

'''simple docstring'''

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class        A_ (				_UpperCAmelCase							):






     '''simple docstring'''



     UpperCAmelCase_						:	Optional[Any]  		=  """ClapFeatureExtractor"""
     UpperCAmelCase_						:	str  		=  ("""RobertaTokenizer""", """RobertaTokenizerFast""")


     def __init__(		self :     List[str]     ,			lowercase_ :     Optional[int]     ,			lowercase_ :     Optional[int]       )						->	Dict:
      super().__init__(UpperCAmelCase_     ,			UpperCAmelCase_       )


     def __call__(		self :     str     ,			lowercase_ :     Dict=None     ,			lowercase_ :     str=None     ,			lowercase_ :     Tuple=None     ,			**lowercase_ :     str       )						->	Optional[Any]:
      UpperCAmelCase       :	Any											= kwargs.pop('sampling_rate'     ,			UpperCAmelCase_       )

      if text is None and audios is None:
       raise ValueError('You have to specify either text or audios. Both cannot be none.'       )

      if text is not None:
       UpperCAmelCase       :	Tuple											= self.tokenizer(UpperCAmelCase_     ,			return_tensors=UpperCAmelCase_     ,			**UpperCAmelCase_       )

      if audios is not None:
       UpperCAmelCase       :	List[Any]											= self.feature_extractor(
           UpperCAmelCase_     ,			sampling_rate=UpperCAmelCase_     ,			return_tensors=UpperCAmelCase_     ,			**UpperCAmelCase_       )

      if text is not None and audios is not None:
       UpperCAmelCase       :	Dict											= audio_features.input_features
       return encoding
      elif text is not None:
       return encoding
      else:
       return BatchEncoding(data=dict(**UpperCAmelCase_       )     ,			tensor_type=UpperCAmelCase_       )


     def    UpperCAmelCase_       (		self :     Union[str, Any]     ,			*lowercase_ :     int     ,			**lowercase_ :     int       )						->	Any:
      return self.tokenizer.batch_decode(*UpperCAmelCase_     ,			**UpperCAmelCase_       )


     def    UpperCAmelCase_       (		self :     Optional[Any]     ,			*lowercase_ :     Optional[Any]     ,			**lowercase_ :     List[Any]       )						->	Optional[Any]:
      return self.tokenizer.decode(*UpperCAmelCase_     ,			**UpperCAmelCase_       )




     @property
     def    UpperCAmelCase_       (		self :     List[str]       )						->	List[str]:
      UpperCAmelCase       :	str											= self.tokenizer.model_input_names
      UpperCAmelCase       :	List[Any]											= self.feature_extractor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names       )       )






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case								=  logging.get_logger(__name__)




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   List[str]				       =["""pixel_values"""]
	def __init__(			self							:   Dict       ,   UpperCAmelCase_							:   bool = True       ,   UpperCAmelCase_							:   int = 32       ,   UpperCAmelCase_							:   int=PILImageResampling.BILINEAR       ,   UpperCAmelCase_							:   bool = True       ,   **UpperCAmelCase_							:   Any       ,   ):
						SCREAMING_SNAKE_CASE__													= do_resize
						SCREAMING_SNAKE_CASE__													= do_rescale
						SCREAMING_SNAKE_CASE__													= size_divisor
						SCREAMING_SNAKE_CASE__													= resample
						super().__init__(**UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   int       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Optional[ChannelDimension] = None       ,   **UpperCAmelCase_							:   Dict							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= get_image_size(UpperCAmelCase_							)
						# Rounds the height and width down to the closest multiple of size_divisor
						SCREAMING_SNAKE_CASE__													= height // size_divisor * size_divisor
						SCREAMING_SNAKE_CASE__													= width // size_divisor * size_divisor
						SCREAMING_SNAKE_CASE__													= resize(UpperCAmelCase_       ,   (new_h, new_w)       ,   resample=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)
						return image
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   float       ,   UpperCAmelCase_							:   Optional[ChannelDimension] = None       ,   **UpperCAmelCase_							:   List[str]							):
						return rescale(image=UpperCAmelCase_       ,   scale=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)





	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   UpperCAmelCase_							:   Optional[int] = None       ,   UpperCAmelCase_							:   List[str]=None       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   UpperCAmelCase_							:   Optional[Union[TensorType, str]] = None       ,   UpperCAmelCase_							:   ChannelDimension = ChannelDimension.FIRST       ,   **UpperCAmelCase_							:   List[Any]       ,   ):
						SCREAMING_SNAKE_CASE__													= do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE__													= do_rescale if do_rescale is not None else self.do_rescale
						SCREAMING_SNAKE_CASE__													= size_divisor if size_divisor is not None else self.size_divisor
						SCREAMING_SNAKE_CASE__													= resample if resample is not None else self.resample

						if do_resize and size_divisor is None:
											raise ValueError('size_divisor is required for resizing'							)

						SCREAMING_SNAKE_CASE__													= make_list_of_images(UpperCAmelCase_							)

						if not valid_images(UpperCAmelCase_							):
											raise ValueError('Invalid image(s)'							)

						# All transformations expect numpy arrays.
						SCREAMING_SNAKE_CASE__													= [to_numpy_array(UpperCAmelCase_							) for img in images]

						if do_resize:
											SCREAMING_SNAKE_CASE__													= [self.resize(UpperCAmelCase_       ,   size_divisor=UpperCAmelCase_       ,   resample=UpperCAmelCase_							) for image in images]

						if do_rescale:
											SCREAMING_SNAKE_CASE__													= [self.rescale(UpperCAmelCase_       ,   scale=1 / 255							) for image in images]

						SCREAMING_SNAKE_CASE__													= [to_channel_dimension_format(UpperCAmelCase_       ,   UpperCAmelCase_							) for image in images]

						SCREAMING_SNAKE_CASE__													= {'pixel_values': images}
						return BatchFeature(data=UpperCAmelCase_       ,   tensor_type=UpperCAmelCase_							)





