

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

import requests
from bsa import BeautifulSoup

__A     :					Optional[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     __UpperCamelCase			(	_A :				int = "dhaka"	,   _A :				Tuple = 5						)							->str:



							"""simple docstring"""
							lowerCamelCase_				     =min(__A	,   50						)  # Prevent abuse!
							lowerCamelCase_				     ={
							    """q""": query,
							    """tbm""": """isch""",
							    """hl""": """en""",
							    """ijn""": """0""",
							}

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

							lowerCamelCase_				     =json.dumps(__A						)
							lowerCamelCase_				     =json.loads(__A						)

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

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

							lowerCamelCase_				     =re.findall(
							    R"""(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]"""	,   __A	,   )
							for index, fixed_full_res_image in enumerate(__A						):
														if index >= max_images:
																					return index
														lowerCamelCase_				     =bytes(__A	,   """ascii"""						).decode(
														    """unicode-escape"""						)
														lowerCamelCase_				     =bytes(__A	,   """ascii"""						).decode(
														    """unicode-escape"""						)
														lowerCamelCase_				     =urllib.request.build_opener()
														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""",
														    )
														]
														urllib.request.install_opener(__A						)
														lowerCamelCase_				     =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:
		__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





'''simple docstring'''





import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     if isinstance(__A  ,	__A):
          _a      =  np.full((len(__A), sequence_length, 2)  ,	__A)
     else:
          _a      =  np.full((len(__A), sequence_length)  ,	__A)

     for i, tensor in enumerate(__A):
          if padding_side == "right":
               if isinstance(__A  ,	__A):
                    _a      =  tensor[:sequence_length]
               else:
                    _a      =  tensor[:sequence_length]
          else:
               if isinstance(__A  ,	__A):
                    _a      =  tensor[:sequence_length]
               else:
                    _a      =  tensor[:sequence_length]

     return out_tensor.tolist()




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  ord(__A)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
          return True
     _a      =  unicodedata.category(__A)
     if cat.startswith('''P'''):
          return True
     return False






@dataclass
class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       PreTrainedTokenizerBase
 __lowerCamelCase    :       Union[bool, str, PaddingStrategy]          =       True
 __lowerCamelCase    :       Optional[int]          =       None
 __lowerCamelCase    :       Optional[int]          =       None
 __lowerCamelCase    :       int          =       -100
 __lowerCamelCase    :       str          =       "pt"



 def 					a__  (self ,   A       )    ->     List[str]:


      """simple docstring"""



      import torch

      _a      =  '''label''' if '''label''' in features[0].keys() else '''labels'''
      _a      =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
      _a      =  self.tokenizer.pad(
          A ,   padding=self.padding ,   max_length=self.max_length ,   pad_to_multiple_of=self.pad_to_multiple_of ,   return_tensors='''pt''' if labels is None else None ,   )

      if labels is None:
           return batch

      _a      =  torch.tensor(batch['''entity_ids''']       ).shape[1]
      _a      =  self.tokenizer.padding_side
      if padding_side == "right":
           _a      =  [
               list(A       ) + [self.label_pad_token_id] * (sequence_length - len(A       )) for label in labels
           ]
      else:
           _a      =  [
               [self.label_pad_token_id] * (sequence_length - len(A       )) + list(A       ) for label in labels
           ]

      _a      =  [feature['''ner_tags'''] for feature in features]
      _a      =  padding_tensor(A ,   -1 ,   A ,   A       )
      _a      =  [feature['''original_entity_spans'''] for feature in features]
      _a      =  padding_tensor(A ,   (-1, -1) ,   A ,   A       )
      _a      =  {k: torch.tensor(A ,   dtype=torch.intaa       ) for k, v in batch.items()}

      return batch



