





'''simple docstring'''






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class        __snake_case	(       __SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= ["image_processor", "tokenizer"]
    lowercase       			= "LayoutLMv2ImageProcessor"
    lowercase       			= ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")

    def __init__(       self   :  str   ,						lowerCamelCase   :  Dict=None   ,						lowerCamelCase   :  Optional[int]=None   ,						**lowerCamelCase   :  Optional[Any]		)       ->  Tuple:
      if "feature_extractor" in kwargs:
        warnings.warn(
            """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
            """ instead."""   ,						_snake_case   ,						)
        lowerCAmelCase_      :				List[str]      =       kwargs.pop("""feature_extractor"""		)

      lowerCAmelCase_      :				Tuple      =       image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError("""You need to specify an `image_processor`."""		)
      if tokenizer is None:
        raise ValueError("""You need to specify a `tokenizer`."""		)

      super().__init__(_snake_case   ,						_snake_case		)

    def __call__(       self   :  List[Any]   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  Dict = None   ,						lowerCamelCase   :  str = None   ,						lowerCamelCase   :  str = None   ,						lowerCamelCase   :  Tuple = None   ,						lowerCamelCase   :  Optional[Any] = True   ,						lowerCamelCase   :  Union[str, Any] = False   ,						lowerCamelCase   :  List[Any] = None   ,						lowerCamelCase   :  Dict = None   ,						lowerCamelCase   :  Dict = 0   ,						lowerCamelCase   :  List[Any] = None   ,						lowerCamelCase   :  Tuple = None   ,						lowerCamelCase   :  List[Any] = None   ,						lowerCamelCase   :  Union[str, Any] = False   ,						lowerCamelCase   :  Any = False   ,						lowerCamelCase   :  Tuple = False   ,						lowerCamelCase   :  int = False   ,						lowerCamelCase   :  Optional[Any] = True   ,						lowerCamelCase   :  Union[str, Any] = None   ,						**lowerCamelCase   :  int   ,						)       ->  Any:
      # verify input
      if self.image_processor.apply_ocr and (boxes is not None):
        raise ValueError(
            """You cannot provide bounding boxes """
            """if you initialized the image processor with apply_ocr set to True."""		)

      if self.image_processor.apply_ocr and (word_labels is not None):
        raise ValueError(
            """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""		)

      if return_overflowing_tokens is True and return_offsets_mapping is False:
        raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""		)

      # first, apply the image processor
      lowerCAmelCase_      :				str      =       self.image_processor(images=_snake_case   ,						return_tensors=_snake_case		)

      # second, apply the tokenizer
      if text is not None and self.image_processor.apply_ocr and text_pair is None:
        if isinstance(_snake_case   ,						_snake_case		):
          lowerCAmelCase_      :				List[Any]      =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
        lowerCAmelCase_      :				Optional[Any]      =       features["words"]

      lowerCAmelCase_      :				Union[str, Any]      =       self.tokenizer(
          text=text if text is not None else features["""words"""]   ,						text_pair=text_pair if text_pair is not None else None   ,						boxes=boxes if boxes is not None else features["""boxes"""]   ,						word_labels=_snake_case   ,						add_special_tokens=_snake_case   ,						padding=_snake_case   ,						truncation=_snake_case   ,						max_length=_snake_case   ,						stride=_snake_case   ,						pad_to_multiple_of=_snake_case   ,						return_token_type_ids=_snake_case   ,						return_attention_mask=_snake_case   ,						return_overflowing_tokens=_snake_case   ,						return_special_tokens_mask=_snake_case   ,						return_offsets_mapping=_snake_case   ,						return_length=_snake_case   ,						verbose=_snake_case   ,						return_tensors=_snake_case   ,						**_snake_case   ,						)

      # add pixel values
      lowerCAmelCase_      :				Tuple      =       features.pop("""pixel_values"""		)
      if return_overflowing_tokens is True:
        lowerCAmelCase_      :				Optional[Any]      =       self.get_overflowing_images(_snake_case   ,						encoded_inputs["""overflow_to_sample_mapping"""]		)
      lowerCAmelCase_      :				Optional[Any]      =       images

      return encoded_inputs

    def __lowercase       (       self   :  Tuple   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  Dict		)       ->  Optional[int]:
      # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
      lowerCAmelCase_      :				Any      =       []
      for sample_idx in overflow_to_sample_mapping:
        images_with_overflow.append(images[sample_idx]		)

      if len(_snake_case		) != len(_snake_case		):
        raise ValueError(
            """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
            F' {len(_snake_case		)} and {len(_snake_case		)}'		)

      return images_with_overflow

    def __lowercase       (       self   :  Optional[int]   ,						*lowerCamelCase   :  Union[str, Any]   ,						**lowerCamelCase   :  List[str]		)       ->  Optional[Any]:
      return self.tokenizer.batch_decode(*_snake_case   ,						**_snake_case		)

    def __lowercase       (       self   :  Optional[int]   ,						*lowerCamelCase   :  Optional[int]   ,						**lowerCamelCase   :  int		)       ->  str:
      return self.tokenizer.decode(*_snake_case   ,						**_snake_case		)

    @property
    def __lowercase       (       self   :  Dict		)       ->  List[str]:
      return ["input_ids", "bbox", "attention_mask", "image"]

    @property
    def __lowercase       (       self   :  Tuple		)       ->  List[Any]:
      warnings.warn(
          """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,						_snake_case   ,						)
      return self.image_processor_class





    @property
    def __lowercase       (       self   :  Union[str, Any]		)       ->  Dict:
      warnings.warn(
          """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,						_snake_case   ,						)
      return self.image_processor







'''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



