import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def      _a					(   lowercase__	:      Optional[Any]  ,	lowercase__	:      Any  ,	lowercase__	:      List[Any]			):







     '''simple docstring'''


     if openai_config_file == "":
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		OpenAIGPTConfig()
     else:
          SCREAMING_SNAKE_CASE__					:					int      =		OpenAIGPTConfig.from_json_file(lowercase__			)
     SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		OpenAIGPTModel(lowercase__			)

     # Load weights from numpy
     load_tf_weights_in_openai_gpt(lowercase__  ,	lowercase__  ,	lowercase__			)

     # Save pytorch-model
     SCREAMING_SNAKE_CASE__					:					str      =		pytorch_dump_folder_path + '/' + WEIGHTS_NAME
     SCREAMING_SNAKE_CASE__					:					Dict      =		pytorch_dump_folder_path + '/' + CONFIG_NAME
     print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''			)
     torch.save(model.state_dict()  ,	lowercase__			)
     print(f'''Save configuration file to {pytorch_config_dump_path}'''			)
     with open(lowercase__  ,	'w'  ,	encoding='utf-8'			) as f:
          f.write(config.to_json_string()			)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:       Union[str, Any]       						=       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--openai_checkpoint_folder_path",
          default=None,
          type=str,
          required=True,
          help="Path to the TensorFlow checkpoint path.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--openai_config_file",
          default="",
          type=str,
          help=(
              "An optional config json file corresponding to the pre-trained OpenAI model. \n"
              "This specifies the model architecture."
          ),
      )
      SCREAMING_SNAKE_CASE__		:       List[str]       						=       parser.parse_args()
      convert_openai_checkpoint_to_pytorch(
          args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
      )





"""simple docstring"""






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

import requests
from bsa import BeautifulSoup

_lowercase							=				{
    '''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__			(		__magic_name__ = "dhaka"			,   __magic_name__ = 5      )						->int:
			__lowercase       =       min(__magic_name__			,   5_0      )  # Prevent abuse!
			__lowercase       =       {
			    "q": query,
			    "tbm": "isch",
			    "hl": "en",
			    "ijn": "0",
			}

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

			__lowercase       =       json.dumps(__magic_name__      )
			__lowercase       =       json.loads(__magic_name__      )

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

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

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


if __name__ == "__main__":
					try:
										_lowercase							=				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





