




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

import requests
from bsa import BeautifulSoup

lowercase__							:	List[Any]											=		{
    "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__       (						_A = "dhaka"	,      _A = 5				):







	'''simple docstring'''





	snake_case_						       =	min(_A	,      50				)  # Prevent abuse!
	snake_case_						       =	{
	    "q": query,
	    "tbm": "isch",
	    "hl": "en",
	    "ijn": "0",
	}

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

	snake_case_						       =	json.dumps(_A				)
	snake_case_						       =	json.loads(_A				)

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

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

	snake_case_						       =	re.findall(
	    R"(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"	,      _A	,      )
	for index, fixed_full_res_image in enumerate(_A				):
		if index >= max_images:
			return index
		snake_case_						       =	bytes(_A	,      "ascii"				).decode(
		    "unicode-escape"				)
		snake_case_						       =	bytes(_A	,      "ascii"				).decode(
		    "unicode-escape"				)
		snake_case_						       =	urllib.request.build_opener()
		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(_A				)
		snake_case_						       =	f"query_{query.replace(' '	,      '_'				)}"
		if not os.path.exists(_A				):
			os.makedirs(_A				)
		urllib.request.urlretrieve(  # noqa: S310
		    _A	,      f"{path_name}/original_size_img_{index}.jpg"				)
	return index


if __name__ == "__main__":
		try:
				lowercase__							:	Optional[int]											=		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






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 						UpperCAmelCase  (							UpperCAmelCase__			,  UpperCAmelCase__			,  UpperCAmelCase__     ):







						'''simple docstring'''





						@register_to_config
						def __init__(		self				:	Tuple			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	float			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	str			,      __lowercase				:	bool = False			,      ):






							"""simple docstring"""






							super().__init__()

							snake_case_						       =	nn.Embedding(__lowercase			,      __lowercase		)

							snake_case_						       =	nn.Embedding(__lowercase			,      __lowercase		)
							snake_case_						       =	False

							snake_case_						       =	nn.Dropout(p=__lowercase		)

							snake_case_						       =	TaConfig(
							    vocab_size=__lowercase			,      d_model=__lowercase			,      num_heads=__lowercase			,      d_kv=__lowercase			,      d_ff=__lowercase			,      dropout_rate=__lowercase			,      feed_forward_proj=__lowercase			,      is_decoder=__lowercase			,      is_encoder_decoder=__lowercase			,      )

							snake_case_						       =	nn.ModuleList()
							for lyr_num in range(__lowercase		):
								snake_case_						       =	TaBlock(__lowercase		)
								self.encoders.append(__lowercase		)

							snake_case_						       =	TaLayerNorm(__lowercase		)
							snake_case_						       =	nn.Dropout(p=__lowercase		)






						def 			snake_case__		(		self				:	str			,      __lowercase				:	Dict			,      __lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.token_embedder(__lowercase		)

							snake_case_						       =	encoder_input_tokens.shape[1]
							snake_case_						       =	torch.arange(__lowercase			,      device=encoder_input_tokens.device		)
							x += self.position_encoding(__lowercase		)

							snake_case_						       =	self.dropout_pre(__lowercase		)

							# inverted the attention mask
							snake_case_						       =	encoder_input_tokens.size()
							snake_case_						       =	self.get_extended_attention_mask(__lowercase			,      __lowercase		)

							for lyr in self.encoders:
								snake_case_						       =	lyr(__lowercase			,      __lowercase		)[0]
							snake_case_						       =	self.layer_norm(__lowercase		)

							return self.dropout_post(__lowercase		), encoder_inputs_mask
