





"""simple docstring"""


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


lowerCamelCase__				  =   logging.get_logger(__name__)

lowerCamelCase__				  =   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase__				  =   {
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase__				  =   {
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}



class      __SCREAMING_SNAKE_CASE    (						snake_case_   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple	     =					VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__							:str	     =					PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__							:List[Any]	     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__							:List[str]	     =					["""input_ids""", """attention_mask"""]
  SCREAMING_SNAKE_CASE__							:Dict	     =					GPTaTokenizer

  def __init__(		self   :       List[Any]					,   __a   :       int=None					,   __a   :       Any=None					,   __a   :       Tuple=None					,   __a   :       Optional[int]="<|endoftext|>"					,   __a   :       str="<|endoftext|>"					,   __a   :       List[str]="<|endoftext|>"					,   __a   :       Optional[Any]=False					,   **__a   :       Optional[int]					,   )		->				Optional[int]:
        super().__init__(
            __a					,   __a					,   tokenizer_file=__a					,   unk_token=__a					,   bos_token=__a					,   eos_token=__a					,   add_prefix_space=__a					,   **__a					,   )

        _UpperCamelCase      :	Union[str, Any]						=       kwargs.pop("add_bos_token"					,   __a	)

        _UpperCamelCase      :	Optional[int]						=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
        if pre_tok_state.get("add_prefix_space"					,   __a	) != add_prefix_space:
              _UpperCamelCase      :	Optional[Any]						=       getattr(__a					,   pre_tok_state.pop("type"	)	)
              _UpperCamelCase      :	Optional[Any]						=       add_prefix_space
              _UpperCamelCase      :	Optional[int]						=       pre_tok_class(**__a	)

        _UpperCamelCase      :	Optional[int]						=       add_prefix_space

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   *__a   :       Union[str, Any]					,   **__a   :       Any	)		->				Union[str, Any]:
        _UpperCamelCase      :	int						=       kwargs.get("is_split_into_words"					,   __a	)
        assert self.add_prefix_space or not is_split_into_words, (
            F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*__a					,   **__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   *__a   :       List[Any]					,   **__a   :       Optional[Any]	)		->				Dict:
        _UpperCamelCase      :	List[Any]						=       kwargs.get("is_split_into_words"					,   __a	)

        assert self.add_prefix_space or not is_split_into_words, (
            F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*__a					,   **__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __a   :       str					,   __a   :       Any = None	)		->				Tuple:
        _UpperCamelCase      :	Optional[int]						=       self._tokenizer.model.save(__a					,   name=__a	)
        return tuple(__a	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       Optional[Any]	)		->				List[Any]:
        _UpperCamelCase      :	List[Any]						=       []
        for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(__a					,   add_special_tokens=__a	) + [self.eos_token_id]	)

        if len(__a	) > self.model_max_length:
              _UpperCamelCase      :	Optional[Any]						=       input_ids[-self.model_max_length :]
        return input_ids







"""simple docstring"""


from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
  lowerCamelCase__				  =   input("Enter image url: ").strip()
  print(f"""Downloading image from {url} ...""")
  lowerCamelCase__				  =   BeautifulSoup(requests.get(url).content, "html.parser")
  # The image URL is in the content field of the first meta tag with property og:image
  lowerCamelCase__				  =   soup.find("meta", {"property": "og:image"})["content"]
  lowerCamelCase__				  =   requests.get(image_url).content
  lowerCamelCase__				  =   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}.""")
