


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






  def __init__(       self   :       Any		,    _lowercase   :       Any=None		,    **_lowercase   :       List[str]			):




         """simple docstring"""







         super().__init__(features=__lowerCAmelCase			)
         UpperCAmelCase__					    =  torch_tensor_kwargs
         import torch  # noqa import torch at initialization






  def       _UpperCAmelCase	(       self   :       Union[str, Any]		,    _lowercase   :       int			):




         """simple docstring"""







         import torch

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






  def       _UpperCAmelCase	(       self   :       Optional[Any]		,    _lowercase   :       str			):




         """simple docstring"""







         import torch

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

         UpperCAmelCase__					    =  {}

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

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






  def       _UpperCAmelCase	(       self   :       Optional[Any]		,    _lowercase   :       Union[str, Any]			):




         """simple docstring"""







         import torch

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






  def       _UpperCAmelCase	(       self   :       Tuple		,    _lowercase   :       List[Any]			):




         """simple docstring"""







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






  def       _UpperCAmelCase	(       self   :       str		,    _lowercase   :       int			):




         """simple docstring"""







         UpperCAmelCase__					    =  self.numpy_arrow_extractor().extract_row(__lowerCAmelCase			)
         UpperCAmelCase__					    =  self.python_features_decoder.decode_row(__lowerCAmelCase			)
         return self.recursive_tensorize(__lowerCAmelCase			)






  def       _UpperCAmelCase	(       self   :       Optional[int]		,    _lowercase   :       Tuple			):




         """simple docstring"""







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






  def       _UpperCAmelCase	(       self   :       Tuple		,    _lowercase   :       Optional[Any]			):




         """simple docstring"""







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






'''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 							snake_case__				(     _A:   str = "dhaka" ,  _A:   int = 5					)       ->      int:

		'''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:
												__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




