else:
                 self.assertTrue(
                     torch.allclose(
                         set_nan_tensor_to_zero(__UpperCAmelCase )       ,					set_nan_tensor_to_zero(__UpperCAmelCase )       ,					atol=1E-5 )       ,					msg=(
                         'Tuple and dict output are not equal. Difference:'
                         F" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"
                         F" {torch.isnan(__UpperCAmelCase ).any()} and `inf`: {torch.isinf(__UpperCAmelCase )}. Dict has"
                         F" `nan`: {torch.isnan(__UpperCAmelCase ).any()} and `inf`: {torch.isinf(__UpperCAmelCase )}."
                     )       ,					)

             recursive_check(__UpperCAmelCase       ,					__UpperCAmelCase )

         for model_class in self.all_model_classes:
           lowerCAmelCase__     :str											=				model_class(__UpperCAmelCase )
           model.to(__UpperCAmelCase )
           model.eval()

           lowerCAmelCase__     :List[str]											=				self._prepare_for_class(__UpperCAmelCase       ,					__UpperCAmelCase )
           lowerCAmelCase__     :int											=				self._prepare_for_class(__UpperCAmelCase       ,					__UpperCAmelCase )
           check_equivalence(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

           lowerCAmelCase__     :Optional[int]											=				self._prepare_for_class(__UpperCAmelCase       ,					__UpperCAmelCase       ,					return_labels=__UpperCAmelCase )
           lowerCAmelCase__     :Dict											=				self._prepare_for_class(__UpperCAmelCase       ,					__UpperCAmelCase       ,					return_labels=__UpperCAmelCase )
           check_equivalence(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

           lowerCAmelCase__     :Union[str, Any]											=				self._prepare_for_class(__UpperCAmelCase       ,					__UpperCAmelCase )
           lowerCAmelCase__     :Optional[Any]											=				self._prepare_for_class(__UpperCAmelCase       ,					__UpperCAmelCase )
           check_equivalence(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					{'output_hidden_states': True} )

           lowerCAmelCase__     :int											=				self._prepare_for_class(__UpperCAmelCase       ,					__UpperCAmelCase       ,					return_labels=__UpperCAmelCase )
           lowerCAmelCase__     :Dict											=				self._prepare_for_class(__UpperCAmelCase       ,					__UpperCAmelCase       ,					return_labels=__UpperCAmelCase )
           check_equivalence(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					{'output_hidden_states': True} )






@require_torch
class    _lowerCAmelCase  (	unittest.TestCase    ,  a			):
       """simple docstring"""




       __magic_name__	:Dict						    =   (MaskFormerSwinBackbone,) if is_torch_available() else ()
       __magic_name__	:str						    =   MaskFormerSwinConfig






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				MaskFormerSwinModelTester(self )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :List[str]											=				self.model_tester.prepare_config_and_inputs_for_common()
         lowerCAmelCase__     :Optional[Any]											=				inputs_dict['pixel_values'].shape[0]

         for backbone_class in self.all_model_classes:
           lowerCAmelCase__     :Optional[int]											=				backbone_class(__UpperCAmelCase )
           backbone.to(__UpperCAmelCase )
           backbone.eval()

           lowerCAmelCase__     :List[str]											=				backbone(**__UpperCAmelCase )

           # Test default outputs and verify feature maps
           self.assertIsInstance(outputs.feature_maps       ,					__UpperCAmelCase )
           self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
           for feature_map, n_channels in zip(outputs.feature_maps       ,					backbone.channels ):
             self.assertTrue(feature_map.shape[:2]       ,					(batch_size, n_channels) )
           self.assertIsNone(outputs.hidden_states )
           self.assertIsNone(outputs.attentions )

           # Test output_hidden_states=True
           lowerCAmelCase__     :Any											=				backbone(**__UpperCAmelCase       ,					output_hidden_states=__UpperCAmelCase )
           self.assertIsNotNone(outputs.hidden_states )
           self.assertTrue(len(outputs.hidden_states )       ,					len(backbone.stage_names ) )
           # We skip the stem layer
           for hidden_states, n_channels in zip(outputs.hidden_states[1:]       ,					backbone.channels ):
             for hidden_state in hidden_states:
               # Hidden states are in the format (batch_size, (height * width), n_channels)
               lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :Tuple											=				hidden_state.shape
               self.assertTrue((h_batch_size, h_n_channels)       ,					(batch_size, n_channels) )

            # Test output_attentions=True
           if self.has_attentions:
             lowerCAmelCase__     :Tuple											=				backbone(**__UpperCAmelCase       ,					output_attentions=__UpperCAmelCase )
             self.assertIsNotNone(outputs.attentions )







"""simple docstring"""





import csv

import tweepy

# Twitter API credentials
__A											=						""""""
__A											=						""""""
__A											=						""""""
__A											=						""""""
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->None:





  """simple docstring"""


  lowerCAmelCase__     :Any											=				tweepy.OAuthHandler(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  auth.set_access_token(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :List[Any]											=				tweepy.API(_SCREAMING_SNAKE_CASE     )

  # initialize a list to hold all the tweepy Tweets
  lowerCAmelCase__     :Union[str, Any]											=				[]

  # make initial request for most recent tweets (200 is the maximum allowed count)
  lowerCAmelCase__     :Optional[Any]											=				api.user_timeline(screen_name=_SCREAMING_SNAKE_CASE	,  count=200     )

  # save most recent tweets
  alltweets.extend(_SCREAMING_SNAKE_CASE     )

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

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

    # all subsequent requests use the max_id param to prevent duplicates
    lowerCAmelCase__     :Union[str, Any]											=				api.user_timeline(
        screen_name=_SCREAMING_SNAKE_CASE	,  count=200	,  max_id=_SCREAMING_SNAKE_CASE     )

    # save most recent tweets
    alltweets.extend(_SCREAMING_SNAKE_CASE     )

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

    print(F"...{len(_SCREAMING_SNAKE_CASE     )} tweets downloaded so far"     )

  # transform the tweepy tweets into a 2D array that will populate the csv
  lowerCAmelCase__     :Optional[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__     :List[str]											=				csv.writer(_SCREAMING_SNAKE_CASE     )
    writer.writerow(['id', 'created_at', 'text']     )
    writer.writerows(_SCREAMING_SNAKE_CASE     )


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