




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

import numpy as np

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       bytes	,							lowercase	:       int     ):


      '''simple docstring'''



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

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

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       int	,							lowercase	:       float	,							lowercase	:       str = "f32le"	,							):


      '''simple docstring'''



      lowerCamelCase_		     =    f"""{sampling_rate}"""
      lowerCamelCase_		     =    '1'
      if format_for_conversion == "s16le":
            lowerCamelCase_		     =    2
      elif format_for_conversion == "f32le":
            lowerCamelCase_		     =    4
      else:
            raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""     )

      lowerCamelCase_		     =    platform.system()
      if system == "Linux":
            lowerCamelCase_		     =    'alsa'
            lowerCamelCase_		     =    'default'
      elif system == "Darwin":
            lowerCamelCase_		     =    'avfoundation'
            lowerCamelCase_		     =    ':0'
      elif system == "Windows":
            lowerCamelCase_		     =    'dshow'
            lowerCamelCase_		     =    'default'

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

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       int	,							lowercase	:       float	,							lowercase	:       Optional[int] = None	,							lowercase	:       Optional[Union[Tuple[float, float], float]] = None	,							lowercase	:       str = "f32le"	,							):


      '''simple docstring'''



      if stream_chunk_s is not None:
            lowerCamelCase_		     =    stream_chunk_s
      else:
            lowerCamelCase_		     =    chunk_length_s

      lowerCamelCase_		     =    ffmpeg_microphone(lowercase	,							lowercase	,							format_for_conversion=lowercase     )
      if format_for_conversion == "s16le":
            lowerCamelCase_		     =    np.intaa
            lowerCamelCase_		     =    2
      elif format_for_conversion == "f32le":
            lowerCamelCase_		     =    np.floataa
            lowerCamelCase_		     =    4
      else:
            raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""     )

      if stride_length_s is None:
            lowerCamelCase_		     =    chunk_length_s / 6
      lowerCamelCase_		     =    int(round(sampling_rate * chunk_length_s     )     ) * size_of_sample
      if isinstance(lowercase	,							(int, float)     ):
            lowerCamelCase_		     =    [stride_length_s, stride_length_s]

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

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Dict	,							lowercase	:       int	,							lowercase	:       Tuple[int, int]	,							lowercase	:       bool = False     ):


      '''simple docstring'''



      lowerCamelCase_		     =    b''
      lowerCamelCase_     ,				lowerCamelCase_		     =    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_		     =    0
      for raw in iterator:
            acc += raw
            if stream and len(lowercase     ) < chunk_len:
                  lowerCamelCase_		     =    (_stride_left, 0)
                  yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
            else:
                  while len(lowercase     ) >= chunk_len:
                        # We are flushing the accumulator
                        lowerCamelCase_		     =    (_stride_left, stride_right)
                        lowerCamelCase_		     =    {'raw': acc[:chunk_len], 'stride': stride}
                        if stream:
                              lowerCamelCase_		     =    False
                        yield item
                        lowerCamelCase_		     =    stride_left
                        lowerCamelCase_		     =    acc[chunk_len - stride_left - stride_right :]
    # Last chunk
      if len(lowercase     ) > stride_left:
            lowerCamelCase_		     =    {'raw': acc, 'stride': (_stride_left, 0)}
            if stream:
                  lowerCamelCase_		     =    False
            yield item






def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]	,							lowercase	:       int     ):


      '''simple docstring'''



      lowerCamelCase_		     =    2**24  # 16Mo
      try:
            with subprocess.Popen(lowercase	,							stdout=subprocess.PIPE	,							bufsize=lowercase     ) as ffmpeg_process:
                  while True:
                        lowerCamelCase_		     =    ffmpeg_process.stdout.read(lowercase     )
                        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









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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase				:		Optional[Any]     =   logging.get_logger(__name__)


if is_vision_available():
 import PIL







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						['''pixel_values''']

 def __init__(				self					:  List[Any]						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = PILImageResampling.BICUBIC						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  bool = True						,							A_					:  Union[int, float] = 1 / 255						,							A_					:  bool = True						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  bool = True						,							**A_					:  Dict						,							)   ->	None:

       """simple docstring"""






       super().__init__(**A_  )
       lowerCamelCase_		     =    size if size is not None else {'shortest_edge': 224}
       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_  )
       lowerCamelCase_		     =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_						,							param_name='crop_size'  )

       lowerCamelCase_		     =    do_resize
       lowerCamelCase_		     =    size
       lowerCamelCase_		     =    resample
       lowerCamelCase_		     =    do_center_crop
       lowerCamelCase_		     =    crop_size
       lowerCamelCase_		     =    do_rescale
       lowerCamelCase_		     =    rescale_factor
       lowerCamelCase_		     =    do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       lowerCamelCase_		     =    image_std if image_std is not None else OPENAI_CLIP_STD
       lowerCamelCase_		     =    do_convert_rgb

 def 				a__ (				self					:  Dict						,							A_					:  np.ndarray						,							A_					:  Dict[str, int]						,							A_					:  PILImageResampling = PILImageResampling.BICUBIC						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Union[str, Any]						,							)   ->	np.ndarray:

       """simple docstring"""






       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_  )
       if "shortest_edge" not in size:
             raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""  )
       lowerCamelCase_		     =    get_resize_output_image_size(A_						,							size=size['shortest_edge']						,							default_to_square=A_  )
       return resize(A_						,							size=A_						,							resample=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Tuple						,							A_					:  np.ndarray						,							A_					:  Dict[str, int]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Dict						,							)   ->	np.ndarray:

       """simple docstring"""






       lowerCamelCase_		     =    get_size_dict(A_  )
       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(A_						,							size=(size['height'], size['width'])						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  str						,							A_					:  np.ndarray						,							A_					:  Union[int, float]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Union[str, Any]						,							)   ->	str:

       """simple docstring"""






       return rescale(A_						,							scale=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Optional[Any]						,							A_					:  np.ndarray						,							A_					:  Union[float, List[float]]						,							A_					:  Union[float, List[float]]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  str						,							)   ->	np.ndarray:

       """simple docstring"""






       return normalize(A_						,							mean=A_						,							std=A_						,							data_format=A_						,							**A_  )





 def 				a__ (				self					:  Any						,							A_					:  ImageInput						,							A_					:  bool = None						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = None						,							A_					:  bool = None						,							A_					:  int = None						,							A_					:  bool = None						,							A_					:  float = None						,							A_					:  bool = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  bool = None						,							A_					:  Optional[Union[str, TensorType]] = None						,							A_					:  Optional[ChannelDimension] = ChannelDimension.FIRST						,							**A_					:  Dict						,							)   ->	PIL.Image.Image:

       """simple docstring"""






       lowerCamelCase_		     =    do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_		     =    size if size is not None else self.size
       lowerCamelCase_		     =    get_size_dict(A_						,							param_name='size'						,							default_to_square=A_  )
       lowerCamelCase_		     =    resample if resample is not None else self.resample
       lowerCamelCase_		     =    do_center_crop if do_center_crop is not None else self.do_center_crop
       lowerCamelCase_		     =    crop_size if crop_size is not None else self.crop_size
       lowerCamelCase_		     =    get_size_dict(A_						,							param_name='crop_size'						,							default_to_square=A_  )
       lowerCamelCase_		     =    do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_		     =    rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_		     =    do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_		     =    image_std if image_std is not None else self.image_std
       lowerCamelCase_		     =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       lowerCamelCase_		     =    make_list_of_images(A_  )

       if not valid_images(A_  ):
             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.'  )

       if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and std must be specified if do_normalize is True.'  )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
             lowerCamelCase_		     =    [convert_to_rgb(A_  ) for image in images]

       # All transformations expect numpy arrays.
       lowerCamelCase_		     =    [to_numpy_array(A_  ) for image in images]

       if do_resize:
             lowerCamelCase_		     =    [self.resize(image=A_						,							size=A_						,							resample=A_  ) for image in images]

       if do_center_crop:
             lowerCamelCase_		     =    [self.center_crop(image=A_						,							size=A_  ) for image in images]

       if do_rescale:
             lowerCamelCase_		     =    [self.rescale(image=A_						,							scale=A_  ) for image in images]

       if do_normalize:
             lowerCamelCase_		     =    [self.normalize(image=A_						,							mean=A_						,							std=A_  ) for image in images]

       lowerCamelCase_		     =    [to_channel_dimension_format(A_						,							A_  ) for image in images]

       lowerCamelCase_		     =    {'pixel_values': images}
       return BatchFeature(data=A_						,							tensor_type=A_  )



