





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize


class       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						List[Any],					lowerCamelCase_:						Tuple,					lowerCamelCase_:						int=sys.maxsize			):
			lowercase__					:      Dict      =		'bilinear'
			lowercase__					:      List[Any]      =		max_size
			lowercase__					:      Optional[int]      =		short_edge_length





	def __call__(						self:						int,					lowerCamelCase_:						Dict			):
			lowercase__					:      int      =		[]
			for img in imgs:
					lowercase__   ,				lowercase__					:      int      =		img.shape[:2]
					# later: provide list and randomly choose index for resize
					lowercase__					:      Optional[int]      =		np.random.randint(self.short_edge_length[0],					self.short_edge_length[1] + 1			)
					if size == 0:
							return img
					lowercase__					:      Optional[int]      =		size * 1.0 / min(lowerCamelCase_,					lowerCamelCase_			)
					if h < w:
							lowercase__   ,				lowercase__					:      Dict      =		size, scale * w
					else:
							lowercase__   ,				lowercase__					:      int      =		scale * h, size
					if max(lowerCamelCase_,					lowerCamelCase_			) > self.max_size:
							lowercase__					:      Any      =		self.max_size * 1.0 / max(lowerCamelCase_,					lowerCamelCase_			)
							lowercase__					:      Tuple      =		newh * scale
							lowercase__					:      Dict      =		neww * scale
					lowercase__					:      List[Any]      =		int(neww + 0.5			)
					lowercase__					:      Tuple      =		int(newh + 0.5			)

					if img.dtype == np.uinta:
							lowercase__					:      Tuple      =		Image.fromarray(lowerCamelCase_			)
							lowercase__					:      Union[str, Any]      =		pil_image.resize((neww, newh),					PILImageResampling.BILINEAR			)
							lowercase__					:      Optional[int]      =		np.asarray(lowerCamelCase_			)
					else:
							lowercase__					:      str      =		img.permute(2,					0,					1			).unsqueeze(0			)  # 3, 0, 1)  # hw(c) -> nchw
							lowercase__					:      Dict      =		nn.functional.interpolate(
							    lowerCamelCase_,					(newh, neww),					mode=self.interp_method,					align_corners=lowerCamelCase_			).squeeze(0			)
					img_augs.append(lowerCamelCase_			)

			return img_augs


class       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						Dict,					lowerCamelCase_:						List[str]			):
			lowercase__					:      Optional[int]      =		ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST],					cfg.INPUT.MAX_SIZE_TEST			)
			lowercase__					:      Union[str, Any]      =		cfg.INPUT.FORMAT
			lowercase__					:      int      =		cfg.SIZE_DIVISIBILITY
			lowercase__					:      Tuple      =		cfg.PAD_VALUE
			lowercase__					:      Optional[int]      =		cfg.INPUT.MAX_SIZE_TEST
			lowercase__					:      Optional[Any]      =		cfg.MODEL.DEVICE
			lowercase__					:      int      =		torch.tensor(cfg.MODEL.PIXEL_STD			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			),					1,					1			)
			lowercase__					:      Any      =		torch.tensor(cfg.MODEL.PIXEL_MEAN			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			),					1,					1			)
			lowercase__					:      List[Any]      =		lambda lowerCamelCase_			: (x - self.pixel_mean) / self.pixel_std

	def    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						Dict			):
			lowercase__					:      Any      =		tuple(max(lowerCamelCase_			) for s in zip(*[img.shape for img in images]			)			)
			lowercase__					:      List[Any]      =		[im.shape[-2:] for im in images]
			lowercase__					:      Dict      =		[
			    nn.functional.pad(
			        lowerCamelCase_,					[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],					value=self.pad_value,					)
			    for size, im in zip(lowerCamelCase_,					lowerCamelCase_			)
			]

			return torch.stack(lowerCamelCase_			), torch.tensor(lowerCamelCase_			)





	def __call__(						self:						Optional[Any],					lowerCamelCase_:						int,					lowerCamelCase_:						Optional[Any]=False			):
			with torch.no_grad():
					if not isinstance(lowerCamelCase_,					lowerCamelCase_			):
							lowercase__					:      int      =		[images]
					if single_image:
							assert len(lowerCamelCase_			) == 1
					for i in range(len(lowerCamelCase_			)			):
							if isinstance(images[i],					torch.Tensor			):
									images.insert(lowerCamelCase_,					images.pop(lowerCamelCase_			).to(self.device			).float()			)
							elif not isinstance(images[i],					torch.Tensor			):
									images.insert(
									    lowerCamelCase_,					torch.as_tensor(img_tensorize(images.pop(lowerCamelCase_			),					input_format=self.input_format			)			)
									    .to(self.device			)
									    .float(),					)
            # resize smallest edge
					lowercase__					:      List[Any]      =		torch.tensor([im.shape[:2] for im in images]			)
					lowercase__					:      Tuple      =		self.aug(lowerCamelCase_			)
					# transpose images and convert to torch tensors
					# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
					# now normalize before pad to avoid useless arithmetic
					lowercase__					:      Dict      =		[self.normalizer(lowerCamelCase_			) for x in images]
					# now pad them to do the following operations
					lowercase__   ,				lowercase__					:      Dict      =		self.pad(lowerCamelCase_			)
					# Normalize

					if self.size_divisibility > 0:
							raise NotImplementedError()
					# pad
					lowercase__					:      Any      =		torch.true_divide(lowerCamelCase_,					lowerCamelCase_			)
					if single_image:
							return images[0], sizes[0], scales_yx[0]
					else:
							return images, sizes, scales_yx


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[str]   ,    _lowercase     : str			)   ->							List[str]:




		'''simple docstring'''
		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes





def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[Any]   ,    _lowercase     : Tuple[int, int]			)   ->							Tuple:




		'''simple docstring'''
		assert torch.isfinite(_lowercase			).all(), "Box tensor contains infinite or NaN!"
		lowercase__   ,				lowercase__					:      List[Any]      =		box_size
		tensor[:, 0].clamp_(min=0   ,    max=_lowercase			)
		tensor[:, 1].clamp_(min=0   ,    max=_lowercase			)
		tensor[:, 2].clamp_(min=0   ,    max=_lowercase			)
		tensor[:, 3].clamp_(min=0   ,    max=_lowercase			)









from math import ceil
from typing import List, Optional, Union

import numpy as np

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


__UpperCamelCase:	Optional[int] = logging.get_logger(__name__)


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				["audio_values", "audio_mask"]

	def __init__(						self:						Any,					lowerCamelCase_:						int=2048,					lowerCamelCase_:						str=1,					lowerCamelCase_:						str=[16, 16],					lowerCamelCase_:						Union[str, Any]=128,					lowerCamelCase_:						Tuple=44100,					lowerCamelCase_:						Union[str, Any]=86,					lowerCamelCase_:						Optional[Any]=2048,					lowerCamelCase_:						Dict=0.0,					**lowerCamelCase_:						str,					):
			super().__init__(
			    feature_size=lowerCamelCase_,					sampling_rate=lowerCamelCase_,					padding_value=lowerCamelCase_,					**lowerCamelCase_,					)

			lowercase__					:      List[str]      =		spectrogram_length
			lowercase__					:      Union[str, Any]      =		num_channels
			lowercase__					:      int      =		patch_size
			lowercase__					:      Union[str, Any]      =		feature_size // self.patch_size[1]
			lowercase__					:      Optional[int]      =		n_fft
			lowercase__					:      int      =		sampling_rate // hop_length_to_sampling_rate
			lowercase__					:      str      =		sampling_rate
			lowercase__					:      Union[str, Any]      =		padding_value
			lowercase__					:      Any      =		mel_filter_bank(
			    num_frequency_bins=1 + n_fft // 2,					num_mel_filters=lowerCamelCase_,					min_frequency=0.0,					max_frequency=2_2_0_5_0.0,					sampling_rate=lowerCamelCase_,					norm='slaney',					mel_scale='slaney',					).T

	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						np.array			):
			lowercase__					:      Optional[Any]      =		spectrogram(
			    lowerCamelCase_,					window_function(self.n_fft,					'hann'			),					frame_length=self.n_fft,					hop_length=self.hop_length,					power=2.0,					mel_filters=self.mel_filters.T,					log_mel='dB',					db_range=8_0.0,					)
			lowercase__					:      Optional[Any]      =		log_spec[:, :-1]
			lowercase__					:      int      =		log_spec - 2_0.0
			lowercase__					:      Dict      =		np.clip(log_spec / 4_0.0,					-2.0,					0.0			) + 1.0
			return log_spec





	def __call__(						self:						Dict,					lowerCamelCase_:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],					lowerCamelCase_:						Optional[Union[str, TensorType]] = None,					lowerCamelCase_:						Optional[bool] = True,					lowerCamelCase_:						Optional[int] = None,					lowerCamelCase_:						bool = False,					lowerCamelCase_:						bool = False,					**lowerCamelCase_:						str,					):

			if sampling_rate is not None:
					if sampling_rate != self.sampling_rate:
							raise ValueError(
							    'This feature extractor is set to support sampling rate'
							    F""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
							    F""" 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.'			)

			lowercase__					:      Any      =		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}"""			)
			lowercase__					:      Optional[Any]      =		is_batched_numpy or (
			    isinstance(lowerCamelCase_,					(list, tuple)			) and (isinstance(raw_speech[0],					(np.ndarray, tuple, list)			))
			)
			if is_batched:
					lowercase__					:      int      =		[np.asarray([speech],					dtype=np.floataa			).T for speech in raw_speech]
			elif not is_batched and not isinstance(lowerCamelCase_,					np.ndarray			):
					lowercase__					:      Optional[int]      =		np.asarray(lowerCamelCase_,					dtype=np.floataa			)
			elif isinstance(lowerCamelCase_,					np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
					lowercase__					:      str      =		raw_speech.astype(np.floataa			)
			# always return batch
			if not is_batched:
					lowercase__					:      Tuple      =		[np.asarray([raw_speech]			).T]

			# Convert audio signals to log mel spectrograms, truncate by time axis
			lowercase__					:      List[str]      =		[
			    self._np_extract_fbank_features(waveform.squeeze()			).T[: self.spectrogram_length] for waveform in raw_speech
			]
			if isinstance(audio_features[0],					lowerCamelCase_			):
					lowercase__					:      str      =		[np.asarray(lowerCamelCase_,					dtype=np.floataa			) for feature in audio_features]

			# Create audio attention mask
			lowercase__					:      Dict      =		max(
			    [ceil(feature.shape[0] / self.patch_size[0]			) * self.freq_len for feature in audio_features]			)  # The maximum number of audio patches in a batch
			if return_attention_mask:
					lowercase__					:      int      =		[
					    (ceil(feature.shape[0] / self.patch_size[0]			) * self.freq_len) * [1]
					    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]			) * self.freq_len) * [0]
					    for feature in audio_features
					]
					lowercase__					:      Any      =		np.array(lowerCamelCase_			).astype(np.floataa			)

			# convert into correct format for padding
			lowercase__					:      Any      =		max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
			lowercase__					:      Tuple      =		np.ones([len(lowerCamelCase_			), 1, max_time_len, self.feature_size]			).astype(np.floataa			)
			lowercase__					:      Dict      =		padded_audio_features * self.padding_value
			for i in range(len(lowerCamelCase_			)			):
					lowercase__					:      Union[str, Any]      =		audio_features[i]
					lowercase__					:      str      =		feature

			# return as BatchFeature
			if return_attention_mask:
					lowercase__					:      Dict      =		{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
			else:
					lowercase__					:      int      =		{'audio_values': padded_audio_features}

			lowercase__					:      List[Any]      =		BatchFeature(data=lowerCamelCase_,					tensor_type=lowerCamelCase_			)
			return encoded_inputs


