


'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A :  Any    							=				logging.get_logger(__name__)






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= ['pixel_values']

    def __init__(       self   :  Optional[int]   ,						lowerCamelCase   :  bool = True   ,						lowerCamelCase   :  Optional[Dict[str, int]] = None   ,						lowerCamelCase   :  PILImageResampling = PILImageResampling.BICUBIC   ,						lowerCamelCase   :  bool = True   ,						lowerCamelCase   :  bool = True   ,						lowerCamelCase   :  Union[int, float] = 1 / 2_55   ,						lowerCamelCase   :  Dict[str, int] = None   ,						lowerCamelCase   :  bool = True   ,						lowerCamelCase   :  Optional[Union[float, List[float]]] = None   ,						lowerCamelCase   :  Optional[Union[float, List[float]]] = None   ,						**lowerCamelCase   :  Any   ,						)       ->  None:
      super().__init__(**lowerCamelCase		)
      lowerCAmelCase_      :				int      =       size if size is not None else {"""height""": 2_24, """width""": 2_24}
      lowerCAmelCase_      :				int      =       get_size_dict(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
      lowerCAmelCase_      :				Optional[Any]      =       get_size_dict(lowerCamelCase   ,						default_to_square=lowerCamelCase   ,						param_name="""crop_size"""		)

      lowerCAmelCase_      :				str      =       do_resize
      lowerCAmelCase_      :				int      =       do_rescale
      lowerCAmelCase_      :				Any      =       do_normalize
      lowerCAmelCase_      :				List[Any]      =       do_center_crop
      lowerCAmelCase_      :				str      =       crop_size
      lowerCAmelCase_      :				Tuple      =       size
      lowerCAmelCase_      :				Optional[Any]      =       resample
      lowerCAmelCase_      :				str      =       rescale_factor
      lowerCAmelCase_      :				Optional[Any]      =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      lowerCAmelCase_      :				Union[str, Any]      =       image_std if image_std is not None else IMAGENET_DEFAULT_STD

    def __lowercase       (       self   :  Optional[Any]   ,						lowerCamelCase   :  np.ndarray   ,						lowerCamelCase   :  Dict[str, int]   ,						lowerCamelCase   :  PILImageResampling = PILImageResampling.BILINEAR   ,						lowerCamelCase   :  Optional[Union[str, ChannelDimension]] = None   ,						**lowerCamelCase   :  Optional[Any]   ,						)       ->  np.ndarray:
      lowerCAmelCase_      :				Any      =       get_size_dict(lowerCamelCase		)

      if "shortest_edge" in size:
        lowerCAmelCase_      :				Union[str, Any]      =       get_resize_output_image_size(lowerCamelCase   ,						size=size["""shortest_edge"""]   ,						default_to_square=lowerCamelCase		)
        # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
      elif "height" in size and "width" in size:
        lowerCAmelCase_      :				Dict      =       (size["""height"""], size["""width"""])
      else:
        raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'		)
      return resize(lowerCamelCase   ,						size=lowerCamelCase   ,						resample=lowerCamelCase   ,						data_format=lowerCamelCase   ,						**lowerCamelCase		)

    def __lowercase       (       self   :  Any   ,						lowerCamelCase   :  np.ndarray   ,						lowerCamelCase   :  Dict[str, int]   ,						lowerCamelCase   :  Optional[Union[str, ChannelDimension]] = None   ,						**lowerCamelCase   :  Union[str, Any]   ,						)       ->  np.ndarray:
      lowerCAmelCase_      :				str      =       get_size_dict(lowerCamelCase		)
      if "height" not in size or "width" not in size:
        raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'		)
      return center_crop(lowerCamelCase   ,						size=(size["""height"""], size["""width"""])   ,						data_format=lowerCamelCase   ,						**lowerCamelCase		)

    def __lowercase       (       self   :  int   ,						lowerCamelCase   :  np.ndarray   ,						lowerCamelCase   :  float   ,						lowerCamelCase   :  Optional[Union[str, ChannelDimension]] = None   ,						**lowerCamelCase   :  List[Any]		)       ->  np.ndarray:
      return rescale(lowerCamelCase   ,						scale=lowerCamelCase   ,						data_format=lowerCamelCase   ,						**lowerCamelCase		)

    def __lowercase       (       self   :  Tuple   ,						lowerCamelCase   :  np.ndarray   ,						lowerCamelCase   :  Union[float, List[float]]   ,						lowerCamelCase   :  Union[float, List[float]]   ,						lowerCamelCase   :  Optional[Union[str, ChannelDimension]] = None   ,						**lowerCamelCase   :  Any   ,						)       ->  np.ndarray:
      return normalize(lowerCamelCase   ,						mean=lowerCamelCase   ,						std=lowerCamelCase   ,						data_format=lowerCamelCase   ,						**lowerCamelCase		)





    def __lowercase       (       self   :  str   ,						lowerCamelCase   :  ImageInput   ,						lowerCamelCase   :  Optional[bool] = None   ,						lowerCamelCase   :  Dict[str, int] = None   ,						lowerCamelCase   :  PILImageResampling = None   ,						lowerCamelCase   :  bool = None   ,						lowerCamelCase   :  int = None   ,						lowerCamelCase   :  Optional[bool] = None   ,						lowerCamelCase   :  Optional[float] = None   ,						lowerCamelCase   :  Optional[bool] = None   ,						lowerCamelCase   :  Optional[Union[float, List[float]]] = None   ,						lowerCamelCase   :  Optional[Union[float, List[float]]] = None   ,						lowerCamelCase   :  Optional[Union[str, TensorType]] = None   ,						lowerCamelCase   :  Union[str, ChannelDimension] = ChannelDimension.FIRST   ,						**lowerCamelCase   :  Optional[int]   ,						)       ->  BatchFeature:
      lowerCAmelCase_      :				List[Any]      =       do_resize if do_resize is not None else self.do_resize
      lowerCAmelCase_      :				List[Any]      =       do_rescale if do_rescale is not None else self.do_rescale
      lowerCAmelCase_      :				Any      =       do_normalize if do_normalize is not None else self.do_normalize
      lowerCAmelCase_      :				Union[str, Any]      =       do_center_crop if do_center_crop is not None else self.do_center_crop
      lowerCAmelCase_      :				Any      =       crop_size if crop_size is not None else self.crop_size
      lowerCAmelCase_      :				Union[str, Any]      =       get_size_dict(lowerCamelCase   ,						param_name="""crop_size"""   ,						default_to_square=lowerCamelCase		)
      lowerCAmelCase_      :				Tuple      =       resample if resample is not None else self.resample
      lowerCAmelCase_      :				Dict      =       rescale_factor if rescale_factor is not None else self.rescale_factor
      lowerCAmelCase_      :				List[str]      =       image_mean if image_mean is not None else self.image_mean
      lowerCAmelCase_      :				List[str]      =       image_std if image_std is not None else self.image_std

      lowerCAmelCase_      :				List[Any]      =       size if size is not None else self.size
      lowerCAmelCase_      :				Dict      =       get_size_dict(lowerCamelCase		)

      if not is_batched(lowerCamelCase		):
        lowerCAmelCase_      :				str      =       [images]

      if not valid_images(lowerCamelCase		):
        raise ValueError(
            """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
            """torch.Tensor, tf.Tensor or jax.ndarray."""		)

      if do_resize and size is None:
        raise ValueError("""Size must be specified if do_resize is True."""		)

      if do_center_crop and crop_size is None:
        raise ValueError("""Crop size must be specified if do_center_crop is True."""		)

      if do_rescale and rescale_factor is None:
        raise ValueError("""Rescale factor must be specified if do_rescale is True."""		)

      # All transformations expect numpy arrays.
      lowerCAmelCase_      :				Union[str, Any]      =       [to_numpy_array(lowerCamelCase		) for image in images]

      if do_resize:
        lowerCAmelCase_      :				Any      =       [self.resize(image=lowerCamelCase   ,						size=lowerCamelCase   ,						resample=lowerCamelCase		) for image in images]

      if do_center_crop:
        lowerCAmelCase_      :				Optional[int]      =       [self.center_crop(image=lowerCamelCase   ,						size=lowerCamelCase		) for image in images]

      if do_rescale:
        lowerCAmelCase_      :				Union[str, Any]      =       [self.rescale(image=lowerCamelCase   ,						scale=lowerCamelCase		) for image in images]

      if do_normalize:
        lowerCAmelCase_      :				Dict      =       [self.normalize(image=lowerCamelCase   ,						mean=lowerCamelCase   ,						std=lowerCamelCase		) for image in images]

      lowerCAmelCase_      :				List[str]      =       [to_channel_dimension_format(lowerCamelCase   ,						lowerCamelCase		) for image in images]

      lowerCAmelCase_      :				Union[str, Any]      =       {"""pixel_values""": images}
      return BatchFeature(data=lowerCamelCase   ,						tensor_type=lowerCamelCase		)







'''simple docstring'''






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

import numpy as np







def     UpperCamelCase_      (   A__ :       bytes							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       f'{sampling_rate}'
  lowerCAmelCase_      :				str      =       """1"""
  lowerCAmelCase_      :				Optional[int]      =       """f32le"""
  lowerCAmelCase_      :				Any      =       [
      """ffmpeg""",
      """-i""",
      """pipe:0""",
      """-ac""",
      ac,
      """-ar""",
      ar,
      """-f""",
      format_for_conversion,
      """-hide_banner""",
      """-loglevel""",
      """quiet""",
      """pipe:1""",
  ]

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







def     UpperCamelCase_      (   A__ :       int							,   A__ :       float							,   A__ :       str = "f32le"							,   ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       f'{sampling_rate}'
  lowerCAmelCase_      :				Any      =       """1"""
  if format_for_conversion == "s16le":
    lowerCAmelCase_      :				Optional[Any]      =       2
  elif format_for_conversion == "f32le":
    lowerCAmelCase_      :				Union[str, Any]      =       4
  else:
    raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

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

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







def     UpperCamelCase_      (   A__ :       int							,   A__ :       float							,   A__ :       Optional[int] = None							,   A__ :       Optional[Union[Tuple[float, float], float]] = None							,   A__ :       str = "f32le"							,   ):
  '''simple docstring'''





  if stream_chunk_s is not None:
    lowerCAmelCase_      :				Union[str, Any]      =       stream_chunk_s
  else:
    lowerCAmelCase_      :				Tuple      =       chunk_length_s

  lowerCAmelCase_      :				List[Any]      =       ffmpeg_microphone(A__							,   A__							,   format_for_conversion=A__ )
  if format_for_conversion == "s16le":
    lowerCAmelCase_      :				Tuple      =       np.intaa
    lowerCAmelCase_      :				List[Any]      =       2
  elif format_for_conversion == "f32le":
    lowerCAmelCase_      :				Dict      =       np.floataa
    lowerCAmelCase_      :				int      =       4
  else:
    raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

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

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







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       int							,   A__ :       Tuple[int, int]							,   A__ :       bool = False ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       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_      :				Union[str, Any]      =       0
  for raw in iterator:
    acc += raw
    if stream and len(A__ ) < chunk_len:
      lowerCAmelCase_      :				Dict      =       (_stride_left, 0)
      yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
    else:
      while len(A__ ) >= chunk_len:
        # We are flushing the accumulator
        lowerCAmelCase_      :				Optional[Any]      =       (_stride_left, stride_right)
        lowerCAmelCase_      :				List[str]      =       {"""raw""": acc[:chunk_len], """stride""": stride}
        if stream:
          lowerCAmelCase_      :				List[Any]      =       False
        yield item
        lowerCAmelCase_      :				str      =       stride_left
        lowerCAmelCase_      :				str      =       acc[chunk_len - stride_left - stride_right :]
    # Last chunk
  if len(A__ ) > stride_left:
    lowerCAmelCase_      :				Tuple      =       {"""raw""": acc, """stride""": (_stride_left, 0)}
    if stream:
      lowerCAmelCase_      :				Optional[Any]      =       False
    yield item







def     UpperCamelCase_      (   A__ :       List[str]							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       2**24  # 16Mo
  try:
    with subprocess.Popen(A__							,   stdout=subprocess.PIPE							,   bufsize=A__ ) as ffmpeg_process:
      while True:
        lowerCAmelCase_      :				Union[str, Any]      =       ffmpeg_process.stdout.read(A__ )
        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



