




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

import numpy as np




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Union[str, Any]:
						_UpperCAmelCase      =			f"{sampling_rate}"
						_UpperCAmelCase      =			"""1"""
						_UpperCAmelCase      =			"""f32le"""
						_UpperCAmelCase      =			[
						    """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:
																		_UpperCAmelCase      =			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
						_UpperCAmelCase      =			output_stream[0]
						_UpperCAmelCase      =			np.frombuffer(_lowercase			,     np.floataa       )
						if audio.shape[0] == 0:
												raise ValueError("""Malformed soundfile"""       )
						return audio




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case = "f32le"			,     )						->				List[str]:
						_UpperCAmelCase      =			f"{sampling_rate}"
						_UpperCAmelCase      =			"""1"""
						if format_for_conversion == "s16le":
												_UpperCAmelCase      =			2
						elif format_for_conversion == "f32le":
												_UpperCAmelCase      =			4
						else:
												raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"       )

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

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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case = None			,     snake_case = None			,     snake_case = "f32le"			,     )						->				str:







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

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

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

						_UpperCAmelCase      =			int(round(sampling_rate * stride_length_s[0]       )       ) * size_of_sample
						_UpperCAmelCase      =			int(round(sampling_rate * stride_length_s[1]       )       ) * size_of_sample
						_UpperCAmelCase      =			datetime.datetime.now()
						_UpperCAmelCase      =			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
												_UpperCAmelCase      =			np.frombuffer(item["""raw"""]			,     dtype=_lowercase       )
												_UpperCAmelCase      =			(
												    item["""stride"""][0] // size_of_sample,
												    item["""stride"""][1] // size_of_sample,
												)
												_UpperCAmelCase      =			sampling_rate
												audio_time += delta
												if datetime.datetime.now() > audio_time + 1_0 * delta:
																		# We're late !! SKIP
																		continue
												yield item




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







def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Any:
						_UpperCAmelCase      =			2**2_4  # 16Mo
						try:
												with subprocess.Popen(_lowercase			,     stdout=subprocess.PIPE			,     bufsize=_lowercase       ) as ffmpeg_process:
																		while True:
																								_UpperCAmelCase      =			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 Union

import fire
import torch
from tqdm import tqdm




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case = "cpu"			,     snake_case = None       )						->				None:
						_UpperCAmelCase      =			torch.load(snake_case			,     map_location=snake_case       )
						for k, v in tqdm(state_dict.items()       ):
												if not isinstance(snake_case			,     torch.Tensor       ):
																		raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""       )
												_UpperCAmelCase      =			v.half()
						if save_path is None:  # overwrite src_path
												_UpperCAmelCase      =			src_path
						torch.save(snake_case			,     snake_case       )


if __name__ == "__main__":
				fire.Fire(convert)