





"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput







class       a							(   a__  ):



 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= dataset
  lowerCAmelCase    				= process
  lowerCAmelCase    				= params



 def __len__(     self     ):







  """simple docstring"""






  return len(self.dataset     )



 def __getitem__(     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.dataset[i]
  lowerCAmelCase    				= self.process(_snake_case				,       **self.params     )
  return processed







class       a							(   a__  ):



 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case=None     ):







  """simple docstring"""






  lowerCAmelCase    				= loader
  lowerCAmelCase    				= infer
  lowerCAmelCase    				= params
  if loader_batch_size == 1:
   # Let's spare some time by deactivating altogether
   lowerCAmelCase    				= None
  lowerCAmelCase    				= loader_batch_size

  # Internal bookkeeping
  lowerCAmelCase    				= None
  lowerCAmelCase    				= None



 def __len__(     self     ):







  """simple docstring"""






  return len(self.loader     )



 def __iter__(     self     ):







  """simple docstring"""






  lowerCAmelCase    				= iter(self.loader     )
  return self



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  if isinstance(self._loader_batch_data				,       torch.Tensor     ):
   # Batch data is simple tensor, just fetch the slice
   lowerCAmelCase    				= self._loader_batch_data[self._loader_batch_index]
  else:
   # Batch data is assumed to be BaseModelOutput (or dict)
   lowerCAmelCase    				= {}
   for k, element in self._loader_batch_data.items():
    if isinstance(_snake_case				,       _snake_case     ):
     # Convert ModelOutput to tuple first
     lowerCAmelCase    				= element.to_tuple()
     if isinstance(element[0]				,       torch.Tensor     ):
      lowerCAmelCase    				= tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
     elif isinstance(element[0]				,       np.ndarray     ):
      lowerCAmelCase    				= tuple(np.expand_dims(el[self._loader_batch_index]				,       0     ) for el in element     )
     continue
    if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_snake_case				,       _snake_case     ):
     # Those are stored as lists of tensors so need specific unbatching.
     if isinstance(element[0]				,       torch.Tensor     ):
      lowerCAmelCase    				= tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
     elif isinstance(element[0]				,       np.ndarray     ):
      lowerCAmelCase    				= tuple(np.expand_dims(el[self._loader_batch_index]				,       0     ) for el in element     )
     continue
    if element is None:
     # This can happen for optional data that get passed around
     lowerCAmelCase    				= None
    elif isinstance(element[self._loader_batch_index]				,       torch.Tensor     ):
     # Take correct batch data, but make it looked like batch_size=1
     # For compatibility with other methods within transformers

     lowerCAmelCase    				= element[self._loader_batch_index].unsqueeze(0     )
    elif isinstance(element[self._loader_batch_index]				,       np.ndarray     ):
     # Take correct batch data, but make it looked like batch_size=1
     # For compatibility with other methods within transformers
     lowerCAmelCase    				= np.expand_dims(element[self._loader_batch_index]				,       0     )
    else:
     # This is typically a list, so no need to `unsqueeze`.
     lowerCAmelCase    				= element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
   lowerCAmelCase    				= self._loader_batch_data.__class__(_snake_case     )
  self._loader_batch_index += 1
  return result



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
   # We are currently unrolling a batch so we just need to return
   # the current item within a batch
   return self.loader_batch_item()

  # We're out of items within a batch
  lowerCAmelCase    				= next(self.iterator     )
  lowerCAmelCase    				= self.infer(_snake_case				,       **self.params     )
  # We now have a batch of "inferred things".
  if self.loader_batch_size is not None:
   # Try to infer the size of the batch
   if isinstance(_snake_case				,       torch.Tensor     ):
    lowerCAmelCase    				= processed
   else:
    lowerCAmelCase    				= list(processed.keys()     )[0]
    lowerCAmelCase    				= processed[key]
   if isinstance(_snake_case				,       _snake_case     ):
    lowerCAmelCase    				= len(_snake_case     )
   else:
    lowerCAmelCase    				= first_tensor.shape[0]
   if 0 < observed_batch_size < self.loader_batch_size:
    # could be last batch so we can't unroll as many
    # elements.
    lowerCAmelCase    				= observed_batch_size
   # Setting internal index to unwrap the batch
   lowerCAmelCase    				= processed
   lowerCAmelCase    				= 0
   return self.loader_batch_item()
  else:
   # We're not unrolling batches
   return processed







class       a							(   a__  ):



 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case=None     ):







  """simple docstring"""






  super().__init__(_snake_case				,       _snake_case				,       _snake_case     )



 def __iter__(     self     ):







  """simple docstring"""






  lowerCAmelCase    				= iter(self.loader     )
  lowerCAmelCase    				= None
  return self



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  if self.subiterator is None:
   lowerCAmelCase    				= self.infer(next(self.iterator     )				,       **self.params     )
  try:
   # Try to return next item
   lowerCAmelCase    				= next(self.subiterator     )
  except StopIteration:
   # When a preprocess iterator ends, we can start lookig at the next item
   # ChunkIterator will keep feeding until ALL elements of iterator
   # all have created their subiterator and have been iterating against.
   #
   # Another way to look at it, is we're basically flattening lists of lists
   # into a single list, but with generators
   lowerCAmelCase    				= self.infer(next(self.iterator     )				,       **self.params     )
   lowerCAmelCase    				= next(self.subiterator     )
  return processed







class       a							(   a__  ):



 def __iter__(     self     ):







  """simple docstring"""






  lowerCAmelCase    				= iter(self.loader     )
  return self



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= False
  lowerCAmelCase    				= []
  if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
   while self._loader_batch_index < self.loader_batch_size:
    lowerCAmelCase    				= self.loader_batch_item()
    lowerCAmelCase    				= item.pop('is_last'     )
    accumulator.append(_snake_case     )
    if is_last:
     return accumulator

  while not is_last:
   lowerCAmelCase    				= self.infer(next(self.iterator     )				,       **self.params     )
   if self.loader_batch_size is not None:
    if isinstance(_snake_case				,       torch.Tensor     ):
     lowerCAmelCase    				= processed
    else:
     lowerCAmelCase    				= list(processed.keys()     )[0]
     lowerCAmelCase    				= processed[key]
    if isinstance(_snake_case				,       _snake_case     ):
     lowerCAmelCase    				= len(_snake_case     )
    else:
     lowerCAmelCase    				= first_tensor.shape[0]
    if 0 < observed_batch_size < self.loader_batch_size:
     # could be last batch so we can't unroll as many
     # elements.
     lowerCAmelCase    				= observed_batch_size
    lowerCAmelCase    				= processed
    lowerCAmelCase    				= 0
    while self._loader_batch_index < self.loader_batch_size:
     lowerCAmelCase    				= self.loader_batch_item()
     lowerCAmelCase    				= item.pop('is_last'     )
     accumulator.append(_snake_case     )
     if is_last:
      return accumulator
   else:
    lowerCAmelCase    				= processed
    lowerCAmelCase    				= item.pop('is_last'     )
    accumulator.append(_snake_case     )
  return accumulator







class       a							(   a__  ):



 def __init__(     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= dataset
  lowerCAmelCase    				= key



 def __len__(     self     ):







  """simple docstring"""






  return len(self.dataset     )



 def __getitem__(     self				,       _snake_case     ):







  """simple docstring"""






  return self.dataset[i][self.key]







class       a							(   a__  ):



 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= dataset
  lowerCAmelCase    				= keya
  lowerCAmelCase    				= keya



 def __len__(     self     ):







  """simple docstring"""






  return len(self.dataset     )



 def __getitem__(     self				,       _snake_case     ):







  """simple docstring"""






  return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}








'''simple docstring'''




import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
  print('Googling.....')
  _a					:			List[Any]							=    'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
  _a					:			Dict							=    requests.get(url, headers={'UserAgent': UserAgent().random})
  # res.raise_for_status()
  with open('project1a.html', 'wb') as out_file:  # only for knowing the class
    for data in res.iter_content(10_000):
      out_file.write(data)
  _a					:			Any							=    BeautifulSoup(res.text, 'html.parser')
  _a					:			Optional[Any]							=    list(soup.select('.eZt8xd'))[:5]

  print(len(links))
  for link in links:
    if link.text == "Maps":
      webbrowser.open(link.get('href'))
    else:
      webbrowser.open(F'''https://google.com{link.get('href')}''')


