


'''simple docstring'''






import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)







def     UpperCamelCase_      (   A__ :       Optional[int] ):
  '''simple docstring'''





  lowerCAmelCase_      :				Union[str, Any]      =       VideoMAEConfig()

  set_architecture_configs(A__							,   A__ )

  if "finetuned" not in model_name:
    lowerCAmelCase_      :				Any      =       False

  if "finetuned" in model_name:
    lowerCAmelCase_      :				List[Any]      =       """huggingface/label-files"""
    if "kinetics" in model_name:
      lowerCAmelCase_      :				Any      =       4_00
      lowerCAmelCase_      :				Optional[Any]      =       """kinetics400-id2label.json"""
    elif "ssv2" in model_name:
      lowerCAmelCase_      :				Optional[Any]      =       1_74
      lowerCAmelCase_      :				Dict      =       """something-something-v2-id2label.json"""
    else:
      raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned.""" )
    lowerCAmelCase_      :				Optional[Any]      =       json.load(open(hf_hub_download(A__							,   A__							,   repo_type="""dataset""" )							,   """r""" ) )
    lowerCAmelCase_      :				Tuple      =       {int(A__ ): v for k, v in idalabel.items()}
    lowerCAmelCase_      :				List[Any]      =       idalabel
    lowerCAmelCase_      :				Any      =       {v: k for k, v in idalabel.items()}

  return config







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





  if "small" in model_name:
    lowerCAmelCase_      :				List[str]      =       3_84
    lowerCAmelCase_      :				List[str]      =       15_36
    lowerCAmelCase_      :				Tuple      =       12
    lowerCAmelCase_      :				Dict      =       16
    lowerCAmelCase_      :				List[Any]      =       12
    lowerCAmelCase_      :				Any      =       3
    lowerCAmelCase_      :				str      =       1_92
    lowerCAmelCase_      :				List[Any]      =       7_68
  elif "large" in model_name:
    lowerCAmelCase_      :				Dict      =       10_24
    lowerCAmelCase_      :				Union[str, Any]      =       40_96
    lowerCAmelCase_      :				Dict      =       24
    lowerCAmelCase_      :				Optional[Any]      =       16
    lowerCAmelCase_      :				Optional[Any]      =       12
    lowerCAmelCase_      :				List[Any]      =       8
    lowerCAmelCase_      :				Union[str, Any]      =       5_12
    lowerCAmelCase_      :				str      =       20_48
  elif "huge" in model_name:
    lowerCAmelCase_      :				Optional[Any]      =       12_80
    lowerCAmelCase_      :				List[Any]      =       51_20
    lowerCAmelCase_      :				int      =       32
    lowerCAmelCase_      :				Optional[Any]      =       16
    lowerCAmelCase_      :				Optional[Any]      =       12
    lowerCAmelCase_      :				List[Any]      =       8
    lowerCAmelCase_      :				int      =       6_40
    lowerCAmelCase_      :				int      =       25_60
  elif "base" not in model_name:
    raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\"""" )







def     UpperCamelCase_      (   A__ :       Union[str, Any] ):
  '''simple docstring'''





  if "encoder." in name:
    lowerCAmelCase_      :				Union[str, Any]      =       name.replace("""encoder."""							,   """""" )
  if "cls_token" in name:
    lowerCAmelCase_      :				int      =       name.replace("""cls_token"""							,   """videomae.embeddings.cls_token""" )
  if "decoder_pos_embed" in name:
    lowerCAmelCase_      :				Union[str, Any]      =       name.replace("""decoder_pos_embed"""							,   """decoder.decoder_pos_embed""" )
  if "pos_embed" in name and "decoder" not in name:
    lowerCAmelCase_      :				str      =       name.replace("""pos_embed"""							,   """videomae.embeddings.position_embeddings""" )
  if "patch_embed.proj" in name:
    lowerCAmelCase_      :				str      =       name.replace("""patch_embed.proj"""							,   """videomae.embeddings.patch_embeddings.projection""" )
  if "patch_embed.norm" in name:
    lowerCAmelCase_      :				str      =       name.replace("""patch_embed.norm"""							,   """videomae.embeddings.norm""" )
  if "decoder.blocks" in name:
    lowerCAmelCase_      :				Dict      =       name.replace("""decoder.blocks"""							,   """decoder.decoder_layers""" )
  if "blocks" in name:
    lowerCAmelCase_      :				str      =       name.replace("""blocks"""							,   """videomae.encoder.layer""" )
  if "attn.proj" in name:
    lowerCAmelCase_      :				str      =       name.replace("""attn.proj"""							,   """attention.output.dense""" )
  if "attn" in name and "bias" not in name:
    lowerCAmelCase_      :				Union[str, Any]      =       name.replace("""attn"""							,   """attention.self""" )
  if "attn" in name:
    lowerCAmelCase_      :				Optional[Any]      =       name.replace("""attn"""							,   """attention.attention""" )
  if "norm1" in name:
    lowerCAmelCase_      :				Union[str, Any]      =       name.replace("""norm1"""							,   """layernorm_before""" )
  if "norm2" in name:
    lowerCAmelCase_      :				List[Any]      =       name.replace("""norm2"""							,   """layernorm_after""" )
  if "mlp.fc1" in name:
    lowerCAmelCase_      :				Optional[int]      =       name.replace("""mlp.fc1"""							,   """intermediate.dense""" )
  if "mlp.fc2" in name:
    lowerCAmelCase_      :				Tuple      =       name.replace("""mlp.fc2"""							,   """output.dense""" )
  if "decoder_embed" in name:
    lowerCAmelCase_      :				Union[str, Any]      =       name.replace("""decoder_embed"""							,   """decoder.decoder_embed""" )
  if "decoder_norm" in name:
    lowerCAmelCase_      :				str      =       name.replace("""decoder_norm"""							,   """decoder.decoder_norm""" )
  if "decoder_pred" in name:
    lowerCAmelCase_      :				int      =       name.replace("""decoder_pred"""							,   """decoder.decoder_pred""" )
  if "norm.weight" in name and "decoder" not in name and "fc" not in name:
    lowerCAmelCase_      :				Tuple      =       name.replace("""norm.weight"""							,   """videomae.layernorm.weight""" )
  if "norm.bias" in name and "decoder" not in name and "fc" not in name:
    lowerCAmelCase_      :				List[str]      =       name.replace("""norm.bias"""							,   """videomae.layernorm.bias""" )
  if "head" in name and "decoder" not in name:
    lowerCAmelCase_      :				Dict      =       name.replace("""head"""							,   """classifier""" )

  return name







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





  for key in orig_state_dict.copy().keys():
    lowerCAmelCase_      :				Any      =       orig_state_dict.pop(A__ )

    if key.startswith("""encoder.""" ):
      lowerCAmelCase_      :				List[Any]      =       key.replace("""encoder."""							,   """""" )

    if "qkv" in key:
      lowerCAmelCase_      :				str      =       key.split(""".""" )
      if key.startswith("""decoder.blocks""" ):
        lowerCAmelCase_      :				Dict      =       config.decoder_hidden_size
        lowerCAmelCase_      :				Tuple      =       int(key_split[2] )
        lowerCAmelCase_      :				List[Any]      =       """decoder.decoder_layers."""
        if "weight" in key:
          lowerCAmelCase_      :				Optional[int]      =       val[:dim, :]
          lowerCAmelCase_      :				Optional[Any]      =       val[dim : dim * 2, :]
          lowerCAmelCase_      :				str      =       val[-dim:, :]
      else:
        lowerCAmelCase_      :				str      =       config.hidden_size
        lowerCAmelCase_      :				Optional[Any]      =       int(key_split[1] )
        lowerCAmelCase_      :				int      =       """videomae.encoder.layer."""
        if "weight" in key:
          lowerCAmelCase_      :				List[str]      =       val[:dim, :]
          lowerCAmelCase_      :				Tuple      =       val[dim : dim * 2, :]
          lowerCAmelCase_      :				int      =       val[-dim:, :]
    else:
      lowerCAmelCase_      :				List[Any]      =       val

  return orig_state_dict







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[Any]      =       hf_hub_download(
      repo_id="""hf-internal-testing/spaghetti-video"""							,   filename="""eating_spaghetti.npy"""							,   repo_type="""dataset""" )
  lowerCAmelCase_      :				int      =       np.load(A__ )
  return list(A__ )







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





  lowerCAmelCase_      :				Tuple      =       get_videomae_config(A__ )

  if "finetuned" in model_name:
    lowerCAmelCase_      :				Optional[Any]      =       VideoMAEForVideoClassification(A__ )
  else:
    lowerCAmelCase_      :				Dict      =       VideoMAEForPreTraining(A__ )

  # download original checkpoint, hosted on Google Drive
  lowerCAmelCase_      :				Dict      =       """pytorch_model.bin"""
  gdown.cached_download(A__							,   A__							,   quiet=A__ )
  lowerCAmelCase_      :				Tuple      =       torch.load(A__							,   map_location="""cpu""" )
  if "model" in files:
    lowerCAmelCase_      :				Optional[int]      =       files["""model"""]
  else:
    lowerCAmelCase_      :				Union[str, Any]      =       files["""module"""]
  lowerCAmelCase_      :				Dict      =       convert_state_dict(A__							,   A__ )

  model.load_state_dict(A__ )
  model.eval()

  # verify model on basic input
  lowerCAmelCase_      :				List[str]      =       VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]							,   image_std=[0.5, 0.5, 0.5] )
  lowerCAmelCase_      :				Dict      =       prepare_video()
  lowerCAmelCase_      :				Tuple      =       image_processor(A__							,   return_tensors="""pt""" )

  if "finetuned" not in model_name:
    lowerCAmelCase_      :				int      =       hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos"""							,   filename="""bool_masked_pos.pt""" )
    lowerCAmelCase_      :				List[Any]      =       torch.load(A__ )

  lowerCAmelCase_      :				Union[str, Any]      =       model(**A__ )
  lowerCAmelCase_      :				List[str]      =       outputs.logits

  lowerCAmelCase_      :				Dict      =       [
      """videomae-small-finetuned-kinetics""",
      """videomae-small-finetuned-ssv2""",
      # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
      """videomae-base-short""",
      """videomae-base-short-finetuned-kinetics""",
      """videomae-base""",
      """videomae-base-finetuned-kinetics""",
      """videomae-large""",
      """videomae-large-finetuned-kinetics""",
      """videomae-huge-finetuned-kinetics""",
      # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
      """videomae-base-short-ssv2""",
      """videomae-base-short-finetuned-ssv2""",
      """videomae-base-ssv2""",
      """videomae-base-finetuned-ssv2""",
  ]

  # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
  if model_name == "videomae-small-finetuned-kinetics":
    lowerCAmelCase_      :				int      =       torch.Size([1, 4_00] )
    lowerCAmelCase_      :				Union[str, Any]      =       torch.tensor([-0.9291, -0.4061, -0.9307] )
  elif model_name == "videomae-small-finetuned-ssv2":
    lowerCAmelCase_      :				Union[str, Any]      =       torch.Size([1, 1_74] )
    lowerCAmelCase_      :				str      =       torch.tensor([0.2671, -0.4689, -0.8235] )
  elif model_name == "videomae-base":
    lowerCAmelCase_      :				str      =       torch.Size([1, 14_08, 15_36] )
    lowerCAmelCase_      :				Optional[Any]      =       torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]] )
  elif model_name == "videomae-base-short":
    lowerCAmelCase_      :				Optional[Any]      =       torch.Size([1, 14_08, 15_36] )
    lowerCAmelCase_      :				List[str]      =       torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]] )
    # we verified the loss both for normalized and unnormalized targets for this one
    lowerCAmelCase_      :				int      =       torch.tensor([0.5142] ) if config.norm_pix_loss else torch.tensor([0.6469] )
  elif model_name == "videomae-large":
    lowerCAmelCase_      :				Optional[Any]      =       torch.Size([1, 14_08, 15_36] )
    lowerCAmelCase_      :				str      =       torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]] )
  elif model_name == "videomae-large-finetuned-kinetics":
    lowerCAmelCase_      :				Optional[int]      =       torch.Size([1, 4_00] )
    lowerCAmelCase_      :				Dict      =       torch.tensor([0.0771, 0.0011, -0.3625] )
  elif model_name == "videomae-huge-finetuned-kinetics":
    lowerCAmelCase_      :				int      =       torch.Size([1, 4_00] )
    lowerCAmelCase_      :				List[str]      =       torch.tensor([0.2433, 0.1632, -0.4894] )
  elif model_name == "videomae-base-short-finetuned-kinetics":
    lowerCAmelCase_      :				List[Any]      =       torch.Size([1, 4_00] )
    lowerCAmelCase_      :				Optional[Any]      =       torch.tensor([0.6588, 0.0990, -0.2493] )
  elif model_name == "videomae-base-finetuned-kinetics":
    lowerCAmelCase_      :				List[Any]      =       torch.Size([1, 4_00] )
    lowerCAmelCase_      :				Dict      =       torch.tensor([0.3669, -0.0688, -0.2421] )
  elif model_name == "videomae-base-short-ssv2":
    lowerCAmelCase_      :				Dict      =       torch.Size([1, 14_08, 15_36] )
    lowerCAmelCase_      :				Dict      =       torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]] )
  elif model_name == "videomae-base-short-finetuned-ssv2":
    lowerCAmelCase_      :				int      =       torch.Size([1, 1_74] )
    lowerCAmelCase_      :				List[str]      =       torch.tensor([-0.0537, -0.1539, -0.3266] )
  elif model_name == "videomae-base-ssv2":
    lowerCAmelCase_      :				Dict      =       torch.Size([1, 14_08, 15_36] )
    lowerCAmelCase_      :				Any      =       torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]] )
  elif model_name == "videomae-base-finetuned-ssv2":
    lowerCAmelCase_      :				int      =       torch.Size([1, 1_74] )
    lowerCAmelCase_      :				List[str]      =       torch.tensor([0.1961, -0.8337, -0.6389] )
  else:
    raise ValueError(f'Model name not supported. Should be one of {model_names}' )

  # verify logits
  assert logits.shape == expected_shape
  if "finetuned" in model_name:
    assert torch.allclose(logits[0, :3]							,   A__							,   atol=1E-4 )
  else:
    print("""Logits:"""							,   logits[0, :3, :3] )
    assert torch.allclose(logits[0, :3, :3]							,   A__							,   atol=1E-4 )
  print("""Logits ok!""" )

  # verify loss, if applicable
  if model_name == "videomae-base-short":
    lowerCAmelCase_      :				Any      =       outputs.loss
    assert torch.allclose(A__							,   A__							,   atol=1E-4 )
    print("""Loss ok!""" )

  if pytorch_dump_folder_path is not None:
    print(f'Saving model and image processor to {pytorch_dump_folder_path}' )
    image_processor.save_pretrained(A__ )
    model.save_pretrained(A__ )

  if push_to_hub:
    print("""Pushing to the hub...""" )
    model.push_to_hub(A__							,   organization="""nielsr""" )


if __name__ == "__main__":
  __A :  Optional[Any]    							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--checkpoint_url",
      default="https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4",
      type=str,
      help=(
          "URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"
          " download link."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default="/Users/nielsrogge/Documents/VideoMAE/Test",
      type=str,
      help="Path to the output PyTorch model directory.",
  )
  parser.add_argument("--model_name", default="videomae-base", type=str, help="Name of the model.")
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
  )

  __A :  Optional[int]    							=				parser.parse_args()
  convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)







'''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



