



"""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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


_lowercase			:			Any										=						logging.get_logger(__name__)







class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							['pixel_values']



					def __init__(	self							:  Any,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Dict[str, int] = None,  lowerCamelCase							:  PILImageResampling = PIL.Image.BICUBIC,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Dict[str, int] = None,  lowerCamelCase							:  Union[int, float] = 1 / 255,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Optional[Union[float, List[float]]] = None,  lowerCamelCase							:  Optional[Union[float, List[float]]] = None,  **lowerCamelCase							:  Any,  )-> None:
								super().__init__(**lowerCamelCase					)
								lowerCamelCase__		:		Any		     =size if size is not None else {'''height''': 256, '''width''': 256}
								lowerCamelCase__		:		Optional[int]		     =get_size_dict(lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								lowerCamelCase__		:		Tuple		     =get_size_dict(lowerCamelCase,  param_name='''crop_size'''					)

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



					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  np.ndarray,  lowerCamelCase							:  Dict[str, int],  lowerCamelCase							:  PILImageResampling = PIL.Image.BICUBIC,  lowerCamelCase							:  Optional[Union[str, ChannelDimension]] = None,  **lowerCamelCase							:  List[str],  )-> np.ndarray:
								lowerCamelCase__		:		Dict		     =get_size_dict(lowerCamelCase					)
								if "height" not in size or "width" not in size:
											raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''					)
								return resize(
								    lowerCamelCase,  size=(size['''height'''], size['''width''']),  resample=lowerCamelCase,  data_format=lowerCamelCase,  **lowerCamelCase					)



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



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



					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  np.ndarray,  lowerCamelCase							:  Union[float, List[float]],  lowerCamelCase							:  Union[float, List[float]],  lowerCamelCase							:  Optional[Union[str, ChannelDimension]] = None,  **lowerCamelCase							:  Dict,  )-> np.ndarray:
								return normalize(lowerCamelCase,  mean=lowerCamelCase,  std=lowerCamelCase,  data_format=lowerCamelCase,  **lowerCamelCase					)




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

								lowerCamelCase__		:		Tuple		     =size if size is not None else self.size
								lowerCamelCase__		:		int		     =get_size_dict(lowerCamelCase					)
								lowerCamelCase__		:		Any		     =crop_size if crop_size is not None else self.crop_size
								lowerCamelCase__		:		List[Any]		     =get_size_dict(lowerCamelCase,  param_name='''crop_size'''					)

								lowerCamelCase__		:		Optional[int]		     =make_list_of_images(lowerCamelCase					)

								if not valid_images(lowerCamelCase					):
											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 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.
								lowerCamelCase__		:		List[str]		     =[to_numpy_array(lowerCamelCase					) for image in images]

								if do_resize:
											lowerCamelCase__		:		Any		     =[self.resize(image=lowerCamelCase,  size=lowerCamelCase,  resample=lowerCamelCase					) for image in images]

								if do_center_crop:
											lowerCamelCase__		:		List[Any]		     =[self.center_crop(image=lowerCamelCase,  size=lowerCamelCase					) for image in images]

								if do_rescale:
											lowerCamelCase__		:		Tuple		     =[self.rescale(image=lowerCamelCase,  scale=lowerCamelCase					) for image in images]

								if do_normalize:
											lowerCamelCase__		:		int		     =[self.normalize(image=lowerCamelCase,  mean=lowerCamelCase,  std=lowerCamelCase					) for image in images]

								lowerCamelCase__		:		List[Any]		     =[to_channel_dimension_format(lowerCamelCase,  lowerCamelCase					) for image in images]

								lowerCamelCase__		:		Optional[Any]		     ={'''pixel_values''': images}
								return BatchFeature(data=lowerCamelCase,  tensor_type=lowerCamelCase					)









"""simple docstring"""






from ...processing_utils import ProcessorMixin

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'SpeechT5FeatureExtractor'
					_a             =							'SpeechT5Tokenizer'



					def __init__(	self							:  Dict,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  str					)-> Any:
								super().__init__(lowerCamelCase,  lowerCamelCase					)



					def __call__(	self							:  Tuple,  *lowerCamelCase							:  List[str],  **lowerCamelCase							:  Optional[int]					)-> List[str]:
								lowerCamelCase__		:		List[Any]		     =kwargs.pop('''audio''',  lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =kwargs.pop('''text''',  lowerCamelCase					)
								lowerCamelCase__		:		int		     =kwargs.pop('''text_target''',  lowerCamelCase					)
								lowerCamelCase__		:		Dict		     =kwargs.pop('''audio_target''',  lowerCamelCase					)
								lowerCamelCase__		:		Any		     =kwargs.pop('''sampling_rate''',  lowerCamelCase					)

								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:
											lowerCamelCase__		:		Union[str, Any]		     =self.feature_extractor(lowerCamelCase,  *lowerCamelCase,  sampling_rate=lowerCamelCase,  **lowerCamelCase					)
								elif text is not None:
											lowerCamelCase__		:		List[Any]		     =self.tokenizer(lowerCamelCase,  **lowerCamelCase					)
								else:
											lowerCamelCase__		:		Any		     =None

								if audio_target is not None:
											lowerCamelCase__		:		List[str]		     =self.feature_extractor(audio_target=lowerCamelCase,  *lowerCamelCase,  sampling_rate=lowerCamelCase,  **lowerCamelCase					)
											lowerCamelCase__		:		Tuple		     =targets['''input_values''']
								elif text_target is not None:
											lowerCamelCase__		:		Dict		     =self.tokenizer(lowerCamelCase,  **lowerCamelCase					)
											lowerCamelCase__		:		int		     =targets['''input_ids''']
								else:
											lowerCamelCase__		:		List[str]		     =None

								if inputs is None:
											return targets

								if targets is not None:
											lowerCamelCase__		:		Dict		     =labels

											lowerCamelCase__		:		Any		     =targets.get('''attention_mask'''					)
											if decoder_attention_mask is not None:
														lowerCamelCase__		:		Dict		     =decoder_attention_mask

								return inputs



					def 		snake_case	(	self							:  int,  *lowerCamelCase							:  Optional[Any],  **lowerCamelCase							:  Optional[int]					)-> Optional[Any]:
								lowerCamelCase__		:		List[Any]		     =kwargs.pop('''input_values''',  lowerCamelCase					)
								lowerCamelCase__		:		Union[str, Any]		     =kwargs.pop('''input_ids''',  lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =kwargs.pop('''labels''',  lowerCamelCase					)

								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:
											lowerCamelCase__		:		List[str]		     =self.feature_extractor.pad(lowerCamelCase,  *lowerCamelCase,  **lowerCamelCase					)
								elif input_ids is not None:
											lowerCamelCase__		:		Tuple		     =self.tokenizer.pad(lowerCamelCase,  **lowerCamelCase					)
								else:
											lowerCamelCase__		:		Any		     =None

								if labels is not None:
											if "input_ids" in labels or (isinstance(lowerCamelCase,  lowerCamelCase					) and "input_ids" in labels[0]):
														lowerCamelCase__		:		str		     =self.tokenizer.pad(lowerCamelCase,  **lowerCamelCase					)
														lowerCamelCase__		:		List[Any]		     =targets['''input_ids''']
											else:
														lowerCamelCase__		:		Any		     =self.feature_extractor.feature_size
														lowerCamelCase__		:		Optional[Any]		     =self.feature_extractor.num_mel_bins
														lowerCamelCase__		:		Optional[int]		     =self.feature_extractor.pad(lowerCamelCase,  *lowerCamelCase,  **lowerCamelCase					)
														lowerCamelCase__		:		List[Any]		     =feature_size_hack
														lowerCamelCase__		:		Tuple		     =targets['''input_values''']
								else:
											lowerCamelCase__		:		Optional[Any]		     =None

								if inputs is None:
											return targets

								if targets is not None:
											lowerCamelCase__		:		Tuple		     =labels

											lowerCamelCase__		:		Optional[int]		     =targets.get('''attention_mask'''					)
											if decoder_attention_mask is not None:
														lowerCamelCase__		:		Optional[Any]		     =decoder_attention_mask

								return inputs



					def 		snake_case	(	self							:  List[str],  *lowerCamelCase							:  Union[str, Any],  **lowerCamelCase							:  List[Any]					)-> List[Any]:
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase					)




					def 		snake_case	(	self							:  List[str],  *lowerCamelCase							:  List[Any],  **lowerCamelCase							:  Tuple					)-> int:
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase					)




