






"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


__lowerCamelCase							=  logging.get_logger(__name__)



def   a    (     __UpperCAmelCase      :    List[str]  )    ->							List[List[ImageInput]]:
						if isinstance(__UpperCAmelCase      ,   (list, tuple)  ) and isinstance(videos[0]      ,   (list, tuple)  ) and is_valid_image(videos[0][0]  ):
												return videos

						elif isinstance(__UpperCAmelCase      ,   (list, tuple)  ) and is_valid_image(videos[0]  ):
												return [videos]

						elif is_valid_image(__UpperCAmelCase  ):
												return [[videos]]

						raise ValueError(f'Could not make batched video from {videos}'  )



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				["pixel_values"]

					def __init__(			self  :      str     ,		__snake_case  :      bool = True     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      PILImageResampling = PILImageResampling.BILINEAR     ,		__snake_case  :      bool = True     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      bool = True     ,		__snake_case  :      Union[int, float] = 1 / 2_5_5     ,		__snake_case  :      bool = True     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		**__snake_case  :      List[str]     ,		)			->							None:
											super().__init__(**__snake_case					)
											__magic_name__:	Tuple							=       size if size is not None else {"""shortest_edge""": 2_2_4}
											__magic_name__:	str							=       get_size_dict(__snake_case     ,		default_to_square=__snake_case					)
											__magic_name__:	Optional[int]							=       crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
											__magic_name__:	str							=       get_size_dict(__snake_case     ,		param_name="""crop_size"""					)

											__magic_name__:	List[str]							=       do_resize
											__magic_name__:	Optional[int]							=       size
											__magic_name__:	Dict							=       do_center_crop
											__magic_name__:	Any							=       crop_size
											__magic_name__:	List[str]							=       resample
											__magic_name__:	str							=       do_rescale
											__magic_name__:	int							=       rescale_factor
											__magic_name__:	List[str]							=       do_normalize
											__magic_name__:	Any							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__magic_name__:	List[str]							=       image_std if image_std is not None else IMAGENET_STANDARD_STD

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      np.ndarray     ,		__snake_case  :      Dict[str, int]     ,		__snake_case  :      PILImageResampling = PILImageResampling.BILINEAR     ,		__snake_case  :      Optional[Union[str, ChannelDimension]] = None     ,		**__snake_case  :      Optional[Any]     ,		)			->							np.ndarray:
											__magic_name__:	Optional[Any]							=       get_size_dict(__snake_case     ,		default_to_square=__snake_case					)
											if "shortest_edge" in size:
																	__magic_name__:	Tuple							=       get_resize_output_image_size(__snake_case     ,		size["""shortest_edge"""]     ,		default_to_square=__snake_case					)
											elif "height" in size and "width" in size:
																	__magic_name__:	Optional[Any]							=       (size["""height"""], size["""width"""])
											else:
																	raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'					)
											return resize(__snake_case     ,		size=__snake_case     ,		resample=__snake_case     ,		data_format=__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      np.ndarray     ,		__snake_case  :      Dict[str, int]     ,		__snake_case  :      Optional[Union[str, ChannelDimension]] = None     ,		**__snake_case  :      Optional[int]     ,		)			->							np.ndarray:
											__magic_name__:	str							=       get_size_dict(__snake_case					)
											if "height" not in size or "width" not in size:
																	raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'					)
											return center_crop(__snake_case     ,		size=(size["""height"""], size["""width"""])     ,		data_format=__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      np.ndarray     ,		__snake_case  :      Union[int, float]     ,		__snake_case  :      Optional[Union[str, ChannelDimension]] = None     ,		**__snake_case  :      Optional[Any]     ,		)			->							Union[str, Any]:
											return rescale(__snake_case     ,		scale=__snake_case     ,		data_format=__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      np.ndarray     ,		__snake_case  :      Union[float, List[float]]     ,		__snake_case  :      Union[float, List[float]]     ,		__snake_case  :      Optional[Union[str, ChannelDimension]] = None     ,		**__snake_case  :      Any     ,		)			->							np.ndarray:
											return normalize(__snake_case     ,		mean=__snake_case     ,		std=__snake_case     ,		data_format=__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      ImageInput     ,		__snake_case  :      bool = None     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      PILImageResampling = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      float = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      Optional[ChannelDimension] = ChannelDimension.FIRST     ,		)			->							np.ndarray:
											if do_resize and size is None or resample is None:
																	raise ValueError("""Size and resample must be specified if do_resize is True."""					)

											if do_center_crop and crop_size is None:
																	raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

											if do_rescale and rescale_factor is None:
																	raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

											if do_normalize and (image_mean is None or image_std is None):
																	raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

											# All transformations expect numpy arrays.
											__magic_name__:	int							=       to_numpy_array(__snake_case					)

											if do_resize:
																	__magic_name__:	Union[str, Any]							=       self.resize(image=__snake_case     ,		size=__snake_case     ,		resample=__snake_case					)

											if do_center_crop:
																	__magic_name__:	str							=       self.center_crop(__snake_case     ,		size=__snake_case					)

											if do_rescale:
																	__magic_name__:	str							=       self.rescale(image=__snake_case     ,		scale=__snake_case					)

											if do_normalize:
																	__magic_name__:	List[Any]							=       self.normalize(image=__snake_case     ,		mean=__snake_case     ,		std=__snake_case					)

											__magic_name__:	str							=       to_channel_dimension_format(__snake_case     ,		__snake_case					)
											return image






					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      ImageInput     ,		__snake_case  :      bool = None     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      PILImageResampling = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      float = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      Optional[Union[float, List[float]]] = None     ,		__snake_case  :      Optional[Union[str, TensorType]] = None     ,		__snake_case  :      ChannelDimension = ChannelDimension.FIRST     ,		**__snake_case  :      int     ,		)			->							PIL.Image.Image:
											__magic_name__:	List[Any]							=       do_resize if do_resize is not None else self.do_resize
											__magic_name__:	List[str]							=       resample if resample is not None else self.resample
											__magic_name__:	Optional[int]							=       do_center_crop if do_center_crop is not None else self.do_center_crop
											__magic_name__:	Optional[Any]							=       do_rescale if do_rescale is not None else self.do_rescale
											__magic_name__:	Dict							=       rescale_factor if rescale_factor is not None else self.rescale_factor
											__magic_name__:	Optional[Any]							=       do_normalize if do_normalize is not None else self.do_normalize
											__magic_name__:	Dict							=       image_mean if image_mean is not None else self.image_mean
											__magic_name__:	List[str]							=       image_std if image_std is not None else self.image_std

											__magic_name__:	List[str]							=       size if size is not None else self.size
											__magic_name__:	Tuple							=       get_size_dict(__snake_case     ,		default_to_square=__snake_case					)
											__magic_name__:	Optional[Any]							=       crop_size if crop_size is not None else self.crop_size
											__magic_name__:	List[str]							=       get_size_dict(__snake_case     ,		param_name="""crop_size"""					)

											if not valid_images(__snake_case					):
																	raise ValueError(
																	    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																	    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

											__magic_name__:	Tuple							=       make_batched(__snake_case					)

											__magic_name__:	int							=       [
											    [
											        self._preprocess_image(
											            image=__snake_case     ,		do_resize=__snake_case     ,		size=__snake_case     ,		resample=__snake_case     ,		do_center_crop=__snake_case     ,		crop_size=__snake_case     ,		do_rescale=__snake_case     ,		rescale_factor=__snake_case     ,		do_normalize=__snake_case     ,		image_mean=__snake_case     ,		image_std=__snake_case     ,		data_format=__snake_case     ,		)
											        for img in video
											    ]
											    for video in videos
											]

											__magic_name__:	Tuple							=       {"""pixel_values""": videos}
											return BatchFeature(data=__snake_case     ,		tensor_type=__snake_case					)










from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'EncodecFeatureExtractor'
 __UpperCamelCase       =      ('T5Tokenizer', 'T5TokenizerFast')

 def __init__(  self,		A_,		A_		)	->       Optional[int]:
    super().__init__(A_,		A_		)
    UpperCAmelCase__						=self.feature_extractor
    UpperCAmelCase__						=False

 def  __UpperCAmelCase						(  self,		A_=None,		A_=None,		A_=True		)	->       Union[str, Any]:
    return self.tokenizer.get_decoder_prompt_ids(task=A_,		language=A_,		no_timestamps=A_		)

 def __call__(  self,		*A_,		**A_		)	->       Any:
    # For backward compatibility
    if self._in_target_context_manager:
       return self.current_processor(*A_,		**A_		)

    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("sampling_rate",		A_		)
    UpperCAmelCase__						=kwargs.pop("text",		A_		)
    if len(A_		) > 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 text is not None:
       UpperCAmelCase__						=self.tokenizer(A_,		**A_		)

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

    if audio is None:
       return inputs

    elif text is None:
       return audio_inputs

    else:
       UpperCAmelCase__						=audio_inputs["input_values"]
       if "padding_mask" in audio_inputs:
          UpperCAmelCase__						=audio_inputs["padding_mask"]
       return inputs

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       Dict:
    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("padding_mask",		A_		)

    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=args[1:]

    if audio_values is not None:
       return self._decode_audio(A_,		padding_mask=A_		)
    else:
       return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)






 def  __UpperCAmelCase						(  self,		A_,		A_ = None		)	->       List[np.ndarray]:
    UpperCAmelCase__						=to_numpy(A_		)
    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=audio_values.shape

    if padding_mask is None:
       return list(A_		)

    UpperCAmelCase__						=to_numpy(A_		)

    # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
    # token (so that the generated audio values are **not** treated as padded tokens)
    UpperCAmelCase__						=seq_len - padding_mask.shape[-1]
    UpperCAmelCase__						=1 - self.feature_extractor.padding_value
    UpperCAmelCase__						=np.pad(A_,		((0, 0), (0, difference)),		"constant",		constant_values=A_		)

    UpperCAmelCase__						=audio_values.tolist()
    for i in range(A_		):
       UpperCAmelCase__						=np.asarray(audio_values[i]		)[
           padding_mask[i][None, :] != self.feature_extractor.padding_value
       ]
       UpperCAmelCase__						=sliced_audio.reshape(A_,		-1		)

    return audio_values



