


from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__UpperCamelCase     :    List[str] 	=				logging.get_logger(__name__)



class 				_UpperCamelCase			(    _a    ):

   '''simple docstring'''







   a_					:				List[Any]     =			['''pixel_values''']

   def __init__(	self	:			Union[str, Any]							,	_lowerCamelCase	:			Dict = True							,	_lowerCamelCase	:			List[str] = 1 / 2_5_5							,	_lowerCamelCase	:			int = True							,	_lowerCamelCase	:			Optional[int] = 8							,	**_lowerCamelCase	:			Tuple							,	):






         '''simple docstring'''



         super().__init__(**__UpperCamelCase				)

         __lowerCamelCase  :   Optional[int]							    =    do_rescale
         __lowerCamelCase  :   int							    =    rescale_factor
         __lowerCamelCase  :   str							    =    do_pad
         __lowerCamelCase  :   Union[str, Any]							    =    pad_size

   def 	_snake_case     (	self	:			Optional[int]							,	_lowerCamelCase	:			Optional[int]							,	_lowerCamelCase	:			List[str]							,	_lowerCamelCase	:			Union[str, Any] = None							,	**_lowerCamelCase	:			List[Any]				):






         '''simple docstring'''



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

   def 	_snake_case     (	self	:			List[Any]							,	_lowerCamelCase	:			Dict							,	_lowerCamelCase	:			Union[str, Any]							,	_lowerCamelCase	:			Dict = None				):






         '''simple docstring'''



         __lowerCamelCase    ,			__lowerCamelCase  :   Optional[int]							    =    get_image_size(__UpperCamelCase				)
         __lowerCamelCase  :   int							    =    (old_height // size + 1) * size - old_height
         __lowerCamelCase  :   str							    =    (old_width // size + 1) * size - old_width

         return pad(__UpperCamelCase							,	((0, pad_height), (0, pad_width))							,	mode="""symmetric"""							,	data_format=__UpperCamelCase				)


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






         '''simple docstring'''



         __lowerCamelCase  :   int							    =    do_rescale if do_rescale is not None else self.do_rescale
         __lowerCamelCase  :   Optional[Any]							    =    rescale_factor if rescale_factor is not None else self.rescale_factor
         __lowerCamelCase  :   List[Any]							    =    do_pad if do_pad is not None else self.do_pad
         __lowerCamelCase  :   Any							    =    pad_size if pad_size is not None else self.pad_size

         __lowerCamelCase  :   List[Any]							    =    make_list_of_images(__UpperCamelCase				)

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

         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.
         __lowerCamelCase  :   Dict							    =    [to_numpy_array(__UpperCamelCase				) for image in images]

         if do_rescale:
               __lowerCamelCase  :   Optional[int]							    =    [self.rescale(image=__UpperCamelCase							,	scale=__UpperCamelCase				) for image in images]

         if do_pad:
               __lowerCamelCase  :   Any							    =    [self.pad(__UpperCamelCase							,	size=__UpperCamelCase				) for image in images]

         __lowerCamelCase  :   Any							    =    [to_channel_dimension_format(__UpperCamelCase							,	__UpperCamelCase				) for image in images]

         __lowerCamelCase  :   Any							    =    {"""pixel_values""": images}
         return BatchFeature(data=__UpperCamelCase							,	tensor_type=__UpperCamelCase				)





from ...processing_utils import ProcessorMixin



class    a_ (			_a    ):
			a	:			Optional[int]					   =							'''SpeechT5FeatureExtractor'''
			a	:			List[Any]					   =							'''SpeechT5Tokenizer'''






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									super().__init__(__UpperCamelCase				,			__UpperCamelCase  )






			def __call__(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					kwargs.pop("""audio"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""text"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""text_target"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""audio_target"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""sampling_rate"""				,			__UpperCamelCase  )

									if audio is not None and text is not None:
															raise ValueError(
															    """Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"""  )
									if audio_target is not None and text_target is not None:
															raise ValueError(
															    """Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"""  )
									if audio is None and audio_target is None and text is None and text_target is None:
															raise ValueError(
															    """You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."""  )

									if audio is not None:
															_lowercase					       =					self.feature_extractor(__UpperCamelCase				,			*__UpperCamelCase				,			sampling_rate=__UpperCamelCase				,			**__UpperCamelCase  )
									elif text is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			**__UpperCamelCase  )
									else:
															_lowercase					       =					None

									if audio_target is not None:
															_lowercase					       =					self.feature_extractor(audio_target=__UpperCamelCase				,			*__UpperCamelCase				,			sampling_rate=__UpperCamelCase				,			**__UpperCamelCase  )
															_lowercase					       =					targets["""input_values"""]
									elif text_target is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			**__UpperCamelCase  )
															_lowercase					       =					targets["""input_ids"""]
									else:
															_lowercase					       =					None

									if inputs is None:
															return targets

									if targets is not None:
															_lowercase					       =					labels

															_lowercase					       =					targets.get("""attention_mask"""  )
															if decoder_attention_mask is not None:
																					_lowercase					       =					decoder_attention_mask

									return inputs






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					kwargs.pop("""input_values"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""input_ids"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""labels"""				,			__UpperCamelCase  )

									if input_values is not None and input_ids is not None:
															raise ValueError("""Cannot process both `input_values` and `input_ids` inputs."""  )
									if input_values is None and input_ids is None and labels is None:
															raise ValueError(
															    """You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."""  )

									if input_values is not None:
															_lowercase					       =					self.feature_extractor.pad(__UpperCamelCase				,			*__UpperCamelCase				,			**__UpperCamelCase  )
									elif input_ids is not None:
															_lowercase					       =					self.tokenizer.pad(__UpperCamelCase				,			**__UpperCamelCase  )
									else:
															_lowercase					       =					None

									if labels is not None:
															if "input_ids" in labels or (isinstance(__UpperCamelCase				,			__UpperCamelCase  ) and "input_ids" in labels[0]):
																					_lowercase					       =					self.tokenizer.pad(__UpperCamelCase				,			**__UpperCamelCase  )
																					_lowercase					       =					targets["""input_ids"""]
															else:
																					_lowercase					       =					self.feature_extractor.feature_size
																					_lowercase					       =					self.feature_extractor.num_mel_bins
																					_lowercase					       =					self.feature_extractor.pad(__UpperCamelCase				,			*__UpperCamelCase				,			**__UpperCamelCase  )
																					_lowercase					       =					feature_size_hack
																					_lowercase					       =					targets["""input_values"""]
									else:
															_lowercase					       =					None

									if inputs is None:
															return targets

									if targets is not None:
															_lowercase					       =					labels

															_lowercase					       =					targets.get("""attention_mask"""  )
															if decoder_attention_mask is not None:
																					_lowercase					       =					decoder_attention_mask

									return inputs






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.batch_decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.decode(*__UpperCamelCase				,			**__UpperCamelCase  )