





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

import requests
from bsa import BeautifulSoup

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'
}






def lowerCamelCase__   (							snake_case_     :  Tuple = "dhaka"	,				snake_case_     :  Optional[Any] = 5	)				->    Optional[Any]:
   __snake_case								=       min(_lowercase	,				50	)  # Prevent abuse!
   __snake_case								=       {
       '''q''': query,
       '''tbm''': '''isch''',
       '''hl''': '''en''',
       '''ijn''': '''0''',
   }

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

   __snake_case								=       json.dumps(_lowercase	)
   __snake_case								=       json.loads(_lowercase	)

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

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

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


if __name__ == "__main__":
    try:
        snake_case_           =      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








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
    import torch

class 		SCREAMING_SNAKE_CASE__    (	TensorFormatter[Mapping, 'torch.Tensor', Mapping]  ):


      def __init__(self	:				Optional[int]    ,      a__	:				Tuple=None    ,      **a__	:				Optional[int]				):





         """simple docstring"""



         super().__init__(features=a__				)
         __snake_case								=       torch_tensor_kwargs
         import torch  # noqa import torch at initialization


      def        a			(self	:				Union[str, Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         import torch

         if isinstance(a__    ,      a__				) and column:
            if all(
                isinstance(a__    ,      torch.Tensor				) and x.shape == column[0].shape and x.dtype == column[0].dtype
                for x in column				):
               return torch.stack(a__				)
         return column


      def        a			(self	:				Optional[Any]    ,      a__	:				str				):





         """simple docstring"""



         import torch

         if isinstance(a__    ,      (str, bytes, type(a__				))				):
            return value
         elif isinstance(a__    ,      (np.character, np.ndarray)				) and np.issubdtype(value.dtype    ,      np.character				):
            return value.tolist()

         __snake_case								=       {}

         if isinstance(a__    ,      (np.number, np.ndarray)				) and np.issubdtype(value.dtype    ,      np.integer				):
            __snake_case								=       {'''dtype''': torch.intaa}
         elif isinstance(a__    ,      (np.number, np.ndarray)				) and np.issubdtype(value.dtype    ,      np.floating				):
            __snake_case								=       {'''dtype''': torch.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
            import PIL.Image

            if isinstance(a__    ,      PIL.Image.Image				):
               __snake_case								=       np.asarray(a__				)
         return torch.tensor(a__    ,      **{**default_dtype, **self.torch_tensor_kwargs}				)


      def        a			(self	:				Optional[int]    ,      a__	:				str				):





         """simple docstring"""



         import torch

         # support for torch, tf, jax etc.
         if hasattr(a__    ,      '''__array__'''				) and not isinstance(a__    ,      torch.Tensor				):
            __snake_case								=       data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(a__    ,      np.ndarray				):
            if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
               return self._consolidate([self.recursive_tensorize(a__				) for substruct in data_struct]				)
         elif isinstance(a__    ,      (list, tuple)				):
            return self._consolidate([self.recursive_tensorize(a__				) for substruct in data_struct]				)
         return self._tensorize(a__				)


      def        a			(self	:				Optional[Any]    ,      a__	:				dict				):





         """simple docstring"""



         return map_nested(self._recursive_tensorize    ,      a__    ,      map_list=a__				)


      def        a			(self	:				Optional[int]    ,      a__	:				pa.Table				):





         """simple docstring"""



         __snake_case								=       self.numpy_arrow_extractor().extract_row(a__				)
         __snake_case								=       self.python_features_decoder.decode_row(a__				)
         return self.recursive_tensorize(a__				)


      def        a			(self	:				List[Any]    ,      a__	:				pa.Table				):





         """simple docstring"""



         __snake_case								=       self.numpy_arrow_extractor().extract_column(a__				)
         __snake_case								=       self.python_features_decoder.decode_column(a__    ,      pa_table.column_names[0]				)
         __snake_case								=       self.recursive_tensorize(a__				)
         __snake_case								=       self._consolidate(a__				)
         return column


      def        a			(self	:				Optional[int]    ,      a__	:				pa.Table				):





         """simple docstring"""



         __snake_case								=       self.numpy_arrow_extractor().extract_batch(a__				)
         __snake_case								=       self.python_features_decoder.decode_batch(a__				)
         __snake_case								=       self.recursive_tensorize(a__				)
         for column_name in batch:
            __snake_case								=       self._consolidate(batch[column_name]				)
         return batch

