






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


__UpperCAmelCase     							=						logging.get_logger(__name__)



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Dict							=  ['''input_features''', '''is_longer''']



		def __init__(							self    ,		_UpperCamelCase=6_4    ,		_UpperCamelCase=4_8_0_0_0    ,		_UpperCamelCase=4_8_0    ,		_UpperCamelCase=1_0    ,		_UpperCamelCase=1_0_2_4    ,		_UpperCamelCase=0.0    ,		_UpperCamelCase=False    ,		_UpperCamelCase = 0    ,		_UpperCamelCase = 1_4_0_0_0    ,		_UpperCamelCase = None    ,		_UpperCamelCase = "fusion"    ,		_UpperCamelCase = "repeatpad"    ,		**_UpperCamelCase    ,		)     ->							Optional[int]:
							super().__init__(
							    feature_size=_UpperCamelCase    ,		sampling_rate=_UpperCamelCase    ,		padding_value=_UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		**_UpperCamelCase    ,		)
							UpperCAmelCase_   :				List[str]						=						top_db
							UpperCAmelCase_   :				List[Any]						=						truncation
							UpperCAmelCase_   :				Optional[Any]						=						padding
							UpperCAmelCase_   :				Union[str, Any]						=						fft_window_size
							UpperCAmelCase_   :				List[str]						=						(fft_window_size >> 1) + 1
							UpperCAmelCase_   :				Tuple						=						hop_length
							UpperCAmelCase_   :				str						=						max_length_s
							UpperCAmelCase_   :				Dict						=						max_length_s * sampling_rate
							UpperCAmelCase_   :				str						=						sampling_rate
							UpperCAmelCase_   :				Dict						=						frequency_min
							UpperCAmelCase_   :				List[Any]						=						frequency_max
							UpperCAmelCase_   :				int						=						mel_filter_bank(
							    num_frequency_bins=self.nb_frequency_bins    ,		num_mel_filters=_UpperCamelCase    ,		min_frequency=_UpperCamelCase    ,		max_frequency=_UpperCamelCase    ,		sampling_rate=_UpperCamelCase    ,		norm=_UpperCamelCase    ,		mel_scale='htk'    ,		)
							UpperCAmelCase_   :				Optional[int]						=						mel_filter_bank(
							    num_frequency_bins=self.nb_frequency_bins    ,		num_mel_filters=_UpperCamelCase    ,		min_frequency=_UpperCamelCase    ,		max_frequency=_UpperCamelCase    ,		sampling_rate=_UpperCamelCase    ,		norm='slaney'    ,		mel_scale='slaney'    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Dict[str, Any]:
							UpperCAmelCase_   :				List[Any]						=						copy.deepcopy(self.__dict__       )
							UpperCAmelCase_   :				Any						=						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  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							np.ndarray:
							UpperCAmelCase_   :				List[str]						=						spectrogram(
							    _UpperCamelCase    ,		window_function(self.fft_window_size    ,		'hann'       )    ,		frame_length=self.fft_window_size    ,		hop_length=self.hop_length    ,		power=2.0    ,		mel_filters=_UpperCamelCase    ,		log_mel='dB'    ,		)
							return log_mel_spectrogram.T



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							List[str]:
							UpperCAmelCase_   :				Optional[int]						=						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
												UpperCAmelCase_   :				str						=						[0]
							if len(ranges[2]       ) == 0:
												# if the audio is too short, we just use the first chunk
												UpperCAmelCase_   :				Union[str, Any]						=						[0]
							# randomly choose index for each part
							UpperCAmelCase_   :				int						=						np.random.choice(ranges[0]       )
							UpperCAmelCase_   :				Optional[Any]						=						np.random.choice(ranges[1]       )
							UpperCAmelCase_   :				Tuple						=						np.random.choice(ranges[2]       )

							UpperCAmelCase_   :				int						=						mel[idx_front : idx_front + chunk_frames, :]
							UpperCAmelCase_   :				str						=						mel[idx_middle : idx_middle + chunk_frames, :]
							UpperCAmelCase_   :				Optional[int]						=						mel[idx_back : idx_back + chunk_frames, :]

							UpperCAmelCase_   :				Optional[int]						=						torch.tensor(mel[None, None, :]       )
							UpperCAmelCase_   :				List[str]						=						torch.nn.functional.interpolate(
							    _UpperCamelCase    ,		size=[chunk_frames, 6_4]    ,		mode='bilinear'    ,		align_corners=_UpperCamelCase       )
							UpperCAmelCase_   :				Tuple						=						mel_shrink[0][0].numpy()
							UpperCAmelCase_   :				str						=						np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]    ,		axis=0       )
							return mel_fusion



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							np.array:
							if waveform.shape[0] > max_length:
												if truncation == "rand_trunc":
																	UpperCAmelCase_   :				Dict						=						True
																	# random crop to max_length (for compatibility) -> this should be handled by self.pad
																	UpperCAmelCase_   :				str						=						len(_UpperCamelCase       ) - max_length
																	UpperCAmelCase_   :				Any						=						np.random.randint(0    ,		overflow + 1       )
																	UpperCAmelCase_   :				Optional[int]						=						waveform[idx : idx + max_length]
																	UpperCAmelCase_   :				List[str]						=						self._np_extract_fbank_features(_UpperCamelCase    ,		self.mel_filters_slaney       )[None, :]
												elif truncation == "fusion":
																	UpperCAmelCase_   :				Union[str, Any]						=						self._np_extract_fbank_features(_UpperCamelCase    ,		self.mel_filters       )
																	UpperCAmelCase_   :				List[Any]						=						max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																	UpperCAmelCase_   :				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.
																						UpperCAmelCase_   :				str						=						np.stack([mel, mel, mel, mel]    ,		axis=0       )
																						UpperCAmelCase_   :				List[Any]						=						False
																	else:
																						UpperCAmelCase_   :				Union[str, Any]						=						self._random_mel_fusion(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )
																						UpperCAmelCase_   :				int						=						True
												else:
																	raise NotImplementedError(f"data_truncating {truncation} not implemented"       )

							else:
												UpperCAmelCase_   :				List[Any]						=						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":
																						UpperCAmelCase_   :				Optional[Any]						=						int(max_length / len(_UpperCamelCase       )       )
																						UpperCAmelCase_   :				Tuple						=						np.stack(np.tile(_UpperCamelCase    ,		n_repeat + 1       )       )[:max_length]
																	if padding == "repeatpad":
																						UpperCAmelCase_   :				Any						=						int(max_length / len(_UpperCamelCase       )       )
																						UpperCAmelCase_   :				List[str]						=						np.stack(np.tile(_UpperCamelCase    ,		_UpperCamelCase       )       )
																	UpperCAmelCase_   :				Optional[int]						=						np.pad(_UpperCamelCase    ,		(0, max_length - waveform.shape[0])    ,		mode='constant'    ,		constant_values=0       )

												if truncation == "fusion":
																	UpperCAmelCase_   :				Any						=						self._np_extract_fbank_features(_UpperCamelCase    ,		self.mel_filters       )
																	UpperCAmelCase_   :				int						=						np.stack([input_mel, input_mel, input_mel, input_mel]    ,		axis=0       )
												else:
																	UpperCAmelCase_   :				int						=						self._np_extract_fbank_features(_UpperCamelCase    ,		self.mel_filters_slaney       )[None, :]

							return input_mel, longer



		def __call__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		**_UpperCamelCase    ,		)     ->							BatchFeature:
							UpperCAmelCase_   :				Any						=						truncation if truncation is not None else self.truncation
							UpperCAmelCase_   :				Tuple						=						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.'       )

							UpperCAmelCase_   :				Tuple						=						isinstance(_UpperCamelCase    ,		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}"       )
							UpperCAmelCase_   :				Dict						=						is_batched_numpy or (
							    isinstance(_UpperCamelCase    ,		(list, tuple)       ) and (isinstance(raw_speech[0]    ,		(np.ndarray, tuple, list)       ))
							)

							if is_batched:
												UpperCAmelCase_   :				List[Any]						=						[np.asarray(_UpperCamelCase    ,		dtype=np.floataa       ) for speech in raw_speech]
							elif not is_batched and not isinstance(_UpperCamelCase    ,		np.ndarray       ):
												UpperCAmelCase_   :				List[str]						=						np.asarray(_UpperCamelCase    ,		dtype=np.floataa       )
							elif isinstance(_UpperCamelCase    ,		np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
												UpperCAmelCase_   :				Optional[Any]						=						raw_speech.astype(np.floataa       )

							# always return batch
							if not is_batched:
												UpperCAmelCase_   :				int						=						[np.asarray(_UpperCamelCase       )]

							# convert to mel spectrogram, truncate and pad if needed.
							UpperCAmelCase_   :				Optional[int]						=						[
							    self._get_input_mel(_UpperCamelCase    ,		max_length if max_length else self.nb_max_samples    ,		_UpperCamelCase    ,		_UpperCamelCase       )
							    for waveform in raw_speech
							]

							UpperCAmelCase_   :				List[str]						=						[]
							UpperCAmelCase_   :				Dict						=						[]
							for mel, longer in padded_inputs:
												input_mel.append(_UpperCamelCase       )
												is_longer.append(_UpperCamelCase       )

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

							if isinstance(input_mel[0]    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				List[Any]						=						[np.asarray(_UpperCamelCase    ,		dtype=np.floataa       ) for feature in input_mel]

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

							UpperCAmelCase_   :				List[Any]						=						{'input_features': input_mel, 'is_longer': is_longer}
							UpperCAmelCase_   :				List[Any]						=						BatchFeature(_UpperCamelCase       )

							if return_tensors is not None:
												UpperCAmelCase_   :				str						=						input_features.convert_to_tensors(_UpperCamelCase       )

							return input_features














from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
		from ...processing_utils import ProcessorMixin
		from ...utils import TensorType


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Dict							=  '''layoutlmv3'''



		def __init__(							self    ,		_UpperCamelCase=5_0_2_6_5    ,		_UpperCamelCase=7_6_8    ,		_UpperCamelCase=1_2    ,		_UpperCamelCase=1_2    ,		_UpperCamelCase=3_0_7_2    ,		_UpperCamelCase="gelu"    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=2    ,		_UpperCamelCase=0.02    ,		_UpperCamelCase=1E-5    ,		_UpperCamelCase=1    ,		_UpperCamelCase=0    ,		_UpperCamelCase=2    ,		_UpperCamelCase=1_0_2_4    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=True    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=6_4    ,		_UpperCamelCase=2_5_6    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=2_2_4    ,		_UpperCamelCase=3    ,		_UpperCamelCase=1_6    ,		_UpperCamelCase=None    ,		**_UpperCamelCase    ,		)     ->							Optional[Any]:
							super().__init__(
							    vocab_size=_UpperCamelCase    ,		hidden_size=_UpperCamelCase    ,		num_hidden_layers=_UpperCamelCase    ,		num_attention_heads=_UpperCamelCase    ,		intermediate_size=_UpperCamelCase    ,		hidden_act=_UpperCamelCase    ,		hidden_dropout_prob=_UpperCamelCase    ,		attention_probs_dropout_prob=_UpperCamelCase    ,		max_position_embeddings=_UpperCamelCase    ,		type_vocab_size=_UpperCamelCase    ,		initializer_range=_UpperCamelCase    ,		layer_norm_eps=_UpperCamelCase    ,		pad_token_id=_UpperCamelCase    ,		bos_token_id=_UpperCamelCase    ,		eos_token_id=_UpperCamelCase    ,		**_UpperCamelCase    ,		)
							UpperCAmelCase_   :				str						=						max_ad_position_embeddings
							UpperCAmelCase_   :				Union[str, Any]						=						coordinate_size
							UpperCAmelCase_   :				Union[str, Any]						=						shape_size
							UpperCAmelCase_   :				str						=						has_relative_attention_bias
							UpperCAmelCase_   :				Tuple						=						rel_pos_bins
							UpperCAmelCase_   :				Dict						=						max_rel_pos
							UpperCAmelCase_   :				Any						=						has_spatial_attention_bias
							UpperCAmelCase_   :				Optional[Any]						=						rel_ad_pos_bins
							UpperCAmelCase_   :				List[str]						=						max_rel_ad_pos
							UpperCAmelCase_   :				List[str]						=						text_embed
							UpperCAmelCase_   :				Dict						=						visual_embed
							UpperCAmelCase_   :				Optional[int]						=						input_size
							UpperCAmelCase_   :				List[Any]						=						num_channels
							UpperCAmelCase_   :				List[Any]						=						patch_size
							UpperCAmelCase_   :				Union[str, Any]						=						classifier_dropout





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Tuple							=  version.parse('''1.12'''      )



		@property
		def  __UpperCAmelCase      (							self       )     ->							Mapping[str, Mapping[int, str]]:
							# The order of inputs is different for question answering and sequence classification
							if self.task in ["question-answering", "sequence-classification"]:
												return OrderedDict(
												    [
												        ('input_ids', {0: 'batch', 1: 'sequence'}),
												        ('attention_mask', {0: 'batch', 1: 'sequence'}),
												        ('bbox', {0: 'batch', 1: 'sequence'}),
												        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
												    ]       )
							else:
												return OrderedDict(
												    [
												        ('input_ids', {0: 'batch', 1: 'sequence'}),
												        ('bbox', {0: 'batch', 1: 'sequence'}),
												        ('attention_mask', {0: 'batch', 1: 'sequence'}),
												        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
												    ]       )



		@property
		def  __UpperCAmelCase      (							self       )     ->							float:
							return 1E-5



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							return 1_2



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = -1    ,		_UpperCamelCase = -1    ,		_UpperCamelCase = False    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 3    ,		_UpperCamelCase = 4_0    ,		_UpperCamelCase = 4_0    ,		)     ->							Mapping[str, Any]:
							setattr(processor.image_processor    ,		'apply_ocr'    ,		_UpperCamelCase       )

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							UpperCAmelCase_   :				Optional[int]						=						compute_effective_axis_dimension(
							    _UpperCamelCase    ,		fixed_dimension=OnnxConfig.default_fixed_batch    ,		num_token_to_add=0       )
							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							UpperCAmelCase_   :				Any						=						processor.tokenizer.num_special_tokens_to_add(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						compute_effective_axis_dimension(
							    _UpperCamelCase    ,		fixed_dimension=OnnxConfig.default_fixed_sequence    ,		num_token_to_add=_UpperCamelCase       )
							# Generate dummy inputs according to compute batch and sequence
							UpperCAmelCase_   :				Tuple						=						[[' '.join([processor.tokenizer.unk_token]       ) * seq_length]] * batch_size

							# Generate dummy bounding boxes
							UpperCAmelCase_   :				List[str]						=						[[[4_8, 8_4, 7_3, 1_2_8]]] * batch_size

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
							UpperCAmelCase_   :				str						=						self._generate_dummy_images(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				List[str]						=						dict(
							    processor(
							        _UpperCamelCase    ,		text=_UpperCamelCase    ,		boxes=_UpperCamelCase    ,		return_tensors=_UpperCamelCase    ,		)       )

							return inputs






