

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Tuple  					=    'Speech2TextFeatureExtractor'
   _lowercase     :       int  					=    'Speech2TextTokenizer'

   def __init__(				self	:    int			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Union[str, Any]       ) ->			Optional[int]:
         """simple docstring"""





         super().__init__(_UpperCamelCase			,						_UpperCamelCase       )
         A_							       =		self.feature_extractor
         A_							       =		False

   def __call__(				self	:    Tuple			,						*lowerCamelCase__	:    Tuple			,						**lowerCamelCase__	:    List[str]       ) ->			Dict:
         """simple docstring"""





         if self._in_target_context_manager:
               return self.current_processor(*_UpperCamelCase			,						**_UpperCamelCase       )

         if "raw_speech" in kwargs:
               warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''       )
               A_							       =		kwargs.pop('''raw_speech'''       )
         else:
               A_							       =		kwargs.pop('''audio'''			,						_UpperCamelCase       )
         A_							       =		kwargs.pop('''sampling_rate'''			,						_UpperCamelCase       )
         A_							       =		kwargs.pop('''text'''			,						_UpperCamelCase       )
         if len(_UpperCamelCase       ) > 0:
               A_							       =		args[0]
               A_							       =		args[1:]

         if audio is None and text is None:
               raise ValueError('''You need to specify either an `audio` or `text` input to process.'''       )

         if audio is not None:
               A_							       =		self.feature_extractor(_UpperCamelCase			,						*_UpperCamelCase			,						sampling_rate=_UpperCamelCase			,						**_UpperCamelCase       )
         if text is not None:
               A_							       =		self.tokenizer(_UpperCamelCase			,						**_UpperCamelCase       )

         if text is None:
               return inputs
         elif audio is None:
               return encodings
         else:
               A_							       =		encodings['''input_ids''']
               return inputs

   def     UpperCamelCase					(				self	:    Union[str, Any]			,						*lowerCamelCase__	:    str			,						**lowerCamelCase__	:    str       ) ->			Any:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    str			,						*lowerCamelCase__	:    Tuple			,						**lowerCamelCase__	:    Union[str, Any]       ) ->			Optional[Any]:
         """simple docstring"""





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





   @contextmanager
   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Any:
         """simple docstring"""





         warnings.warn(
             '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
             '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
             '''your audio inputs, or in a separate call.'''       )
         A_							       =		True
         A_							       =		self.tokenizer
         yield
         A_							       =		self.feature_extractor
         A_							       =		False









'''simple docstring'''
import colorsys

from PIL import Image  # type: ignore

def 					lowerCamelCase_ ( __UpperCamelCase					:       float							,    __UpperCamelCase					:       float							,    __UpperCamelCase					:       int		)					->	float:







     """simple docstring"""
     _A				=							x
     _A				=							y
     for step in range(__UpperCamelCase		):  # noqa: B007
          _A				=							a * a - b * b + x
          _A				=							2 * a * b + y
          _A				=							a_new

          # divergence happens for all complex number with an absolute value
          # greater than 4
          if a * a + b * b > 4:
               break
     return step / (max_step - 1)

def 					lowerCamelCase_ ( __UpperCamelCase					:       float		)					->	tuple:







     """simple docstring"""
     if distance == 1:
          return (0, 0, 0)
     else:
          return (2_5_5, 2_5_5, 2_5_5)

def 					lowerCamelCase_ ( __UpperCamelCase					:       float		)					->	tuple:







     """simple docstring"""
     if distance == 1:
          return (0, 0, 0)
     else:
          return tuple(round(i * 2_5_5		) for i in colorsys.hsv_to_rgb(__UpperCamelCase							,    1							,    1		)		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       int = 8_0_0							,    __UpperCamelCase					:       int = 6_0_0							,    __UpperCamelCase					:       float = -0.6							,    __UpperCamelCase					:       float = 0							,    __UpperCamelCase					:       float = 3.2							,    __UpperCamelCase					:       int = 5_0							,    __UpperCamelCase					:       bool = True							,    )					->	Image.Image:







     """simple docstring"""
     _A				=							Image.new('RGB'							,    (image_width, image_height)		)
     _A				=							img.load()

     # loop through the image-coordinates
     for image_x in range(__UpperCamelCase		):
          for image_y in range(__UpperCamelCase		):
               # determine the figure-coordinates based on the image-coordinates
               _A				=							figure_width / image_width * image_height
               _A				=							figure_center_x + (image_x / image_width - 0.5) * figure_width
               _A				=							figure_center_y + (image_y / image_height - 0.5) * figure_height

               _A				=							get_distance(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase		)

               # color the corresponding pixel based on the selected coloring-function
               if use_distance_color_coding:
                    _A				=							get_color_coded_rgb(__UpperCamelCase		)
               else:
                    _A				=							get_black_and_white_rgb(__UpperCamelCase		)

     return img


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # colored version, full figure
    lowerCAmelCase						=					get_image()

    # uncomment for colored version, different section, zoomed in
    # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
    # figure_width = 0.8)

    # uncomment for black and white version, full figure
    # img = get_image(use_distance_color_coding = False)

    # uncomment to save the image
    # img.save("mandelbrot.png")

    img.show()





