
'''simple docstring'''

from ...processing_utils import ProcessorMixin




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Any	=			"""SpeechT5FeatureExtractor"""
 A_				:				Optional[int]	=			"""SpeechT5Tokenizer"""


 def __init__(    self  : Union[str, Any]			,							a__  : Union[str, Any]			,							a__  : Optional[int]    ):
  '''simple docstring'''

  super().__init__(a__			,							a__    )


 def __call__(    self  : Tuple			,							*a__  : Any			,							**a__  : int    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	kwargs.pop("audio"			,							a__    )
  lowerCAmelCase__     :   Union[str, Any]     		=	kwargs.pop("text"			,							a__    )
  lowerCAmelCase__     :   Dict     		=	kwargs.pop("text_target"			,							a__    )
  lowerCAmelCase__     :   str     		=	kwargs.pop("audio_target"			,							a__    )
  lowerCAmelCase__     :   List[Any]     		=	kwargs.pop("sampling_rate"			,							a__    )

  if audio is not None and text is not None:
   raise ValueError(
       "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"    )
  if audio_target is not None and text_target is not None:
   raise ValueError(
       "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"    )
  if audio is None and audio_target is None and text is None and text_target is None:
   raise ValueError(
       "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."    )

  if audio is not None:
   lowerCAmelCase__     :   str     		=	self.feature_extractor(a__			,							*a__			,							sampling_rate=a__			,							**a__    )
  elif text is not None:
   lowerCAmelCase__     :   Tuple     		=	self.tokenizer(a__			,							**a__    )
  else:
   lowerCAmelCase__     :   Tuple     		=	None

  if audio_target is not None:
   lowerCAmelCase__     :   Tuple     		=	self.feature_extractor(audio_target=a__			,							*a__			,							sampling_rate=a__			,							**a__    )
   lowerCAmelCase__     :   int     		=	targets["input_values"]
  elif text_target is not None:
   lowerCAmelCase__     :   int     		=	self.tokenizer(a__			,							**a__    )
   lowerCAmelCase__     :   Optional[Any]     		=	targets["input_ids"]
  else:
   lowerCAmelCase__     :   Dict     		=	None

  if inputs is None:
   return targets

  if targets is not None:
   lowerCAmelCase__     :   Any     		=	labels

   lowerCAmelCase__     :   Any     		=	targets.get("attention_mask"    )
   if decoder_attention_mask is not None:
    lowerCAmelCase__     :   List[str]     		=	decoder_attention_mask

  return inputs


 def    _A	(    self  : Dict			,							*a__  : int			,							**a__  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	kwargs.pop("input_values"			,							a__    )
  lowerCAmelCase__     :   List[str]     		=	kwargs.pop("input_ids"			,							a__    )
  lowerCAmelCase__     :   Any     		=	kwargs.pop("labels"			,							a__    )

  if input_values is not None and input_ids is not None:
   raise ValueError("Cannot process both `input_values` and `input_ids` inputs."    )
  if input_values is None and input_ids is None and labels is None:
   raise ValueError(
       "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."    )

  if input_values is not None:
   lowerCAmelCase__     :   str     		=	self.feature_extractor.pad(a__			,							*a__			,							**a__    )
  elif input_ids is not None:
   lowerCAmelCase__     :   Tuple     		=	self.tokenizer.pad(a__			,							**a__    )
  else:
   lowerCAmelCase__     :   Union[str, Any]     		=	None

  if labels is not None:
   if "input_ids" in labels or (isinstance(a__			,							a__    ) and "input_ids" in labels[0]):
    lowerCAmelCase__     :   Dict     		=	self.tokenizer.pad(a__			,							**a__    )
    lowerCAmelCase__     :   Dict     		=	targets["input_ids"]
   else:
    lowerCAmelCase__     :   List[str]     		=	self.feature_extractor.feature_size
    lowerCAmelCase__     :   Optional[int]     		=	self.feature_extractor.num_mel_bins
    lowerCAmelCase__     :   Optional[int]     		=	self.feature_extractor.pad(a__			,							*a__			,							**a__    )
    lowerCAmelCase__     :   Tuple     		=	feature_size_hack
    lowerCAmelCase__     :   Union[str, Any]     		=	targets["input_values"]
  else:
   lowerCAmelCase__     :   List[Any]     		=	None

  if inputs is None:
   return targets

  if targets is not None:
   lowerCAmelCase__     :   List[Any]     		=	labels

   lowerCAmelCase__     :   Any     		=	targets.get("attention_mask"    )
   if decoder_attention_mask is not None:
    lowerCAmelCase__     :   Tuple     		=	decoder_attention_mask

  return inputs


 def    _A	(    self  : Dict			,							*a__  : int			,							**a__  : Any    ):
  '''simple docstring'''

  return self.tokenizer.batch_decode(*a__			,							**a__    )


 def    _A	(    self  : Any			,							*a__  : Any			,							**a__  : Optional[int]    ):
  '''simple docstring'''

  return self.tokenizer.decode(*a__			,							**a__    )







'''simple docstring'''

import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
     print("""Googling.....""")
     snake_case									=    """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
     snake_case									=    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(1_00_00):
               out_file.write(data)
     snake_case									=    BeautifulSoup(res.text, """html.parser""")
     snake_case									=    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")}')





