

'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import CLIPSegProcessor, ViTImageProcessor





@require_vision
class 							_snake_case       (				unittest.TestCase  ):
   def 			__UpperCamelCase  (       self	:					Tuple							):
     SCREAMING_SNAKE_CASE:Tuple  					=    tempfile.mkdtemp()

     # fmt: off
     SCREAMING_SNAKE_CASE:int  					=    ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
     # fmt: on
     SCREAMING_SNAKE_CASE:int  					=    dict(zip(UpperCamelCase_   ,range(len(UpperCamelCase_							)							)							)							)
     SCREAMING_SNAKE_CASE:Optional[int]  					=    ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    {'''unk_token''': '''<unk>'''}

     SCREAMING_SNAKE_CASE:Optional[Any]  					=    os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES["vocab_file"]							)
     SCREAMING_SNAKE_CASE:Dict  					=    os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES["merges_file"]							)
     with open(self.vocab_file   ,"w"   ,encoding="utf-8"							) as fp:
       fp.write(json.dumps(UpperCamelCase_							) + "\n"							)
     with open(self.merges_file   ,"w"   ,encoding="utf-8"							) as fp:
       fp.write("\n".join(UpperCamelCase_							)							)

     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    {
         '''do_resize''': True,
         '''size''': 20,
         '''do_center_crop''': True,
         '''crop_size''': 18,
         '''do_normalize''': True,
         '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
         '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
     }
     SCREAMING_SNAKE_CASE:Any  					=    os.path.join(self.tmpdirname   ,UpperCamelCase_							)
     with open(self.image_processor_file   ,"w"   ,encoding="utf-8"							) as fp:
       json.dump(UpperCamelCase_   ,UpperCamelCase_							)
   def 			__UpperCamelCase  (       self	:					Any   ,**SCREAMING_SNAKE_CASE__	:					int							):
     return CLIPTokenizer.from_pretrained(self.tmpdirname   ,**UpperCamelCase_							)
   def 			__UpperCamelCase  (       self	:					List[Any]   ,**SCREAMING_SNAKE_CASE__	:					Optional[Any]							):
     return CLIPTokenizerFast.from_pretrained(self.tmpdirname   ,**UpperCamelCase_							)
   def 			__UpperCamelCase  (       self	:					Union[str, Any]   ,**SCREAMING_SNAKE_CASE__	:					Any							):
     return ViTImageProcessor.from_pretrained(self.tmpdirname   ,**UpperCamelCase_							)
   def 			__UpperCamelCase  (       self	:					List[Any]							):
     shutil.rmtree(self.tmpdirname							)
   def 			__UpperCamelCase  (       self	:					int							):
     SCREAMING_SNAKE_CASE:List[Any]  					=    [np.random.randint(255   ,size=(3, 30, 400)   ,dtype=np.uinta							)]

     SCREAMING_SNAKE_CASE:Optional[Any]  					=    [Image.fromarray(np.moveaxis(UpperCamelCase_   ,0   ,-1							)							) for x in image_inputs]

     return image_inputs
   def 			__UpperCamelCase  (       self	:					List[Any]							):
     SCREAMING_SNAKE_CASE:Optional[int]  					=    self.get_tokenizer()
     SCREAMING_SNAKE_CASE:Dict  					=    self.get_rust_tokenizer()
     SCREAMING_SNAKE_CASE:Dict  					=    self.get_image_processor()

     SCREAMING_SNAKE_CASE:Optional[Any]  					=    CLIPSegProcessor(tokenizer=UpperCamelCase_   ,image_processor=UpperCamelCase_							)
     processor_slow.save_pretrained(self.tmpdirname							)
     SCREAMING_SNAKE_CASE:str  					=    CLIPSegProcessor.from_pretrained(self.tmpdirname   ,use_fast=UpperCamelCase_							)

     SCREAMING_SNAKE_CASE:Optional[int]  					=    CLIPSegProcessor(tokenizer=UpperCamelCase_   ,image_processor=UpperCamelCase_							)
     processor_fast.save_pretrained(self.tmpdirname							)
     SCREAMING_SNAKE_CASE:Any  					=    CLIPSegProcessor.from_pretrained(self.tmpdirname							)

     self.assertEqual(processor_slow.tokenizer.get_vocab()   ,tokenizer_slow.get_vocab()							)
     self.assertEqual(processor_fast.tokenizer.get_vocab()   ,tokenizer_fast.get_vocab()							)
     self.assertEqual(tokenizer_slow.get_vocab()   ,tokenizer_fast.get_vocab()							)
     self.assertIsInstance(processor_slow.tokenizer   ,UpperCamelCase_							)
     self.assertIsInstance(processor_fast.tokenizer   ,UpperCamelCase_							)

     self.assertEqual(processor_slow.image_processor.to_json_string()   ,image_processor.to_json_string()							)
     self.assertEqual(processor_fast.image_processor.to_json_string()   ,image_processor.to_json_string()							)
     self.assertIsInstance(processor_slow.image_processor   ,UpperCamelCase_							)
     self.assertIsInstance(processor_fast.image_processor   ,UpperCamelCase_							)
   def 			__UpperCamelCase  (       self	:					Dict							):
     SCREAMING_SNAKE_CASE:List[Any]  					=    CLIPSegProcessor(tokenizer=self.get_tokenizer()   ,image_processor=self.get_image_processor()							)
     processor.save_pretrained(self.tmpdirname							)

     SCREAMING_SNAKE_CASE:Dict  					=    self.get_tokenizer(bos_token="(BOS)"   ,eos_token="(EOS)"							)
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    self.get_image_processor(do_normalize=UpperCamelCase_   ,padding_value=1.0							)

     SCREAMING_SNAKE_CASE:List[str]  					=    CLIPSegProcessor.from_pretrained(
         self.tmpdirname   ,bos_token="(BOS)"   ,eos_token="(EOS)"   ,do_normalize=UpperCamelCase_   ,padding_value=1.0							)

     self.assertEqual(processor.tokenizer.get_vocab()   ,tokenizer_add_kwargs.get_vocab()							)
     self.assertIsInstance(processor.tokenizer   ,UpperCamelCase_							)

     self.assertEqual(processor.image_processor.to_json_string()   ,image_processor_add_kwargs.to_json_string()							)
     self.assertIsInstance(processor.image_processor   ,UpperCamelCase_							)
   def 			__UpperCamelCase  (       self	:					List[Any]							):
     SCREAMING_SNAKE_CASE:Tuple  					=    self.get_image_processor()
     SCREAMING_SNAKE_CASE:str  					=    self.get_tokenizer()

     SCREAMING_SNAKE_CASE:List[Any]  					=    CLIPSegProcessor(tokenizer=UpperCamelCase_   ,image_processor=UpperCamelCase_							)

     SCREAMING_SNAKE_CASE:str  					=    self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE:Optional[int]  					=    image_processor(UpperCamelCase_   ,return_tensors="np"							)
     SCREAMING_SNAKE_CASE:Dict  					=    processor(images=UpperCamelCase_   ,return_tensors="np"							)

     for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()   ,input_processor[key].sum()   ,delta=1e-2							)
   def 			__UpperCamelCase  (       self	:					Tuple							):
     SCREAMING_SNAKE_CASE:Tuple  					=    self.get_image_processor()
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    self.get_tokenizer()

     SCREAMING_SNAKE_CASE:List[str]  					=    CLIPSegProcessor(tokenizer=UpperCamelCase_   ,image_processor=UpperCamelCase_							)

     SCREAMING_SNAKE_CASE:List[str]  					=    '''lower newer'''

     SCREAMING_SNAKE_CASE:str  					=    processor(text=UpperCamelCase_							)

     SCREAMING_SNAKE_CASE:Any  					=    tokenizer(UpperCamelCase_							)

     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]   ,encoded_processor[key]							)
   def 			__UpperCamelCase  (       self	:					Any							):
     SCREAMING_SNAKE_CASE:List[str]  					=    self.get_image_processor()
     SCREAMING_SNAKE_CASE:Tuple  					=    self.get_tokenizer()

     SCREAMING_SNAKE_CASE:List[str]  					=    CLIPSegProcessor(tokenizer=UpperCamelCase_   ,image_processor=UpperCamelCase_							)

     SCREAMING_SNAKE_CASE:Tuple  					=    '''lower newer'''
     SCREAMING_SNAKE_CASE:List[Any]  					=    self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE:List[Any]  					=    processor(text=UpperCamelCase_   ,images=UpperCamelCase_							)

     self.assertListEqual(list(inputs.keys()							)   ,["input_ids", "attention_mask", "pixel_values"]							)

     # test if it raises when no input is passed
     with pytest.raises(UpperCamelCase_							):
       processor()
   def 			__UpperCamelCase  (       self	:					Any							):
     SCREAMING_SNAKE_CASE:Any  					=    self.get_image_processor()
     SCREAMING_SNAKE_CASE:Any  					=    self.get_tokenizer()

     SCREAMING_SNAKE_CASE:Dict  					=    CLIPSegProcessor(tokenizer=UpperCamelCase_   ,image_processor=UpperCamelCase_							)

     SCREAMING_SNAKE_CASE:List[str]  					=    self.prepare_image_inputs()
     SCREAMING_SNAKE_CASE:Optional[int]  					=    self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE:List[Any]  					=    processor(images=UpperCamelCase_   ,visual_prompt=UpperCamelCase_							)

     self.assertListEqual(list(inputs.keys()							)   ,["pixel_values", "conditional_pixel_values"]							)

     # test if it raises when no input is passed
     with pytest.raises(UpperCamelCase_							):
       processor()







   def 			__UpperCamelCase  (       self	:					Optional[Any]							):
     SCREAMING_SNAKE_CASE:Dict  					=    self.get_image_processor()
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    self.get_tokenizer()

     SCREAMING_SNAKE_CASE:Optional[int]  					=    CLIPSegProcessor(tokenizer=UpperCamelCase_   ,image_processor=UpperCamelCase_							)

     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    processor.batch_decode(UpperCamelCase_							)
     SCREAMING_SNAKE_CASE:Dict  					=    tokenizer.batch_decode(UpperCamelCase_							)

     self.assertListEqual(UpperCamelCase_   ,UpperCamelCase_							)










'''simple docstring'''
import csv

import tweepy

# Twitter API credentials
__snake_case							      =     ''''''
__snake_case							      =     ''''''
__snake_case							      =     ''''''
__snake_case							      =     ''''''


def 			a      (			__a   )  ->	None:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= tweepy.OAuthHandler(__a ,					__a   )
					auth.set_access_token(__a ,					__a   )
					UpperCamelCase__   :List[str]												= tweepy.API(__a   )

					# initialize a list to hold all the tweepy Tweets
					UpperCamelCase__   :Dict												= []

					# make initial request for most recent tweets (200 is the maximum allowed count)
					UpperCamelCase__   :Tuple												= api.user_timeline(screen_name=__a ,					count=200   )

					# save most recent tweets
					alltweets.extend(__a   )

					# save the id of the oldest tweet less one
					UpperCamelCase__   :Union[str, Any]												= alltweets[-1].id - 1

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

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

										# save most recent tweets
										alltweets.extend(__a   )

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

										print(f'''...{len(__a   )} tweets downloaded so far'''   )

					# transform the tweepy tweets into a 2D array that will populate the csv
					UpperCamelCase__   :int												= [[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__   :Tuple												= csv.writer(__a   )
										writer.writerow(['''id''', '''created_at''', '''text''']   )
										writer.writerows(__a   )


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