




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A						:							Optional[Any]							=  logging.get_logger(__name__)

__A						:							List[Any]							=  {
    """bigcode/gpt_bigcode-santacoder""": """https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json""",
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''gpt_bigcode'''
							a__						  =     ['''past_key_values''']
							a__						  =     {
							    '''hidden_size''': '''n_embd''',
							    '''max_position_embeddings''': '''n_positions''',
							    '''num_attention_heads''': '''n_head''',
							    '''num_hidden_layers''': '''n_layer''',
							}





							def __init__(					self       :	int       ,     a       :	List[Any]=50_257       ,     a       :	Union[str, Any]=1_024       ,     a       :	Union[str, Any]=768       ,     a       :	Dict=12       ,     a       :	List[str]=12       ,     a       :	Union[str, Any]=None       ,     a       :	List[str]="gelu_pytorch_tanh"       ,     a       :	Tuple=0.1       ,     a       :	Optional[int]=0.1       ,     a       :	Dict=0.1       ,     a       :	Tuple=1E-5       ,     a       :	Tuple=0.02       ,     a       :	Optional[Any]=True       ,     a       :	List[Any]=True       ,     a       :	Optional[Any]=50_256       ,     a       :	Optional[Any]=50_256       ,     a       :	Tuple=True       ,     a       :	Optional[Any]=True       ,     a       :	Tuple=True       ,     **a       :	Any       ,     )			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					vocab_size
										SCREAMING_SNAKE_CASE           =					n_positions
										SCREAMING_SNAKE_CASE           =					n_embd
										SCREAMING_SNAKE_CASE           =					n_layer
										SCREAMING_SNAKE_CASE           =					n_head
										SCREAMING_SNAKE_CASE           =					n_inner
										SCREAMING_SNAKE_CASE           =					activation_function
										SCREAMING_SNAKE_CASE           =					resid_pdrop
										SCREAMING_SNAKE_CASE           =					embd_pdrop
										SCREAMING_SNAKE_CASE           =					attn_pdrop
										SCREAMING_SNAKE_CASE           =					layer_norm_epsilon
										SCREAMING_SNAKE_CASE           =					initializer_range
										SCREAMING_SNAKE_CASE           =					scale_attn_weights
										SCREAMING_SNAKE_CASE           =					use_cache
										SCREAMING_SNAKE_CASE           =					attention_softmax_in_fpaa
										SCREAMING_SNAKE_CASE           =					scale_attention_softmax_in_fpaa
										SCREAMING_SNAKE_CASE           =					multi_query

										SCREAMING_SNAKE_CASE           =					bos_token_id
										SCREAMING_SNAKE_CASE           =					eos_token_id

										super().__init__(bos_token_id=a       ,     eos_token_id=a       ,     **a			)












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






