


import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class      __lowerCamelCase       (UpperCAmelCase__							,	unittest.TestCase		):
       _lowercase											=			OpenAIGPTTokenizer
       _lowercase											=			OpenAIGPTTokenizerFast
       _lowercase											=			True
       _lowercase											=			False







       def 						snake_case_     (       self:      str ):






            '''simple docstring'''




            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            __UpperCamelCase      =       [
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """w</w>""",
                """r</w>""",
                """t</w>""",
                """lo""",
                """low""",
                """er</w>""",
                """low</w>""",
                """lowest</w>""",
                """newer</w>""",
                """wider</w>""",
                """<unk>""",
            ]
            __UpperCamelCase      =       dict(zip(A_,range(len(A_ ) ) ) )
            __UpperCamelCase      =       ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

            __UpperCamelCase      =       os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['vocab_file'] )
            __UpperCamelCase      =       os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['merges_file'] )
            with open(self.vocab_file,'w' ) as fp:
                 fp.write(json.dumps(A_ ) )
            with open(self.merges_file,'w' ) as fp:
                 fp.write('\n'.join(A_ ) )







       def 						snake_case_     (       self:      Optional[Any],A_:      str ):






            '''simple docstring'''




            return "lower newer", "lower newer"







       def 						snake_case_     (       self:      Dict ):






            '''simple docstring'''




            __UpperCamelCase      =       OpenAIGPTTokenizer(self.vocab_file,self.merges_file )

            __UpperCamelCase      =       """lower"""
            __UpperCamelCase      =       ["""low""", """er</w>"""]
            __UpperCamelCase      =       tokenizer.tokenize(A_ )
            self.assertListEqual(A_,A_ )

            __UpperCamelCase      =       tokens + ["""<unk>"""]
            __UpperCamelCase      =       [14, 15, 20]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ ),A_ )







       def 						snake_case_     (       self:      Optional[Any],A_:      int=15 ):






            '''simple docstring'''




            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
                      __UpperCamelCase      =       self.rust_tokenizer_class.from_pretrained(A_,**A_ )

                      # Simple input
                      __UpperCamelCase      =       """This is a simple input"""
                      __UpperCamelCase      =       ["""This is a simple input 1""", """This is a simple input 2"""]
                      __UpperCamelCase      =       ("""This is a simple input""", """This is a pair""")
                      __UpperCamelCase      =       [
                          ("""This is a simple input 1""", """This is a simple input 2"""),
                          ("""This is a simple pair 1""", """This is a simple pair 2"""),
                      ]

                      # Simple input tests
                      self.assertRaises(A_,tokenizer_r.encode,A_,max_length=A_,padding='max_length' )

                      # Simple input
                      self.assertRaises(A_,tokenizer_r.encode_plus,A_,max_length=A_,padding='max_length' )

                      # Simple input
                      self.assertRaises(
                          A_,tokenizer_r.batch_encode_plus,A_,max_length=A_,padding='max_length',)

                      # Pair input
                      self.assertRaises(A_,tokenizer_r.encode,A_,max_length=A_,padding='max_length' )

                      # Pair input
                      self.assertRaises(A_,tokenizer_r.encode_plus,A_,max_length=A_,padding='max_length' )

                      # Pair input
                      self.assertRaises(
                          A_,tokenizer_r.batch_encode_plus,A_,max_length=A_,padding='max_length',)







       def 						snake_case_     (       self:      List[Any] ):






            '''simple docstring'''




            pass



@require_ftfy
@require_spacy
@require_tokenizers
class      __lowerCamelCase       (UpperCAmelCase__		):
       pass






'''simple docstring'''







from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
   A__:    Union[str, Any]					    =       input('''Enter image url: ''').strip()
   print(F"Downloading image from {url} ...")
   A__:    Tuple					    =       BeautifulSoup(requests.get(url).content, '''html.parser''')
   # The image URL is in the content field of the first meta tag with property og:image
   A__:    Union[str, Any]					    =       soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
   A__:    List[Any]					    =       requests.get(image_url).content
   A__:    List[str]					    =       F"{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"
   with open(file_name, '''wb''') as fp:
      fp.write(image_data)
   print(F"Done. Image saved to disk as {file_name}.")


