'''simple docstring'''




import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
   _a   :	Tuple 							=   "%20".join(argv[1:]) if len(argv) > 1 else quote(str(input("Search: ")))

   print("Googling.....")

   _a   :	Optional[Any] 							=   f"""https://www.google.com/search?q={query}&num=100"""

   _a   :	Optional[int] 							=   requests.get(
       url,
       headers={"User-Agent": str(UserAgent().random)},
   )

   try:
      _a   :	Dict 							=   (
          BeautifulSoup(res.text, "html.parser")
          .find("div", attrs={"class": "yuRUbf"})
          .find("a")
          .get("href")
      )

   except AttributeError:
      _a   :	Union[str, Any] 							=   parse_qs(
          BeautifulSoup(res.text, "html.parser")
          .find("div", attrs={"class": "kCrYT"})
          .find("a")
          .get("href")
      )["url"][0]

   webbrowser.open(link)







'''simple docstring'''




# 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 		__A       (TensorFormatter[Mapping, "torch.Tensor", Mapping]   ):






       def __init__(      self				,     UpperCamelCase_=None				,     **UpperCamelCase_      ):
              super().__init__(features=UpperCamelCase_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		torch_tensor_kwargs
              import torch  # noqa import torch at initialization






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              import torch

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






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              import torch

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

              __UpperCAmelCase  :				int       				=		{}

              if isinstance(UpperCamelCase_				,     (np.number, np.ndarray)      ) and np.issubdtype(value.dtype				,     np.integer      ):
                     __UpperCAmelCase  :				Optional[int]       				=		{"dtype": torch.intaa}
              elif isinstance(UpperCamelCase_				,     (np.number, np.ndarray)      ) and np.issubdtype(value.dtype				,     np.floating      ):
                     __UpperCAmelCase  :				str       				=		{"dtype": torch.floataa}
              elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                     import PIL.Image

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






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              import torch

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






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return map_nested(self._recursive_tensorize				,     UpperCamelCase_				,     map_list=UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		self.numpy_arrow_extractor().extract_row(UpperCamelCase_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		self.python_features_decoder.decode_row(UpperCamelCase_      )
              return self.recursive_tensorize(UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		self.numpy_arrow_extractor().extract_column(UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[Any]       				=		self.python_features_decoder.decode_column(UpperCamelCase_				,     pa_table.column_names[0]      )
              __UpperCAmelCase  :				List[Any]       				=		self.recursive_tensorize(UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		self._consolidate(UpperCamelCase_      )
              return column







       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				int       				=		self.numpy_arrow_extractor().extract_batch(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		self.python_features_decoder.decode_batch(UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[int]       				=		self.recursive_tensorize(UpperCamelCase_      )
              for column_name in batch:
                     __UpperCAmelCase  :				Tuple       				=		self._consolidate(batch[column_name]      )
              return batch






