



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 SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      ["image_processor", "tokenizer"]
    __lowerCamelCase			: str					=      "LayoutLMv2ImageProcessor"
    __lowerCamelCase			: str					=      ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")






    def __init__(       self,		lowerCamelCase__=None,		lowerCamelCase__=None,		**lowerCamelCase__		):
        if "feature_extractor" in kwargs:
            warnings.warn(
                """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                """ instead.""",		lowerCamelCase__,		)
            A :       str          =   kwargs.pop("""feature_extractor"""		)

        A :       Union[str, Any]          =   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__,		):
        # 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."""		)

        if return_overflowing_tokens is True and return_offsets_mapping is False:
            raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""		)

        # first, apply the image processor
        A :       List[str]          =   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__		):
                A :       Any          =   [text]  # add batch dimension (as the image processor always adds a batch dimension)
            A :       List[str]          =   features["""words"""]

        A :       Optional[int]          =   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
        A :       Dict          =   features.pop("""pixel_values"""		)
        if return_overflowing_tokens is True:
            A :       Any          =   self.get_overflowing_images(lowerCamelCase__,		encoded_inputs["""overflow_to_sample_mapping"""]		)
        A :       str          =   images

        return encoded_inputs






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
        A :       Dict          =   []
        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 			_lowerCAmelCase					(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        return self.tokenizer.batch_decode(*lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        return self.tokenizer.decode(*lowerCamelCase__,		**lowerCamelCase__		)






    @property
    def 			_lowerCAmelCase					(       self		):
        return ["input_ids", "bbox", "attention_mask", "image"]






    @property
    def 			_lowerCAmelCase					(       self		):
        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 			_lowerCAmelCase					(       self		):
        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""",		lowerCamelCase__,		)
        return self.image_processor







import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_:Dict             =						logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      ["input_features", "is_longer"]






    def __init__(       self,		lowerCamelCase__=64,		lowerCamelCase__=4_8000,		lowerCamelCase__=480,		lowerCamelCase__=10,		lowerCamelCase__=1024,		lowerCamelCase__=0.0,		lowerCamelCase__=False,		lowerCamelCase__ = 0,		lowerCamelCase__ = 1_4000,		lowerCamelCase__ = None,		lowerCamelCase__ = "fusion",		lowerCamelCase__ = "repeatpad",		**lowerCamelCase__,		):
        super().__init__(
            feature_size=lowerCamelCase__,		sampling_rate=lowerCamelCase__,		padding_value=lowerCamelCase__,		return_attention_mask=lowerCamelCase__,		**lowerCamelCase__,		)
        A :       Tuple          =   top_db
        A :       Dict          =   truncation
        A :       int          =   padding
        A :       Optional[Any]          =   fft_window_size
        A :       Optional[Any]          =   (fft_window_size >> 1) + 1
        A :       List[Any]          =   hop_length
        A :       Tuple          =   max_length_s
        A :       str          =   max_length_s * sampling_rate
        A :       List[str]          =   sampling_rate
        A :       Dict          =   frequency_min
        A :       str          =   frequency_max
        A :       Union[str, Any]          =   mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins,		num_mel_filters=lowerCamelCase__,		min_frequency=lowerCamelCase__,		max_frequency=lowerCamelCase__,		sampling_rate=lowerCamelCase__,		norm=lowerCamelCase__,		mel_scale="""htk""",		)
        A :       Union[str, Any]          =   mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins,		num_mel_filters=lowerCamelCase__,		min_frequency=lowerCamelCase__,		max_frequency=lowerCamelCase__,		sampling_rate=lowerCamelCase__,		norm="""slaney""",		mel_scale="""slaney""",		)






    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   copy.deepcopy(self.__dict__		)
        A :       Tuple          =   self.__class__.__name__
        if "mel_filters" in output:
            del output["mel_filters"]
        if "mel_filters_slaney" in output:
            del output["mel_filters_slaney"]
        return output






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        A :       Optional[Any]          =   spectrogram(
            lowerCamelCase__,		window_function(self.fft_window_size,		"""hann"""		),		frame_length=self.fft_window_size,		hop_length=self.hop_length,		power=2.0,		mel_filters=lowerCamelCase__,		log_mel="""dB""",		)
        return log_mel_spectrogram.T






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       Any          =   np.array_split(list(range(0,		total_frames - chunk_frames + 1		)		),		3		)
        if len(ranges[1]		) == 0:
            # if the audio is too short, we just use the first chunk
            A :       Any          =   [0]
        if len(ranges[2]		) == 0:
            # if the audio is too short, we just use the first chunk
            A :       Union[str, Any]          =   [0]
        # randomly choose index for each part
        A :       str          =   np.random.choice(ranges[0]		)
        A :       str          =   np.random.choice(ranges[1]		)
        A :       Tuple          =   np.random.choice(ranges[2]		)

        A :       List[str]          =   mel[idx_front : idx_front + chunk_frames, :]
        A :       Union[str, Any]          =   mel[idx_middle : idx_middle + chunk_frames, :]
        A :       Any          =   mel[idx_back : idx_back + chunk_frames, :]

        A :       Any          =   torch.tensor(mel[None, None, :]		)
        A :       Union[str, Any]          =   torch.nn.functional.interpolate(
            lowerCamelCase__,		size=[chunk_frames, 64],		mode="""bilinear""",		align_corners=lowerCamelCase__		)
        A :       List[Any]          =   mel_shrink[0][0].numpy()
        A :       Any          =   np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back],		axis=0		)
        return mel_fusion






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        if waveform.shape[0] > max_length:
            if truncation == "rand_trunc":
                A :       List[Any]          =   True
                # random crop to max_length (for compatibility) -> this should be handled by self.pad
                A :       int          =   len(lowerCamelCase__		) - max_length
                A :       Optional[int]          =   np.random.randint(0,		overflow + 1		)
                A :       List[Any]          =   waveform[idx : idx + max_length]
                A :       Optional[Any]          =   self._np_extract_fbank_features(lowerCamelCase__,		self.mel_filters_slaney		)[None, :]
            elif truncation == "fusion":
                A :       List[str]          =   self._np_extract_fbank_features(lowerCamelCase__,		self.mel_filters		)
                A :       List[str]          =   max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                A :       Optional[Any]          =   mel.shape[0]
                if chunk_frames == total_frames:
                    # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                    # In this case, we just use the whole audio.
                    A :       str          =   np.stack([mel, mel, mel, mel],		axis=0		)
                    A :       Union[str, Any]          =   False
                else:
                    A :       Dict          =   self._random_mel_fusion(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
                    A :       Tuple          =   True
            else:
                raise NotImplementedError(f'''data_truncating {truncation} not implemented'''		)

        else:
            A :       int          =   False
            # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
            if waveform.shape[0] < max_length:
                if padding == "repeat":
                    A :       Optional[int]          =   int(max_length / len(lowerCamelCase__		)		)
                    A :       Union[str, Any]          =   np.stack(np.tile(lowerCamelCase__,		n_repeat + 1		)		)[:max_length]
                if padding == "repeatpad":
                    A :       Dict          =   int(max_length / len(lowerCamelCase__		)		)
                    A :       Dict          =   np.stack(np.tile(lowerCamelCase__,		lowerCamelCase__		)		)
                A :       Any          =   np.pad(lowerCamelCase__,		(0, max_length - waveform.shape[0]),		mode="""constant""",		constant_values=0		)

            if truncation == "fusion":
                A :       Union[str, Any]          =   self._np_extract_fbank_features(lowerCamelCase__,		self.mel_filters		)
                A :       Tuple          =   np.stack([input_mel, input_mel, input_mel, input_mel],		axis=0		)
            else:
                A :       List[str]          =   self._np_extract_fbank_features(lowerCamelCase__,		self.mel_filters_slaney		)[None, :]

        return input_mel, longer






    def __call__(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A :       Any          =   truncation if truncation is not None else self.truncation
        A :       Union[str, Any]          =   padding if padding else self.padding

        if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                raise ValueError(
                    f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
                    f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
                    f''' was sampled with {self.sampling_rate} and not {sampling_rate}.'''		)
        else:
            logger.warning(
                """It is strongly recommended to pass the `sampling_rate` argument to this function. """
                """Failing to do so can result in silent errors that might be hard to debug."""		)

        A :       str          =   isinstance(lowerCamelCase__,		np.ndarray		) and len(raw_speech.shape		) > 1
        if is_batched_numpy and len(raw_speech.shape		) > 2:
            raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''		)
        A :       Any          =   is_batched_numpy or (
            isinstance(lowerCamelCase__,		(list, tuple)		) and (isinstance(raw_speech[0],		(np.ndarray, tuple, list)		))
        )

        if is_batched:
            A :       Optional[int]          =   [np.asarray(lowerCamelCase__,		dtype=np.floataa		) for speech in raw_speech]
        elif not is_batched and not isinstance(lowerCamelCase__,		np.ndarray		):
            A :       int          =   np.asarray(lowerCamelCase__,		dtype=np.floataa		)
        elif isinstance(lowerCamelCase__,		np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
            A :       int          =   raw_speech.astype(np.floataa		)

        # always return batch
        if not is_batched:
            A :       List[Any]          =   [np.asarray(lowerCamelCase__		)]

        # convert to mel spectrogram, truncate and pad if needed.
        A :       Tuple          =   [
            self._get_input_mel(lowerCamelCase__,		max_length if max_length else self.nb_max_samples,		lowerCamelCase__,		lowerCamelCase__		)
            for waveform in raw_speech
        ]

        A :       Optional[Any]          =   []
        A :       Optional[Any]          =   []
        for mel, longer in padded_inputs:
            input_mel.append(lowerCamelCase__		)
            is_longer.append(lowerCamelCase__		)

        if truncation == "fusion" and sum(lowerCamelCase__		) == 0:
            # if no audio is longer than 10s, then randomly select one audio to be longer
            A :       Tuple          =   np.random.randint(0,		len(lowerCamelCase__		)		)
            A :       str          =   True

        if isinstance(input_mel[0],		lowerCamelCase__		):
            A :       int          =   [np.asarray(lowerCamelCase__,		dtype=np.floataa		) for feature in input_mel]

        # is_longer is a list of bool
        A :       Optional[int]          =   [[longer] for longer in is_longer]

        A :       Union[str, Any]          =   {"""input_features""": input_mel, """is_longer""": is_longer}
        A :       Tuple          =   BatchFeature(lowerCamelCase__		)

        if return_tensors is not None:
            A :       List[str]          =   input_features.convert_to_tensors(lowerCamelCase__		)

        return input_features


