




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


__A						:							List[str]							=  logging.get_logger(__name__)

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

__A						:							Optional[Any]							=  {
    "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",
    },
}

__A						:							List[Any]							=  {
    "gpt2": 1_0_2_4,
    "gpt2-medium": 1_0_2_4,
    "gpt2-large": 1_0_2_4,
    "gpt2-xl": 1_0_2_4,
    "distilgpt2": 1_0_2_4,
}




class 			UpperCAmelCase_      (   __UpperCAmelCase       ):




							'''simple docstring'''



							a__						  =     VOCAB_FILES_NAMES
							a__						  =     PRETRAINED_VOCAB_FILES_MAP
							a__						  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__						  =     ['''input_ids''', '''attention_mask''']
							a__						  =     GPTaTokenizer





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

										SCREAMING_SNAKE_CASE           =					kwargs.pop("""add_bos_token"""       ,     lowerCAmelCase_			)

										SCREAMING_SNAKE_CASE           =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
										if pre_tok_state.get("""add_prefix_space"""       ,     lowerCAmelCase_			) != add_prefix_space:
													SCREAMING_SNAKE_CASE           =					getattr(lowerCAmelCase_       ,     pre_tok_state.pop("""type"""			)			)
													SCREAMING_SNAKE_CASE           =					add_prefix_space
													SCREAMING_SNAKE_CASE           =					pre_tok_class(**lowerCAmelCase_			)

										SCREAMING_SNAKE_CASE           =					add_prefix_space





							def    _UpperCAmelCase     (					self       :	int       ,     *a       :	Optional[Any]       ,     **a       :	Optional[Any]			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					kwargs.get("""is_split_into_words"""       ,     lowerCAmelCase_			)
										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(*lowerCAmelCase_       ,     **lowerCAmelCase_			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     *a       :	str       ,     **a       :	Optional[int]			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					kwargs.get("""is_split_into_words"""       ,     lowerCAmelCase_			)

										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(*lowerCAmelCase_       ,     **lowerCAmelCase_			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str       ,     a       :	Optional[str] = None			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					self._tokenizer.model.save(lowerCAmelCase_       ,     name=lowerCAmelCase_			)
										return tuple(lowerCAmelCase_			)







							def    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	"Conversation"			)			->					int:
										SCREAMING_SNAKE_CASE           =					[]
										for is_user, text in conversation.iter_texts():
													input_ids.extend(self.encode(lowerCAmelCase_       ,     add_special_tokens=lowerCAmelCase_			) + [self.eos_token_id]			)

										if len(lowerCAmelCase_			) > self.model_max_length:
													SCREAMING_SNAKE_CASE           =					input_ids[-self.model_max_length :]
										return input_ids












import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

__A						:							int							=  {
    """User-Agent""": """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
    """ (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"""
}
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE = "dhaka"					,	SCREAMING_SNAKE_CASE = 5   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					min(SCREAMING_SNAKE_CASE					,	50   )  # Prevent abuse!
			SCREAMING_SNAKE_CASE           =					{
			    """q""": query,
			    """tbm""": """isch""",
			    """hl""": """en""",
			    """ijn""": """0""",
			}

			SCREAMING_SNAKE_CASE           =					requests.get("""https://www.google.com/search"""					,	params=SCREAMING_SNAKE_CASE					,	headers=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					BeautifulSoup(html.text					,	"""html.parser"""   )
			SCREAMING_SNAKE_CASE           =					"""""".join(
			    re.findall(r"""AF_initDataCallback\(([^<]+)\);"""					,	str(soup.select("""script"""   )   )   )   )

			SCREAMING_SNAKE_CASE           =					json.dumps(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					json.loads(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					re.findall(
			    r"""\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","""					,	SCREAMING_SNAKE_CASE					,	)
			if not matched_google_image_data:
						return 0

			SCREAMING_SNAKE_CASE           =					re.sub(
			    r"""\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]"""					,	""""""					,	str(SCREAMING_SNAKE_CASE   )					,	)

			SCREAMING_SNAKE_CASE           =					re.findall(
			    r"""(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"""					,	SCREAMING_SNAKE_CASE					,	)
			for index, fixed_full_res_image in enumerate(SCREAMING_SNAKE_CASE   ):
						if index >= max_images:
									return index
						SCREAMING_SNAKE_CASE           =					bytes(SCREAMING_SNAKE_CASE					,	"""ascii"""   ).decode(
						    """unicode-escape"""   )
						SCREAMING_SNAKE_CASE           =					bytes(SCREAMING_SNAKE_CASE					,	"""ascii"""   ).decode(
						    """unicode-escape"""   )
						SCREAMING_SNAKE_CASE           =					urllib.request.build_opener()
						SCREAMING_SNAKE_CASE           =					[
						    (
						        """User-Agent""",
						        """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
						        """ (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582""",
						    )
						]
						urllib.request.install_opener(SCREAMING_SNAKE_CASE   )
						SCREAMING_SNAKE_CASE           =					f"""query_{query.replace(" "					,	"_"   )}"""
						if not os.path.exists(SCREAMING_SNAKE_CASE   ):
									os.makedirs(SCREAMING_SNAKE_CASE   )
						urllib.request.urlretrieve(  # noqa: S310
						    SCREAMING_SNAKE_CASE					,	f"""{path_name}/original_size_img_{index}.jpg"""   )
			return index


if __name__ == "__main__":
		try:
				__A						:							List[Any]							=  download_images_from_google_query(sys.argv[1])
				print(f'''{image_count} images were downloaded to disk.''')
		except IndexError:
				print("""Please provide a search term.""")
				raise






