






"""simple docstring"""
import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

lowerCamelCase_					=      {
    '''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 				snake_case      (   A__ = "dhaka"	,A__ = 5						):
			UpperCAmelCase_     : List[str]			     =					min(__A	,50						)  # Prevent abuse!
			UpperCAmelCase_     : List[str]			     =					{
			    "q": query,
			    "tbm": "isch",
			    "hl": "en",
			    "ijn": "0",
			}

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

			UpperCAmelCase_     : List[Any]			     =					json.dumps(__A						)
			UpperCAmelCase_     : str			     =					json.loads(__A						)

			UpperCAmelCase_     : List[str]			     =					re.findall(
			    r"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","	,__A	,)
			if not matched_google_image_data:
						return 0

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

			UpperCAmelCase_     : int			     =					re.findall(
			    r"(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"	,__A	,)
			for index, fixed_full_res_image in enumerate(__A						):
						if index >= max_images:
									return index
						UpperCAmelCase_     : List[str]			     =					bytes(__A	,"ascii"						).decode(
						    "unicode-escape"						)
						UpperCAmelCase_     : List[Any]			     =					bytes(__A	,"ascii"						).decode(
						    "unicode-escape"						)
						UpperCAmelCase_     : List[str]			     =					urllib.request.build_opener()
						UpperCAmelCase_     : 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(__A						)
						UpperCAmelCase_     : int			     =					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:
						lowerCamelCase_					=      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 inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment





class      A							(				unittest.TestCase     ):


   def 			lowercase_      (self     :						Union[str, Any]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  inspect.getfile(accelerate.test_utils		)
         UpperCAmelCase__								=  os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_script.py"]		)
         UpperCAmelCase__								=  os.path.sep.join(
             mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_distributed_data_loop.py"]		)
         UpperCAmelCase__								=  os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_ops.py"]		)


   @require_multi_gpu
   def 			lowercase_      (self     :						List[str]		)						->				Any:
         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices."""		)
         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
         with patch_environment(omp_num_threads=1		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)


   @require_multi_gpu
   def 			lowercase_      (self     :						str		)						->				str:
         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices."""		)
         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
         print(f"""Command: {cmd}"""		)
         with patch_environment(omp_num_threads=1		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)


   @require_multi_gpu
   def 			lowercase_      (self     :						Tuple		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__		)]
         with patch_environment(omp_num_threads=1		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)





   @require_multi_gpu
   def 			lowercase_      (self     :						Dict		)						->				str:
         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""		)
         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
         with patch_environment(omp_num_threads=1       ,  cuda_visible_devices="0,1"		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)


if __name__ == "__main__":
      UpperCamelCase__											=					Accelerator()
      UpperCamelCase__											=					(accelerator.state.process_index + 2, 1_0)
      UpperCamelCase__											=					torch.randint(0, 1_0, shape).to(accelerator.device)

      UpperCamelCase__											=					''

      UpperCamelCase__											=					accelerator.pad_across_processes(tensor)
      if tensora.shape[0] != accelerator.state.num_processes + 1:
            error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
      if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
            error_msg += "Tensors have different values."
      if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
            error_msg += "Padding was not done with the right value (0)."

      UpperCamelCase__											=					accelerator.pad_across_processes(tensor, pad_first=True)
      if tensora.shape[0] != accelerator.state.num_processes + 1:
            error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
      UpperCamelCase__											=					accelerator.state.num_processes - accelerator.state.process_index - 1
      if not torch.equal(tensora[index:], tensor):
            error_msg += "Tensors have different values."
      if not torch.all(tensora[:index] == 0):
            error_msg += "Padding was not done with the right value (0)."

      # Raise error at the end to make sure we don't stop at the first failure.
      if len(error_msg) > 0:
            raise ValueError(error_msg)



