



'''simple docstring'''





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      :	Optional[int]			,	_a      :	Tuple			,	_a      :	Any=sys.maxsize   )					->					List[Any]:
    __lowerCamelCase       :       List[str]           = 'bilinear'
    __lowerCamelCase       :       Any           = max_size
    __lowerCamelCase       :       List[str]           = short_edge_length




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

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

    return img_augs






class 					lowerCamelCase_    :
  """simple docstring"""




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




  def 						_lowercase     ( self      :	Optional[int]			,	_a      :	Optional[Any]   )					->					Optional[int]:
    __lowerCamelCase       :       Tuple           = tuple(max(_a   ) for s in zip(*[img.shape for img in images]   )   )
    __lowerCamelCase       :       Union[str, Any]           = [im.shape[-2:] for im in images]
    __lowerCamelCase       :       List[Any]           = [
        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      :	Optional[Any]			,	_a      :	Dict			,	_a      :	Tuple=False   )					->					List[Any]:
    with torch.no_grad():
      if not isinstance(_a			,	_a   ):
        __lowerCamelCase       :       Tuple           = [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
      __lowerCamelCase       :       str           = torch.tensor([im.shape[:2] for im in images]   )
      __lowerCamelCase       :       Dict           = 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
      __lowerCamelCase       :       Dict           = [self.normalizer(_a   ) for x in images]
      # now pad them to do the following operations
      __lowerCamelCase       ,__lowerCamelCase       :       List[Any]           = self.pad(_a   )
      # Normalize

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




def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Union[str, Any]:
  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes


def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Dict:
  assert torch.isfinite(_lowerCAmelCase       ).all(), "Box tensor contains infinite or NaN!"
  __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = box_size
  tensor[:, 0].clamp_(min=0       ,max=_lowerCAmelCase       )
  tensor[:, 1].clamp_(min=0       ,max=_lowerCAmelCase       )
  tensor[:, 2].clamp_(min=0       ,max=_lowerCAmelCase       )
  tensor[:, 3].clamp_(min=0       ,max=_lowerCAmelCase       )











'''simple docstring'''





import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      np.array:
  __lowerCamelCase       :       Any           = F'{sampling_rate}'
  __lowerCamelCase       :       List[str]           = '1'
  __lowerCamelCase       :       int           = 'f32le'
  __lowerCamelCase       :       Dict           = [
      'ffmpeg',
      '-i',
      'pipe:0',
      '-ac',
      ac,
      '-ar',
      ar,
      '-f',
      format_for_conversion,
      '-hide_banner',
      '-loglevel',
      'quiet',
      'pipe:1',
  ]

  try:
    with subprocess.Popen(_lowerCAmelCase       ,stdin=subprocess.PIPE       ,stdout=subprocess.PIPE       ) as ffmpeg_process:
      __lowerCamelCase       :       Tuple           = ffmpeg_process.communicate(_lowerCAmelCase       )
  except FileNotFoundError as error:
    raise ValueError('ffmpeg was not found but is required to load audio files from filename'       ) from error
  __lowerCamelCase       :       Any           = output_stream[0]
  __lowerCamelCase       :       Union[str, Any]           = np.frombuffer(_lowerCAmelCase       ,np.floataa       )
  if audio.shape[0] == 0:
    raise ValueError('Malformed soundfile'       )
  return audio

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = "f32le"       ,)      ->      Dict:
  __lowerCamelCase       :       Optional[Any]           = F'{sampling_rate}'
  __lowerCamelCase       :       Optional[int]           = '1'
  if format_for_conversion == "s16le":
    __lowerCamelCase       :       List[Any]           = 2
  elif format_for_conversion == "f32le":
    __lowerCamelCase       :       Tuple           = 4
  else:
    raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'       )

  __lowerCamelCase       :       Any           = platform.system()
  if system == "Linux":
    __lowerCamelCase       :       Tuple           = 'alsa'
    __lowerCamelCase       :       Optional[Any]           = 'default'
  elif system == "Darwin":
    __lowerCamelCase       :       Union[str, Any]           = 'avfoundation'
    __lowerCamelCase       :       Tuple           = ':0'
  elif system == "Windows":
    __lowerCamelCase       :       List[str]           = 'dshow'
    __lowerCamelCase       :       Optional[Any]           = 'default'

  __lowerCamelCase       :       Optional[int]           = [
      'ffmpeg',
      '-f',
      format_,
      '-i',
      input_,
      '-ac',
      ac,
      '-ar',
      ar,
      '-f',
      format_for_conversion,
      '-fflags',
      'nobuffer',
      '-hide_banner',
      '-loglevel',
      'quiet',
      'pipe:1',
  ]
  __lowerCamelCase       :       List[str]           = int(round(sampling_rate * chunk_length_s       )       ) * size_of_sample
  __lowerCamelCase       :       int           = _ffmpeg_stream(_lowerCAmelCase       ,_lowerCAmelCase       )
  for item in iterator:
    yield item

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,_lowerCAmelCase = "f32le"       ,)      ->      List[str]:
  if stream_chunk_s is not None:
    __lowerCamelCase       :       int           = stream_chunk_s
  else:
    __lowerCamelCase       :       List[Any]           = chunk_length_s

  __lowerCamelCase       :       Dict           = ffmpeg_microphone(_lowerCAmelCase       ,_lowerCAmelCase       ,format_for_conversion=_lowerCAmelCase       )
  if format_for_conversion == "s16le":
    __lowerCamelCase       :       List[str]           = np.intaa
    __lowerCamelCase       :       Union[str, Any]           = 2
  elif format_for_conversion == "f32le":
    __lowerCamelCase       :       Union[str, Any]           = np.floataa
    __lowerCamelCase       :       Optional[Any]           = 4
  else:
    raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'       )

  if stride_length_s is None:
    __lowerCamelCase       :       Any           = chunk_length_s / 6
  __lowerCamelCase       :       List[str]           = int(round(sampling_rate * chunk_length_s       )       ) * size_of_sample
  if isinstance(_lowerCAmelCase       ,(int, float)       ):
    __lowerCamelCase       :       Tuple           = [stride_length_s, stride_length_s]

  __lowerCamelCase       :       Union[str, Any]           = int(round(sampling_rate * stride_length_s[0]       )       ) * size_of_sample
  __lowerCamelCase       :       Optional[Any]           = int(round(sampling_rate * stride_length_s[1]       )       ) * size_of_sample
  __lowerCamelCase       :       Dict           = datetime.datetime.now()
  __lowerCamelCase       :       Any           = datetime.timedelta(seconds=_lowerCAmelCase       )
  for item in chunk_bytes_iter(_lowerCAmelCase       ,_lowerCAmelCase       ,stride=(stride_left, stride_right)       ,stream=_lowerCAmelCase       ):
    # Put everything back in numpy scale
    __lowerCamelCase       :       Optional[int]           = np.frombuffer(item['raw']       ,dtype=_lowerCAmelCase       )
    __lowerCamelCase       :       Tuple           = (
        item['stride'][0] // size_of_sample,
        item['stride'][1] // size_of_sample,
    )
    __lowerCamelCase       :       Optional[int]           = sampling_rate
    audio_time += delta
    if datetime.datetime.now() > audio_time + 10 * delta:
      # We're late !! SKIP
      continue
    yield item

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = False       )      ->      str:
  __lowerCamelCase       :       Optional[int]           = b''
  __lowerCamelCase       ,__lowerCamelCase       :       Any           = stride
  if stride_left + stride_right >= chunk_len:
    raise ValueError(
        F'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'       )
  __lowerCamelCase       :       str           = 0
  for raw in iterator:
    acc += raw
    if stream and len(_lowerCAmelCase       ) < chunk_len:
      __lowerCamelCase       :       Any           = (_stride_left, 0)
      yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
    else:
      while len(_lowerCAmelCase       ) >= chunk_len:
        # We are flushing the accumulator
        __lowerCamelCase       :       Any           = (_stride_left, stride_right)
        __lowerCamelCase       :       Optional[int]           = {'raw': acc[:chunk_len], 'stride': stride}
        if stream:
          __lowerCamelCase       :       List[str]           = False
        yield item
        __lowerCamelCase       :       Tuple           = stride_left
        __lowerCamelCase       :       Union[str, Any]           = acc[chunk_len - stride_left - stride_right :]
    # Last chunk
  if len(_lowerCAmelCase       ) > stride_left:
    __lowerCamelCase       :       Tuple           = {'raw': acc, 'stride': (_stride_left, 0)}
    if stream:
      __lowerCamelCase       :       List[str]           = False
    yield item


def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Tuple:
  __lowerCamelCase       :       int           = 2**24  # 16Mo
  try:
    with subprocess.Popen(_lowerCAmelCase       ,stdout=subprocess.PIPE       ,bufsize=_lowerCAmelCase       ) as ffmpeg_process:
      while True:
        __lowerCamelCase       :       Union[str, Any]           = ffmpeg_process.stdout.read(_lowerCAmelCase       )
        if raw == b"":
          break
        yield raw
  except FileNotFoundError as error:
    raise ValueError('ffmpeg was not found but is required to stream audio files from filename'       ) from error






