






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	['''image_processor''', '''tokenizer''']
				a__       							=	'''LayoutLMv3ImageProcessor'''
				a__       							=	('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')







				def __init__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''							,       __lowerCAmelCase							,       )
												__magic_name__		:Union[str, Any]   =   kwargs.pop('''feature_extractor'''  )

								__magic_name__		:List[str]   =   image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
												raise ValueError('''You need to specify an `image_processor`.'''  )
								if tokenizer is None:
												raise ValueError('''You need to specify a `tokenizer`.'''  )

								super().__init__(__lowerCAmelCase							,       __lowerCAmelCase  )







				def __call__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = True							,       __lowerCAmelCase = False							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = 0							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = False							,       __lowerCAmelCase = False							,       __lowerCAmelCase = False							,       __lowerCAmelCase = False							,       __lowerCAmelCase = True							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								# verify input
								if self.image_processor.apply_ocr and (boxes is not None):
												raise ValueError(
												    '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''  )

								if self.image_processor.apply_ocr and (word_labels is not None):
												raise ValueError(
												    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''  )

								# first, apply the image processor
								__magic_name__		:Union[str, Any]   =   self.image_processor(images=__lowerCAmelCase							,       return_tensors=__lowerCAmelCase  )

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
												if isinstance(__lowerCAmelCase							,       __lowerCAmelCase  ):
																__magic_name__		:str   =   [text]  # add batch dimension (as the image processor always adds a batch dimension)
												__magic_name__		:Union[str, Any]   =   features['''words''']

								__magic_name__		:Dict   =   self.tokenizer(
								    text=text if text is not None else features['''words''']							,       text_pair=text_pair if text_pair is not None else None							,       boxes=boxes if boxes is not None else features['''boxes''']							,       word_labels=__lowerCAmelCase							,       add_special_tokens=__lowerCAmelCase							,       padding=__lowerCAmelCase							,       truncation=__lowerCAmelCase							,       max_length=__lowerCAmelCase							,       stride=__lowerCAmelCase							,       pad_to_multiple_of=__lowerCAmelCase							,       return_token_type_ids=__lowerCAmelCase							,       return_attention_mask=__lowerCAmelCase							,       return_overflowing_tokens=__lowerCAmelCase							,       return_special_tokens_mask=__lowerCAmelCase							,       return_offsets_mapping=__lowerCAmelCase							,       return_length=__lowerCAmelCase							,       verbose=__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase							,       )

								# add pixel values
								__magic_name__		:List[str]   =   features.pop('''pixel_values'''  )
								if return_overflowing_tokens is True:
												__magic_name__		:Any   =   self.get_overflowing_images(__lowerCAmelCase							,       encoded_inputs['''overflow_to_sample_mapping''']  )
								__magic_name__		:List[str]   =   images

								return encoded_inputs







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								__magic_name__		:Optional[int]   =   []
								for sample_idx in overflow_to_sample_mapping:
												images_with_overflow.append(images[sample_idx]  )

								if len(__lowerCAmelCase  ) != len(__lowerCAmelCase  ):
												raise ValueError(
												    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
												    F''' {len(__lowerCAmelCase  )} and {len(__lowerCAmelCase  )}'''  )

								return images_with_overflow







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.batch_decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								return ["input_ids", "bbox", "attention_mask", "pixel_values"]







				@property
				def       A						(    self  ):
								"""simple docstring"""
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,       __lowerCAmelCase							,       )
								return self.image_processor_class







				@property
				def       A						(    self  ):
								"""simple docstring"""
								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,       __lowerCAmelCase							,       )
								return self.image_processor








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  )