






'''simple docstring'''





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

import numpy as np


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






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

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


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase = "f32le"						,     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Tuple							=    F'''{sampling_rate}'''
  SCREAMING_SNAKE_CASE       :      Tuple							=    "1"
  if format_for_conversion == "s16le":
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    2
  elif format_for_conversion == "f32le":
    SCREAMING_SNAKE_CASE       :      List[Any]							=    4
  else:
    raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''				)

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

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


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase = None						,     lowercase = None						,     lowercase = "f32le"						,     ):

  """simple docstring"""






  if stream_chunk_s is not None:
    SCREAMING_SNAKE_CASE       :      str							=    stream_chunk_s
  else:
    SCREAMING_SNAKE_CASE       :      int							=    chunk_length_s

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

  if stride_length_s is None:
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    chunk_length_s / 6
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    int(round(sampling_rate * chunk_length_s				)				) * size_of_sample
  if isinstance(__UpperCamelCase						,     (int, float)				):
    SCREAMING_SNAKE_CASE       :      Tuple							=    [stride_length_s, stride_length_s]

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


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase						,     lowercase = False				):

  """simple docstring"""






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


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[str]							=    2**24  # 16Mo
  try:
    with subprocess.Popen(__UpperCamelCase						,     stdout=subprocess.PIPE						,     bufsize=__UpperCamelCase				) as ffmpeg_process:
      while True:
        SCREAMING_SNAKE_CASE       :      List[str]							=    ffmpeg_process.stdout.read(__UpperCamelCase				)
        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 ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[int]			=	'''markuplm'''


     def __init__(				self       :	int					, UpperCAmelCase_       :	List[str]=3_0522					, UpperCAmelCase_       :	Optional[Any]=768					, UpperCAmelCase_       :	Any=12					, UpperCAmelCase_       :	Tuple=12					, UpperCAmelCase_       :	Union[str, Any]=3072					, UpperCAmelCase_       :	Dict="gelu"					, UpperCAmelCase_       :	Optional[int]=0.1					, UpperCAmelCase_       :	Tuple=0.1					, UpperCAmelCase_       :	Dict=512					, UpperCAmelCase_       :	Optional[int]=2					, UpperCAmelCase_       :	Any=0.02					, UpperCAmelCase_       :	Dict=1E-12					, UpperCAmelCase_       :	int=0					, UpperCAmelCase_       :	Optional[Any]=0					, UpperCAmelCase_       :	Optional[int]=2					, UpperCAmelCase_       :	str=256					, UpperCAmelCase_       :	str=1024					, UpperCAmelCase_       :	List[str]=216					, UpperCAmelCase_       :	List[Any]=1001					, UpperCAmelCase_       :	Union[str, Any]=32					, UpperCAmelCase_       :	Dict=50					, UpperCAmelCase_       :	Optional[int]="absolute"					, UpperCAmelCase_       :	Optional[int]=True					, UpperCAmelCase_       :	Any=None					, **UpperCAmelCase_       :	Any					, ):
       super().__init__(
           pad_token_id=UpperCAmelCase_					, bos_token_id=UpperCAmelCase_					, eos_token_id=UpperCAmelCase_					, **UpperCAmelCase_					, )
       SCREAMING_SNAKE_CASE       :      Any							=    vocab_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    hidden_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    num_hidden_layers
       SCREAMING_SNAKE_CASE       :      Tuple							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      List[str]							=    hidden_act
       SCREAMING_SNAKE_CASE       :      Tuple							=    intermediate_size
       SCREAMING_SNAKE_CASE       :      Any							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      int							=    max_position_embeddings
       SCREAMING_SNAKE_CASE       :      Tuple							=    type_vocab_size
       SCREAMING_SNAKE_CASE       :      Any							=    initializer_range
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    layer_norm_eps
       SCREAMING_SNAKE_CASE       :      int							=    position_embedding_type
       SCREAMING_SNAKE_CASE       :      List[str]							=    use_cache
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    classifier_dropout
       # additional properties
       SCREAMING_SNAKE_CASE       :      Any							=    max_depth
       SCREAMING_SNAKE_CASE       :      int							=    max_xpath_tag_unit_embeddings
       SCREAMING_SNAKE_CASE       :      List[str]							=    max_xpath_subs_unit_embeddings
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    tag_pad_id
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    subs_pad_id
       SCREAMING_SNAKE_CASE       :      List[Any]							=    xpath_unit_hidden_size






