


from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   None
	A_           =   None
	A_           =   None
	A_           =   None





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def __init__(       self						,    __a=1						,    __a=0						,    __a=2						,    __a=512						,    __a="cls"						,    __a=False						,    __a=True						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(pad_token_id=__a						,    bos_token_id=__a						,    eos_token_id=__a						,    **__a							)
						__a      :	Any    =  project_dim
						__a      :	Optional[Any]    =  pooler_fn
						__a      :	int    =  learn_encoder
						__a      :	str    =  use_attention_mask





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   [r"pooler", r"logit_scale"]
	A_           =   [r"position_ids", r"predictions.decoder.bias"]
	A_           =   "roberta"
	A_           =   RobertaSeriesConfig




	def __init__(       self						,    __a							):

						'''simple docstring'''





						super().__init__(__a							)
						__a      :	Optional[Any]    =  XLMRobertaModel(__a							)
						__a      :	str    =  nn.Linear(config.hidden_size						,    config.project_dim							)
						__a      :	Optional[int]    =  getattr(__a						,    'has_pre_transformation'						,    __a							)
						if self.has_pre_transformation:
											__a      :	int    =  nn.Linear(config.hidden_size						,    config.project_dim							)
											__a      :	List[str]    =  nn.LayerNorm(config.hidden_size						,    eps=config.layer_norm_eps							)
						self.post_init()





	def 				__UpperCAmelCase							(       self						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    ):

						'''simple docstring'''





						__a      :	Optional[Any]    =  return_dict if return_dict is not None else self.config.use_return_dict

						__a      :	Tuple    =  self.base_model(
						    input_ids=__a						,    attention_mask=__a						,    token_type_ids=__a						,    position_ids=__a						,    head_mask=__a						,    inputs_embeds=__a						,    encoder_hidden_states=__a						,    encoder_attention_mask=__a						,    output_attentions=__a						,    output_hidden_states=True if self.has_pre_transformation else output_hidden_states						,    return_dict=__a						,    )

						if self.has_pre_transformation:
											__a      :	Optional[Any]    =  outputs['hidden_states'][-2]
											__a      :	Optional[int]    =  self.pre_LN(__a							)
											__a      :	Union[str, Any]    =  self.transformation_pre(__a							)

											return TransformationModelOutput(
											    projection_state=__a						,    last_hidden_state=outputs.last_hidden_state						,    hidden_states=outputs.hidden_states						,    attentions=outputs.attentions						,    )
						else:
											__a      :	Optional[Any]    =  self.transformation(outputs.last_hidden_state							)
											return TransformationModelOutput(
											    projection_state=__a						,    last_hidden_state=outputs.last_hidden_state						,    hidden_states=outputs.hidden_states						,    attentions=outputs.attentions						,    )







'''simple docstring'''



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

import requests
from bsa import BeautifulSoup

__lowercase     :						str	=     {
    '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				:       str = "dhaka"						,				_SCREAMING_SNAKE_CASE				:       int = 5       ):
					__a      :	Optional[Any]    =  min(_SCREAMING_SNAKE_CASE						,				50       )  # Prevent abuse!
					__a      :	Optional[Any]    =  {
					    'q': query,
					    'tbm': 'isch',
					    'hl': 'en',
					    'ijn': '0',
					}

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

					__a      :	Optional[Any]    =  json.dumps(_SCREAMING_SNAKE_CASE       )
					__a      :	List[str]    =  json.loads(_SCREAMING_SNAKE_CASE       )

					__a      :	List[Any]    =  re.findall(
					    r'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",'						,				_SCREAMING_SNAKE_CASE						,				)
					if not matched_google_image_data:
										return 0

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

					__a      :	Optional[Any]    =  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
										__a      :	List[str]    =  bytes(_SCREAMING_SNAKE_CASE						,				'ascii'       ).decode(
										    'unicode-escape'       )
										__a      :	Tuple    =  bytes(_SCREAMING_SNAKE_CASE						,				'ascii'       ).decode(
										    'unicode-escape'       )
										__a      :	Dict    =  urllib.request.build_opener()
										__a      :	Union[str, 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',
										    )
										]
										urllib.request.install_opener(_SCREAMING_SNAKE_CASE       )
										__a      :	List[Any]    =  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:
		__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



