





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

import numpy as np




def    A_(			A				,      A   ):
	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(A				,      stdin=subprocess.PIPE				,      stdout=subprocess.PIPE   ) as ffmpeg_process:
			UpperCAmelCase_								=     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
	UpperCAmelCase_								=     output_stream[0]
	UpperCAmelCase_								=     np.frombuffer(A				,      np.floataa   )
	if audio.shape[0] == 0:
		raise ValueError("""Malformed soundfile"""   )
	return audio




def    A_(			A				,      A				,      A = "f32le"				,      ):
	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(A				,      A   )
	for item in iterator:
		yield item




def    A_(			A				,      A				,      A = None				,      A = None				,      A = "f32le"				,      ):
	if stream_chunk_s is not None:
		UpperCAmelCase_								=     stream_chunk_s
	else:
		UpperCAmelCase_								=     chunk_length_s

	UpperCAmelCase_								=     ffmpeg_microphone(A				,      A				,      format_for_conversion=A   )
	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(A				,      (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=A   )
	for item in chunk_bytes_iter(A				,      A				,      stride=(stride_left, stride_right)				,      stream=A   ):
		# Put everything back in numpy scale
		UpperCAmelCase_								=     np.frombuffer(item["""raw"""]				,      dtype=A   )
		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 + 10 * delta:
			# We're late !! SKIP
			continue
		yield item




def    A_(			A				,      A				,      A				,      A = False   ):
	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(A   ) < chunk_len:
			UpperCAmelCase_								=     (_stride_left, 0)
			yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
		else:
			while len(A   ) >= 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(A   ) > stride_left:
		UpperCAmelCase_								=     {"""raw""": acc, """stride""": (_stride_left, 0)}
		if stream:
			UpperCAmelCase_								=     False
		yield item




def    A_(			A				,      A   ):
	UpperCAmelCase_								=     2**24  # 16Mo
	try:
		with subprocess.Popen(A				,      stdout=subprocess.PIPE				,      bufsize=A   ) as ffmpeg_process:
			while True:
				UpperCAmelCase_								=     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









from torch import nn







class   _UpperCamelCase  (       nn.Module  ):







					'''simple docstring'''

					def __init__(						self    :  Dict ,					__lowercase    :  List[str] ,					__lowercase    :  Dict		):






						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_								=     class_size
						UpperCAmelCase_								=     embed_size
						# self.mlp1 = nn.Linear(embed_size, embed_size)
						# self.mlp2 = (nn.Linear(embed_size, class_size))
						UpperCAmelCase_								=     nn.Linear(__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.mlp(__lowercase		)
						return logits


