






"""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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__a       :	str             = logging.get_logger(__name__)







class        _SCREAMING_SNAKE_CASE						(  __lowercase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =['pixel_values']




							def __init__(   self:     Optional[int]				,		__A:     int = True				,		__A:     List[str] = None				,		__A:     List[str] = PILImageResampling.BICUBIC				,		__A:     Optional[int] = True				,		__A:     List[Any] = True				,		__A:     Tuple = 1 / 255				,		__A:     Optional[Any] = None				,		__A:     Optional[int] = True				,		__A:     Tuple = None				,		__A:     Optional[int] = None				,		**__A:     List[str]				,		):

												'''simple docstring'''

												super().__init__(**__A    )
												a__          =  size if size is not None else {'''height''': 224, '''width''': 224}
												a__          =  get_size_dict(__A    )
												a__          =  crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
												a__          =  get_size_dict(__A				,		default_to_square=__A				,		param_name='''crop_size'''    )

												a__          =  do_resize
												a__          =  do_rescale
												a__          =  do_normalize
												a__          =  do_center_crop
												a__          =  crop_size
												a__          =  size
												a__          =  resample
												a__          =  rescale_factor
												a__          =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												a__          =  image_std if image_std is not None else IMAGENET_DEFAULT_STD




							def  lowercase		(   self:     List[Any]				,		__A:     int				,		__A:     List[Any]				,		__A:     Optional[Any] = PILImageResampling.BILINEAR				,		__A:     Union[str, Any] = None				,		**__A:     Tuple				,		):

												'''simple docstring'''

												a__          =  get_size_dict(__A    )

												if "shortest_edge" in size:
																	a__          =  get_resize_output_image_size(__A				,		size=size['''shortest_edge''']				,		default_to_square=__A    )
																	# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
												elif "height" in size and "width" in size:
																	a__          =  (size['''height'''], size['''width'''])
												else:
																	raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'    )
												return resize(__A				,		size=__A				,		resample=__A				,		data_format=__A				,		**__A    )




							def  lowercase		(   self:     Any				,		__A:     str				,		__A:     str				,		__A:     List[str] = None				,		**__A:     List[str]				,		):

												'''simple docstring'''

												a__          =  get_size_dict(__A    )
												if "height" not in size or "width" not in size:
																	raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'    )
												return center_crop(__A				,		size=(size['''height'''], size['''width'''])				,		data_format=__A				,		**__A    )




							def  lowercase		(   self:     Dict				,		__A:     List[str]				,		__A:     Dict				,		__A:     List[str] = None				,		**__A:     Dict    ):

												'''simple docstring'''

												return rescale(__A				,		scale=__A				,		data_format=__A				,		**__A    )




							def  lowercase		(   self:     Optional[Any]				,		__A:     int				,		__A:     Any				,		__A:     List[Any]				,		__A:     str = None				,		**__A:     List[Any]				,		):

												'''simple docstring'''

												return normalize(__A				,		mean=__A				,		std=__A				,		data_format=__A				,		**__A    )





							def  lowercase		(   self:     List[str]				,		__A:     Dict				,		__A:     int = None				,		__A:     Tuple = None				,		__A:     Tuple = None				,		__A:     str = None				,		__A:     Optional[int] = None				,		__A:     List[str] = None				,		__A:     str = None				,		__A:     Any = None				,		__A:     Tuple = None				,		__A:     Optional[Any] = None				,		__A:     List[str] = None				,		__A:     Optional[int] = ChannelDimension.FIRST				,		**__A:     Optional[int]				,		):

												'''simple docstring'''

												a__          =  do_resize if do_resize is not None else self.do_resize
												a__          =  do_rescale if do_rescale is not None else self.do_rescale
												a__          =  do_normalize if do_normalize is not None else self.do_normalize
												a__          =  do_center_crop if do_center_crop is not None else self.do_center_crop
												a__          =  crop_size if crop_size is not None else self.crop_size
												a__          =  get_size_dict(__A				,		param_name='''crop_size'''				,		default_to_square=__A    )
												a__          =  resample if resample is not None else self.resample
												a__          =  rescale_factor if rescale_factor is not None else self.rescale_factor
												a__          =  image_mean if image_mean is not None else self.image_mean
												a__          =  image_std if image_std is not None else self.image_std

												a__          =  size if size is not None else self.size
												a__          =  get_size_dict(__A    )

												if not is_batched(__A    ):
																	a__          =  [images]

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

												if do_resize and size is None:
																	raise ValueError('''Size 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.'''    )

												# All transformations expect numpy arrays.
												a__          =  [to_numpy_array(__A    ) for image in images]

												if do_resize:
																	a__          =  [self.resize(image=__A				,		size=__A				,		resample=__A    ) for image in images]

												if do_center_crop:
																	a__          =  [self.center_crop(image=__A				,		size=__A    ) for image in images]

												if do_rescale:
																	a__          =  [self.rescale(image=__A				,		scale=__A    ) for image in images]

												if do_normalize:
																	a__          =  [self.normalize(image=__A				,		mean=__A				,		std=__A    ) for image in images]

												a__          =  [to_channel_dimension_format(__A				,		__A    ) for image in images]

												a__          =  {'''pixel_values''': images}
												return BatchFeature(data=__A				,		tensor_type=__A    )










"""simple docstring"""







from typing import List, Optional

import numpy as np

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







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





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




							def __init__(   self:     List[Any]				,		__A:     Any				,		__A:     Dict    ):

												'''simple docstring'''

												super().__init__(__A				,		__A    )
												a__          =  self.feature_extractor
												a__          =  False




							def  lowercase		(   self:     Union[str, Any]				,		__A:     List[Any]=None				,		__A:     Optional[Any]=None				,		__A:     List[Any]=True    ):

												'''simple docstring'''

												return self.tokenizer.get_decoder_prompt_ids(task=__A				,		language=__A				,		no_timestamps=__A    )




							def __call__(   self:     Union[str, Any]				,		*__A:     int				,		**__A:     Dict    ):

												'''simple docstring'''

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

												a__          =  kwargs.pop('''audio'''				,		__A    )
												a__          =  kwargs.pop('''sampling_rate'''				,		__A    )
												a__          =  kwargs.pop('''text'''				,		__A    )
												if len(__A    ) > 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 text is not None:
																	a__          =  self.tokenizer(__A				,		**__A    )

												if audio is not None:
																	a__          =  self.feature_extractor(__A				,		*__A				,		sampling_rate=__A				,		**__A    )

												if audio is None:
																	return inputs

												elif text is None:
																	return audio_inputs

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




							def  lowercase		(   self:     Union[str, Any]				,		*__A:     List[str]				,		**__A:     Tuple    ):

												'''simple docstring'''

												a__          =  kwargs.pop('''audio'''				,		__A    )
												a__          =  kwargs.pop('''padding_mask'''				,		__A    )

												if len(__A    ) > 0:
																	a__          =  args[0]
																	a__          =  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  lowercase		(   self:     Union[str, Any]				,		*__A:     Optional[int]				,		**__A:     Optional[Any]    ):

												'''simple docstring'''

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





							def  lowercase		(   self:     Union[str, Any]				,		__A:     Dict				,		__A:     Optional = None    ):

												'''simple docstring'''

												a__          =  to_numpy(__A    )
												a__       ,a__       ,a__          =  audio_values.shape

												if padding_mask is None:
																	return list(__A    )

												a__          =  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)
												a__          =  seq_len - padding_mask.shape[-1]
												a__          =  1 - self.feature_extractor.padding_value
												a__          =  np.pad(__A				,		((0, 0), (0, difference))				,		'''constant'''				,		constant_values=__A    )

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

												return audio_values


