

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       __UpperCAmelCase  :



   def __init__(							self							:  List[Any],	__A							:  List[Any],	__A							:  Any=sys.maxsize		):
     UpperCAmelCase :							Optional[Any]								=				'''bilinear'''
     UpperCAmelCase :							Optional[int]								=				max_size
     UpperCAmelCase :							Union[str, Any]								=				short_edge_length





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

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

     return img_augs






class       __UpperCAmelCase  :



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



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

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





   def __call__(							self							:  str,	__A							:  Any,	__A							:  Optional[int]=False		):
     with torch.no_grad():
       if not isinstance(__A,	__A		):
         UpperCAmelCase :							str								=				[images]
       if single_image:
         assert len(__A		) == 1
       for i in range(len(__A		)		):
         if isinstance(images[i],	torch.Tensor		):
           images.insert(__A,	images.pop(__A		).to(self.device		).float()		)
         elif not isinstance(images[i],	torch.Tensor		):
           images.insert(
               __A,	torch.as_tensor(img_tensorize(images.pop(__A		),	input_format=self.input_format		)		)
               .to(self.device		)
               .float(),	)
            # resize smallest edge
       UpperCAmelCase :							List[str]								=				torch.tensor([im.shape[:2] for im in images]		)
       UpperCAmelCase :							int								=				self.aug(__A		)
       # 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
       UpperCAmelCase :							Union[str, Any]								=				[self.normalizer(__A		) for x in images]
       # now pad them to do the following operations
       UpperCAmelCase				,		UpperCAmelCase :							List[Any]								=				self.pad(__A		)
       # Normalize

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






def     a__  (	UpperCAmelCase							:    Dict       ,				UpperCAmelCase							:    str    )						->		Dict:
  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes






def     a__  (	UpperCAmelCase							:    Any       ,				UpperCAmelCase							:    Tuple[int, int]    )						->		str:
  assert torch.isfinite(UpperCAmelCase    ).all(), "Box tensor contains infinite or NaN!"
  UpperCAmelCase				,		UpperCAmelCase :							Any								=				box_size
  tensor[:, 0].clamp_(min=0       ,				max=UpperCAmelCase    )
  tensor[:, 1].clamp_(min=0       ,				max=UpperCAmelCase    )
  tensor[:, 2].clamp_(min=0       ,				max=UpperCAmelCase    )
  tensor[:, 3].clamp_(min=0       ,				max=UpperCAmelCase    )








from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_lowerCamelCase       :     str           =							logging.get_logger(__name__)





class       __UpperCAmelCase  ( lowerCamelCase__		):
   UpperCamelCase       =  ["""input_values""", """attention_mask"""]



   def __init__(							self							:  Union[str, Any],	__A							:  Dict=1,	__A							:  Any=1_6_0_0_0,	__A							:  List[str]=1_2_8,	__A							:  str=1_0_2_4,	__A							:  int=0.0,	__A							:  str=True,	__A							:  Any=-4.2_6_7_7_3_9_3,	__A							:  Optional[int]=4.5_6_8_9_9_7_4,	__A							:  Optional[int]=False,	**__A							:  str,	):
     super().__init__(feature_size=__A,	sampling_rate=__A,	padding_value=__A,	**__A		)
     UpperCAmelCase :							int								=				num_mel_bins
     UpperCAmelCase :							Tuple								=				max_length
     UpperCAmelCase :							str								=				do_normalize
     UpperCAmelCase :							List[Any]								=				mean
     UpperCAmelCase :							Union[str, Any]								=				std
     UpperCAmelCase :							Any								=				return_attention_mask



   def  __magic_name__					(							self							:  Dict,	__A							:  np.ndarray,	__A							:  int,	):
     UpperCAmelCase :							Union[str, Any]								=				torch.from_numpy(__A		).unsqueeze(0		)
     UpperCAmelCase :							List[str]								=				ta_kaldi.fbank(
         __A,	htk_compat=__A,	sample_frequency=self.sampling_rate,	use_energy=__A,	window_type='''hanning''',	num_mel_bins=self.num_mel_bins,	dither=0.0,	frame_shift=1_0,	)

     UpperCAmelCase :							int								=				fbank.shape[0]
     UpperCAmelCase :							int								=				max_length - n_frames

     # pad or truncate, depending on difference
     if difference > 0:
       UpperCAmelCase :							List[Any]								=				torch.nn.ZeroPadad((0, 0, 0, difference)		)
       UpperCAmelCase :							int								=				pad_module(__A		)
     elif difference < 0:
       UpperCAmelCase :							Dict								=				fbank[0:max_length, :]

     UpperCAmelCase :							int								=				fbank.numpy()

     return fbank



   def  __magic_name__					(							self							:  Optional[Any],	__A							:  np.ndarray		):
     return (input_values - (self.mean)) / (self.std * 2)





   def __call__(							self							:  Union[str, Any],	__A							:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],	__A							:  Optional[int] = None,	__A							:  Optional[Union[str, TensorType]] = None,	**__A							:  Optional[Any],	):

     if sampling_rate is not None:
       if sampling_rate != self.sampling_rate:
         raise ValueError(
             F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
             F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
             F''' {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 :							Any								=				isinstance(__A,	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(__A,	(list, tuple)		) and (isinstance(raw_speech[0],	(np.ndarray, tuple, list)		))
     )

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

     # always return batch
     if not is_batched:
       UpperCAmelCase :							str								=				[raw_speech]

     # extract fbank features and pad/truncate to max_length
     UpperCAmelCase :							Union[str, Any]								=				[self._extract_fbank_features(__A,	max_length=self.max_length		) for waveform in raw_speech]

     # convert into BatchFeature
     UpperCAmelCase :							Optional[Any]								=				BatchFeature({'''input_values''': features}		)

     # make sure list is in array format
     UpperCAmelCase :							Optional[int]								=				padded_inputs.get('''input_values'''		)
     if isinstance(input_values[0],	__A		):
       UpperCAmelCase :							Dict								=				[np.asarray(__A,	dtype=np.floataa		) for feature in input_values]

     # normalization
     if self.do_normalize:
       UpperCAmelCase :							str								=				[self.normalize(__A		) for feature in input_values]

     if return_tensors is not None:
       UpperCAmelCase :							Tuple								=				padded_inputs.convert_to_tensors(__A		)

     return padded_inputs





