




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__":
 lowerCamelCase				:		Any     =   "%20".join(argv[1:]) if len(argv) > 1 else quote(str(input("Search: ")))

 print("Googling.....")

 lowerCamelCase				:		Union[str, Any]     =   F"""https://www.google.com/search?q={query}&num=100"""

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

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

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

 webbrowser.open(link)









import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Union[dict, list, tuple, torch.Tensor]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    []
      if isinstance(lowercase	,							lowercase     ):
            for v in tree.values():
                  shapes.extend(_fetch_dims(lowercase     )     )
      elif isinstance(lowercase	,							(list, tuple)     ):
            for t in tree:
                  shapes.extend(_fetch_dims(lowercase     )     )
      elif isinstance(lowercase	,							torch.Tensor     ):
            shapes.append(tree.shape     )
      else:
            raise ValueError('Not supported'     )

      return shapes

@torch.jit.ignore
def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       int	,							lowercase	:       Tuple[int, ...]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    []
      for d in reversed(lowercase     ):
            idx.append(flat_idx % d     )
            lowerCamelCase_		     =    flat_idx // d

      return tuple(reversed(lowercase     )     )

@torch.jit.ignore
def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Sequence[int]	,							lowercase	:       Sequence[int]	,							lowercase	:       Sequence[int]	,							lowercase	:       Optional[Sequence[bool]] = None	,							lowercase	:       Optional[Sequence[bool]] = None	,							):


      '''simple docstring'''



      def reduce_edge_list(lowercase	:       List[bool]     ) -> None:
            lowerCamelCase_		     =    True
            for i in range(len(lowercase     )     ):
                  lowerCamelCase_		     =    -1 * (i + 1)
                  l[reversed_idx] &= tally
                  lowerCamelCase_		     =    l[reversed_idx]

      if start_edges is None:
            lowerCamelCase_		     =    [s == 0 for s in start]
            reduce_edge_list(lowercase     )
      if end_edges is None:
            lowerCamelCase_		     =    [e == (d - 1) for e, d in zip(lowercase	,							lowercase     )]
            reduce_edge_list(lowercase     )

      # Base cases. Either start/end are empty and we're done, or the final,
      # one-dimensional tensor can be simply sliced
      if len(lowercase     ) == 0:
            return [()]
      elif len(lowercase     ) == 1:
            return [(slice(start[0]	,							end[0] + 1     ),)]

      lowerCamelCase_		     =    []
      lowerCamelCase_		     =    []

      # Dimensions common to start and end can be selected directly
      for s, e in zip(lowercase	,							lowercase     ):
            if s == e:
                  path_list.append(slice(lowercase	,							s + 1     )     )
            else:
                  break

      lowerCamelCase_		     =    tuple(lowercase     )
      lowerCamelCase_		     =    len(lowercase     )

      # start == end, and we're done
      if divergence_idx == len(lowercase     ):
            return [path]

      def upper() -> Tuple[Tuple[slice, ...], ...]:
            assert start_edges is not None
            assert end_edges is not None

            lowerCamelCase_		     =    start[divergence_idx]
            return tuple(
                path + (slice(lowercase	,							sdi + 1     ),) + s
                for s in _get_minimal_slice_set(
                    start[divergence_idx + 1 :]	,							[d - 1 for d in dims[divergence_idx + 1 :]]	,							dims[divergence_idx + 1 :]	,							start_edges=start_edges[divergence_idx + 1 :]	,							end_edges=[True for _ in end_edges[divergence_idx + 1 :]]	,							)     )

      def lower() -> Tuple[Tuple[slice, ...], ...]:
            assert start_edges is not None
            assert end_edges is not None

            lowerCamelCase_		     =    end[divergence_idx]
            return tuple(
                path + (slice(lowercase	,							edi + 1     ),) + s
                for s in _get_minimal_slice_set(
                    [0 for _ in start[divergence_idx + 1 :]]	,							end[divergence_idx + 1 :]	,							dims[divergence_idx + 1 :]	,							start_edges=[True for _ in start_edges[divergence_idx + 1 :]]	,							end_edges=end_edges[divergence_idx + 1 :]	,							)     )

      # If both start and end are at the edges of the subtree rooted at
      # divergence_idx, we can just select the whole subtree at once
      if start_edges[divergence_idx] and end_edges[divergence_idx]:
            slices.append(path + (slice(start[divergence_idx]	,							end[divergence_idx] + 1     ),)     )
      # If just start is at the edge, we can grab almost all of the subtree,
      # treating only the ragged bottom edge as an edge case
      elif start_edges[divergence_idx]:
            slices.append(path + (slice(start[divergence_idx]	,							end[divergence_idx]     ),)     )
            slices.extend(lower()     )
      # Analogous to the previous case, but the top is ragged this time
      elif end_edges[divergence_idx]:
            slices.extend(upper()     )
            slices.append(path + (slice(start[divergence_idx] + 1	,							end[divergence_idx] + 1     ),)     )
      # If both sides of the range are ragged, we need to handle both sides
      # separately. If there's contiguous meat in between them, we can index it
      # in one big chunk
      else:
            slices.extend(upper()     )
            lowerCamelCase_		     =    end[divergence_idx] - start[divergence_idx]
            if middle_ground > 1:
                  slices.append(path + (slice(start[divergence_idx] + 1	,							end[divergence_idx]     ),)     )
            slices.extend(lower()     )

      return slices

@torch.jit.ignore
def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       torch.Tensor	,							lowercase	:       int	,							lowercase	:       int	,							lowercase	:       int     ):


      '''simple docstring'''



      lowerCamelCase_		     =    t.shape[:no_batch_dims]
      lowerCamelCase_		     =    list(_flat_idx_to_idx(lowercase	,							lowercase     )     )
      # _get_minimal_slice_set is inclusive
      lowerCamelCase_		     =    list(_flat_idx_to_idx(flat_end - 1	,							lowercase     )     )

      # Get an ordered list of slices to perform
      lowerCamelCase_		     =    _get_minimal_slice_set(
          lowercase	,							lowercase	,							lowercase	,							)

      lowerCamelCase_		     =    [t[s] for s in slices]

      return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]     ) for s in sliced_tensors]     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Callable	,							lowercase	:       Dict[str, Any]	,							lowercase	:       int	,							lowercase	:       int	,							lowercase	:       bool = False	,							lowercase	:       Any = None	,							lowercase	:       bool = False	,							):


      '''simple docstring'''



      if not (len(lowercase     ) > 0):
            raise ValueError('Must provide at least one input'     )

      lowerCamelCase_		     =    [shape[:no_batch_dims] for shape in _fetch_dims(lowercase     )]
      lowerCamelCase_		     =    tuple([max(lowercase     ) for s in zip(*lowercase     )]     )

      def _prep_inputs(lowercase	:       torch.Tensor     ) -> torch.Tensor:
            if not low_mem:
                  if not sum(t.shape[:no_batch_dims]     ) == no_batch_dims:
                        lowerCamelCase_		     =    t.expand(orig_batch_dims + t.shape[no_batch_dims:]     )
                  lowerCamelCase_		     =    t.reshape(-1	,							*t.shape[no_batch_dims:]     )
            else:
                  lowerCamelCase_		     =    t.expand(orig_batch_dims + t.shape[no_batch_dims:]     )
            return t

      lowerCamelCase_		     =    tensor_tree_map(_prep_inputs	,							lowercase     )
      lowerCamelCase_		     =    None
      if _out is not None:
            lowerCamelCase_		     =    tensor_tree_map(lambda lowercase     : t.view([-1] + list(t.shape[no_batch_dims:]     )     )	,							_out     )

      lowerCamelCase_		     =    1
      for d in orig_batch_dims:
            flat_batch_dim *= d

      lowerCamelCase_		     =    flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

      def _select_chunk(lowercase	:       torch.Tensor     ) -> torch.Tensor:
            return t[i : i + chunk_size] if t.shape[0] != 1 else t

      lowerCamelCase_		     =    0
      lowerCamelCase_		     =    prepped_outputs
      for _ in range(lowercase     ):
            # Chunk the input
            if not low_mem:
                  lowerCamelCase_		     =    _select_chunk
            else:
                  lowerCamelCase_		     =    partial(
                      _chunk_slice	,							flat_start=lowercase	,							flat_end=min(lowercase	,							i + chunk_size     )	,							no_batch_dims=len(lowercase     )	,							)

            lowerCamelCase_		     =    tensor_tree_map(lowercase	,							lowercase     )

            # Run the layer on the chunk
            lowerCamelCase_		     =    layer(**lowercase     )

            # Allocate space for the output
            if out is None:
                  lowerCamelCase_		     =    tensor_tree_map(lambda lowercase     : t.new_zeros((flat_batch_dim,) + t.shape[1:]     )	,							lowercase     )

            # Put the chunk in its pre-allocated space
            if isinstance(lowercase	,							lowercase     ):

                  def assign(lowercase	:       dict	,							lowercase	:       dict     ) -> None:
                        for k, v in da.items():
                              if isinstance(lowercase	,							lowercase     ):
                                    assign(lowercase	,							da[k]     )
                              else:
                                    if _add_into_out:
                                          v[i : i + chunk_size] += da[k]
                                    else:
                                          lowerCamelCase_		     =    da[k]

                  assign(lowercase	,							lowercase     )
            elif isinstance(lowercase	,							lowercase     ):
                  for xa, xa in zip(lowercase	,							lowercase     ):
                        if _add_into_out:
                              xa[i : i + chunk_size] += xa
                        else:
                              lowerCamelCase_		     =    xa
            elif isinstance(lowercase	,							torch.Tensor     ):
                  if _add_into_out:
                        out[i : i + chunk_size] += output_chunk
                  else:
                        lowerCamelCase_		     =    output_chunk
            else:
                  raise ValueError('Not supported'     )

            i += chunk_size

      lowerCamelCase_		     =    tensor_tree_map(lambda lowercase     : t.view(orig_batch_dims + t.shape[1:]     )	,							lowercase     )

      return out







class   A:



 '''simple docstring'''


 def __init__(				self					:  Optional[Any]						,							A_					:  int = 512						,							)   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    max_chunk_size
       lowerCamelCase_		     =    None
       lowerCamelCase_		     =    None

 def 				a__ (				self					:  int						,							A_					:  Callable						,							A_					:  tuple						,							A_					:  int  )   ->	int:

       """simple docstring"""






       logging.info('Tuning chunk size...'  )

       if min_chunk_size >= self.max_chunk_size:
             return min_chunk_size

       lowerCamelCase_		     =    [2**l for l in range(int(math.log(self.max_chunk_size						,							2  )  ) + 1  )]
       lowerCamelCase_		     =    [c for c in candidates if c > min_chunk_size]
       lowerCamelCase_		     =    [min_chunk_size] + candidates
       candidates[-1] += 4

       def test_chunk_size(A_					:  int  ) -> bool:
             try:
                   with torch.no_grad():
                         fn(*A_						,							chunk_size=A_  )
                   return True
             except RuntimeError:
                   return False

       lowerCamelCase_		     =    0
       lowerCamelCase_		     =    len(A_  ) - 1
       while i > min_viable_chunk_size_index:
             lowerCamelCase_		     =    test_chunk_size(candidates[i]  )
             if not viable:
                   lowerCamelCase_		     =    (min_viable_chunk_size_index + i) // 2
             else:
                   lowerCamelCase_		     =    i
                   lowerCamelCase_		     =    (i + len(A_  ) - 1) // 2

       return candidates[min_viable_chunk_size_index]

 def 				a__ (				self					:  List[str]						,							A_					:  Iterable						,							A_					:  Iterable  )   ->	bool:

       """simple docstring"""






       lowerCamelCase_		     =    True
       for aa, aa in zip(A_						,							A_  ):
             assert type(A_  ) == type(A_  )
             if isinstance(A_						,							(list, tuple)  ):
                   consistent &= self._compare_arg_caches(A_						,							A_  )
             elif isinstance(A_						,							A_  ):
                   lowerCamelCase_		     =    [v for _, v in sorted(aa.items()						,							key=lambda A_  : x[0]  )]
                   lowerCamelCase_		     =    [v for _, v in sorted(aa.items()						,							key=lambda A_  : x[0]  )]
                   consistent &= self._compare_arg_caches(A_						,							A_  )
             else:
                   consistent &= aa == aa

       return consistent





 def 				a__ (				self					:  int						,							A_					:  Callable						,							A_					:  tuple						,							A_					:  int						,							)   ->	int:

       """simple docstring"""






       lowerCamelCase_		     =    True
       lowerCamelCase_		     =    tree_map(lambda A_  : a.shape if isinstance(A_						,							torch.Tensor  ) else a						,							A_						,							A_  )
       if self.cached_arg_data is not None:
             # If args have changed shape/value, we need to re-tune
             assert len(self.cached_arg_data  ) == len(A_  )
             lowerCamelCase_		     =    self._compare_arg_caches(self.cached_arg_data						,							A_  )
       else:
             # Otherwise, we can reuse the precomputed value
             lowerCamelCase_		     =    False

       if not consistent:
             lowerCamelCase_		     =    self._determine_favorable_chunk_size(
                 A_						,							A_						,							A_						,							)
             lowerCamelCase_		     =    arg_data

       assert self.cached_chunk_size is not None

       return self.cached_chunk_size



