code: 
  
  
  import json
  import os
  import random
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers import BertTokenizer, BertTokenizerFast
  from transformers.models.bert.tokenization_bert 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 FlavaImageProcessor, FlavaProcessor
     from transformers.models.flava.image_processing_flava import (
         FLAVA_CODEBOOK_MEAN,
         FLAVA_CODEBOOK_STD,
         FLAVA_IMAGE_MEAN,
         FLAVA_IMAGE_STD,
     )
  
  
  @require_vision
  class 		_SCREAMING_SNAKE_CASE    (	unittest.TestCase    ):
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       List[str]       =    tempfile.mkdtemp()
  
         # fmt: off
         _UpperCAmelCase		:       List[str]       =    ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
         # fmt: on
         _UpperCAmelCase		:       Optional[Any]       =    os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES["""vocab_file"""]						)
  
         with open(self.vocab_file       ,				"""w"""       ,				encoding="""utf-8"""						) as fp:
           fp.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)
  
         _UpperCAmelCase		:       List[Any]       =    {
             """image_mean""": FLAVA_IMAGE_MEAN,
             """image_std""": FLAVA_IMAGE_STD,
             """do_normalize""": True,
             """do_resize""": True,
             """size""": 2_24,
             """do_center_crop""": True,
             """crop_size""": 2_24,
             """input_size_patches""": 14,
             """total_mask_patches""": 75,
             """mask_group_max_patches""": None,
             """mask_group_min_patches""": 16,
             """mask_group_min_aspect_ratio""": 0.3,
             """mask_group_max_aspect_ratio""": None,
             """codebook_do_resize""": True,
             """codebook_size""": 1_12,
             """codebook_do_center_crop""": True,
             """codebook_crop_size""": 1_12,
             """codebook_do_map_pixels""": True,
             """codebook_do_normalize""": True,
             """codebook_image_mean""": FLAVA_CODEBOOK_MEAN,
             """codebook_image_std""": FLAVA_CODEBOOK_STD,
         }
  
         _UpperCAmelCase		:       Any       =    os.path.join(self.tmpdirname       ,				A_						)
         with open(self.image_processor_file       ,				"""w"""       ,				encoding="""utf-8"""						) as fp:
           json.dump(A_       ,				A_						)
  
  
       def 					__snake_case(     self       ,				**A_						):
         return BertTokenizer.from_pretrained(self.tmpdirname       ,				**A_						)
  
  
       def 					__snake_case(     self       ,				**A_						):
         return BertTokenizerFast.from_pretrained(self.tmpdirname       ,				**A_						)
  
  
       def 					__snake_case(     self       ,				**A_						):
         return FlavaImageProcessor.from_pretrained(self.tmpdirname       ,				**A_						)
  
  
       def 					__snake_case(     self						):
         shutil.rmtree(self.tmpdirname						)
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Union[str, Any]       =    [np.random.randint(2_55       ,				size=(3, 30, 4_00)       ,				dtype=np.uinta						)]
  
         _UpperCAmelCase		:       Union[str, Any]       =    [Image.fromarray(np.moveaxis(A_       ,				0       ,				-1						)						) for x in image_inputs]
  
         return image_inputs
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Optional[int]       =    self.get_tokenizer()
         _UpperCAmelCase		:       Dict       =    self.get_rust_tokenizer()
         _UpperCAmelCase		:       int       =    self.get_image_processor()
  
         _UpperCAmelCase		:       int       =    FlavaProcessor(tokenizer=A_       ,				image_processor=A_						)
         processor_slow.save_pretrained(self.tmpdirname						)
         _UpperCAmelCase		:       Union[str, Any]       =    FlavaProcessor.from_pretrained(self.tmpdirname       ,				use_fast=A_						)
  
         _UpperCAmelCase		:       List[str]       =    FlavaProcessor(tokenizer=A_       ,				image_processor=A_						)
         processor_fast.save_pretrained(self.tmpdirname						)
         _UpperCAmelCase		:       Optional[Any]       =    FlavaProcessor.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       ,				A_						)
         self.assertIsInstance(processor_fast.tokenizer       ,				A_						)
  
         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       ,				A_						)
         self.assertIsInstance(processor_fast.image_processor       ,				A_						)
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Tuple       =    FlavaProcessor(tokenizer=self.get_tokenizer()       ,				image_processor=self.get_image_processor()						)
         processor.save_pretrained(self.tmpdirname						)
  
         _UpperCAmelCase		:       List[str]       =    self.get_tokenizer(bos_token="""(BOS)"""       ,				eos_token="""(EOS)"""						)
         _UpperCAmelCase		:       List[Any]       =    self.get_image_processor(do_normalize=A_       ,				padding_value=1.0						)
  
         _UpperCAmelCase		:       int       =    FlavaProcessor.from_pretrained(
             self.tmpdirname       ,				bos_token="""(BOS)"""       ,				eos_token="""(EOS)"""       ,				do_normalize=A_       ,				padding_value=1.0						)
  
         self.assertEqual(processor.tokenizer.get_vocab()       ,				tokenizer_add_kwargs.get_vocab()						)
         self.assertIsInstance(processor.tokenizer       ,				A_						)
  
         self.assertEqual(processor.image_processor.to_json_string()       ,				image_processor_add_kwargs.to_json_string()						)
         self.assertIsInstance(processor.image_processor       ,				A_						)
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       List[Any]       =    self.get_image_processor()
         _UpperCAmelCase		:       List[str]       =    self.get_tokenizer()
  
         _UpperCAmelCase		:       Optional[int]       =    FlavaProcessor(tokenizer=A_       ,				image_processor=A_						)
  
         _UpperCAmelCase		:       Tuple       =    self.prepare_image_inputs()
  
         _UpperCAmelCase		:       int       =    image_processor(A_       ,				return_tensors="""np"""						)
         _UpperCAmelCase		:       Any       =    processor(images=A_       ,				return_tensors="""np"""						)
  
         for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()       ,				input_processor[key].sum()       ,				delta=1e-2						)
  
         # With rest of the args
         random.seed(12_34						)
         _UpperCAmelCase		:       List[str]       =    image_processor(
             A_       ,				return_image_mask=A_       ,				return_codebook_pixels=A_       ,				return_tensors="""np"""						)
         random.seed(12_34						)
         _UpperCAmelCase		:       Tuple       =    processor(
             images=A_       ,				return_image_mask=A_       ,				return_codebook_pixels=A_       ,				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 					__snake_case(     self						):
         _UpperCAmelCase		:       List[Any]       =    self.get_image_processor()
         _UpperCAmelCase		:       int       =    self.get_tokenizer()
  
         _UpperCAmelCase		:       Optional[int]       =    FlavaProcessor(tokenizer=A_       ,				image_processor=A_						)
  
         _UpperCAmelCase		:       Union[str, Any]       =    """lower newer"""
  
         _UpperCAmelCase		:       Tuple       =    processor(text=A_						)
  
         _UpperCAmelCase		:       Any       =    tokenizer(A_						)
  
         for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]       ,				encoded_processor[key]						)
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       List[str]       =    self.get_image_processor()
         _UpperCAmelCase		:       int       =    self.get_tokenizer()
  
         _UpperCAmelCase		:       Optional[int]       =    FlavaProcessor(tokenizer=A_       ,				image_processor=A_						)
  
         _UpperCAmelCase		:       Union[str, Any]       =    """lower newer"""
         _UpperCAmelCase		:       List[str]       =    self.prepare_image_inputs()
  
         _UpperCAmelCase		:       Union[str, Any]       =    processor(text=A_       ,				images=A_						)
  
         self.assertListEqual(list(inputs.keys()						)       ,				["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]						)
  
         # add extra args
         _UpperCAmelCase		:       List[str]       =    processor(text=A_       ,				images=A_       ,				return_codebook_pixels=A_       ,				return_image_mask=A_						)
  
         self.assertListEqual(
             list(inputs.keys()						)       ,				[
                 """input_ids""",
                 """token_type_ids""",
                 """attention_mask""",
                 """pixel_values""",
                 """codebook_pixel_values""",
                 """bool_masked_pos""",
             ]       ,				)
  
         # test if it raises when no input is passed
         with pytest.raises(A_						):
           processor()
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Any       =    self.get_image_processor()
         _UpperCAmelCase		:       Optional[int]       =    self.get_tokenizer()
  
         _UpperCAmelCase		:       Tuple       =    FlavaProcessor(tokenizer=A_       ,				image_processor=A_						)
  
         _UpperCAmelCase		:       Any       =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
         _UpperCAmelCase		:       Optional[Any]       =    processor.batch_decode(A_						)
         _UpperCAmelCase		:       int       =    tokenizer.batch_decode(A_						)
  
         self.assertListEqual(A_       ,				A_						)
  
  
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       List[Any]       =    self.get_image_processor()
         _UpperCAmelCase		:       List[str]       =    self.get_tokenizer()
  
         _UpperCAmelCase		:       List[Any]       =    FlavaProcessor(tokenizer=A_       ,				image_processor=A_						)
  
         _UpperCAmelCase		:       Optional[Any]       =    """lower newer"""
         _UpperCAmelCase		:       str       =    self.prepare_image_inputs()
  
         _UpperCAmelCase		:       List[Any]       =    processor(text=A_       ,				images=A_						)
  
         self.assertListEqual(list(inputs.keys()						)       ,				processor.model_input_names						)
  
  
  
  
  
  
style_context: 
  
  
  import os
  
  import requests
  from bsa import BeautifulSoup
  from fake_useragent import UserAgent
  
  SCREAMING_SNAKE_CASE__				:    int         =    {'UserAgent': UserAgent().random}
  SCREAMING_SNAKE_CASE__				:    Tuple         =    'https://www.mywaifulist.moe/random'
  
  
  
  
  def       a__						(		snake_case__				:       str	,      snake_case__				:       str    ):
    _UpperCAmelCase		:       Any       =    requests.get(snake_case__	,      headers=snake_case__    )
    with open(snake_case__	,      """wb"""    ) as file:
      file.write(image.content    )
  
  
  
  
  def       a__						(		):
    _UpperCAmelCase		:       List[Any]       =    BeautifulSoup(requests.get(snake_case__	,      headers=snake_case__    ).text	,      """html.parser"""    )
    _UpperCAmelCase		:       Optional[int]       =    soup.find("""meta"""	,      attrs={"""property""": """og:title"""}    ).attrs["""content"""]
    _UpperCAmelCase		:       int       =    soup.find("""meta"""	,      attrs={"""property""": """og:image"""}    ).attrs["""content"""]
    _UpperCAmelCase		:       Any       =    soup.find("""p"""	,      id="""description"""    ).get_text()
    _UpperCAmelCase,_UpperCAmelCase		:       Dict       =    os.path.splitext(os.path.basename(snake_case__    )    )
    _UpperCAmelCase		:       Tuple       =    title.strip().replace(""" """	,      """_"""    )
    _UpperCAmelCase		:       Optional[Any]       =    f'''{image_title}{image_extension}'''
    save_image(snake_case__	,      snake_case__    )
    return (title, description, image_title)
  
  
  if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__				:    Optional[int]         =    random_anime_character()
   print(F'{title}\n\n{desc}\n\nImage saved : {image_title}')
  
  
  
  
  
  