import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class      A							(				_lowerCamelCase     ):
   __UpperCAmelCase : Optional[int]      =			'Speech2TextFeatureExtractor'
   __UpperCAmelCase : Dict      =			'Speech2TextTokenizer'


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



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


   def __call__(self     :						Optional[Any]       ,  *__UpperCAmelCase     :						str       ,  **__UpperCAmelCase     :						Tuple		)						->				Union[str, Any]:
         """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."		)
               UpperCAmelCase__								=  kwargs.pop("raw_speech"		)
         else:
               UpperCAmelCase__								=  kwargs.pop("audio"       ,  _UpperCamelCase		)
         UpperCAmelCase__								=  kwargs.pop("sampling_rate"       ,  _UpperCamelCase		)
         UpperCAmelCase__								=  kwargs.pop("text"       ,  _UpperCamelCase		)
         if len(_UpperCamelCase		) > 0:
               UpperCAmelCase__								=  args[0]
               UpperCAmelCase__								=  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:
               UpperCAmelCase__								=  self.feature_extractor(_UpperCamelCase       ,  *_UpperCamelCase       ,  sampling_rate=_UpperCamelCase       ,  **_UpperCamelCase		)
         if text is not None:
               UpperCAmelCase__								=  self.tokenizer(_UpperCamelCase       ,  **_UpperCamelCase		)

         if text is None:
               return inputs
         elif audio is None:
               return encodings
         else:
               UpperCAmelCase__								=  encodings["input_ids"]
               return inputs


   def 			lowercase_      (self     :						Optional[Any]       ,  *__UpperCAmelCase     :						Dict       ,  **__UpperCAmelCase     :						Dict		)						->				Dict:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Optional[Any]       ,  *__UpperCAmelCase     :						Tuple       ,  **__UpperCAmelCase     :						Dict		)						->				Optional[int]:
         """simple docstring"""



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





   @contextmanager
   def 			lowercase_      (self     :						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."		)
         UpperCAmelCase__								=  True
         UpperCAmelCase__								=  self.tokenizer
         yield
         UpperCAmelCase__								=  self.feature_extractor
         UpperCAmelCase__								=  False










from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				cva.getAffineTransform(lowerCamelCase					,				lowerCamelCase )
      return cva.warpAffine(lowerCamelCase					,				lowerCamelCase					,				(rows, cols) )


if __name__ == "__main__":
       # read original image
       snake_case__					:							List[Any]													=     cva.imread(
           str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
       )
       # turn image in gray scale value
       snake_case__					:							List[str]													=     cva.cvtColor(image, cva.COLOR_BGR2GRAY)
       # get image shape
       snake_case__						,    snake_case__					:							str													=     gray_img.shape

       # set different points to rotate image
       snake_case__					:							Any													=     np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
       snake_case__					:							str													=     np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
       snake_case__					:							int													=     np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
       snake_case__					:							List[str]													=     np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

       # add all rotated images in a list
       snake_case__					:							Optional[Any]													=     [
           gray_img,
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
       ]

       # plot different image rotations
       snake_case__					:							Optional[Any]													=     plt.figure(1)
       snake_case__					:							Dict													=     ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
       for i, image in enumerate(images):
              plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
              plt.title(titles[i])
              plt.axis('off')
              plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
       plt.show()





