

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

import numpy as np



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Optional[int]									= f"{sampling_rate}"
	UpperCAmelCase__:	Optional[Any]									= "1"
	UpperCAmelCase__:	List[str]									= "f32le"
	UpperCAmelCase__:	List[str]									= [
	    "ffmpeg",
	    "-i",
	    "pipe:0",
	    "-ac",
	    ac,
	    "-ar",
	    ar,
	    "-f",
	    format_for_conversion,
	    "-hide_banner",
	    "-loglevel",
	    "quiet",
	    "pipe:1",
	]

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



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = "f32le"	,):
	UpperCAmelCase__:	str									= f"{sampling_rate}"
	UpperCAmelCase__:	List[str]									= "1"
	if format_for_conversion == "s16le":
		UpperCAmelCase__:	List[Any]									= 2
	elif format_for_conversion == "f32le":
		UpperCAmelCase__:	int									= 4
	else:
		raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"      )

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

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



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = None	,SCREAMING_SNAKE_CASE = None	,SCREAMING_SNAKE_CASE = "f32le"	,):
	if stream_chunk_s is not None:
		UpperCAmelCase__:	str									= stream_chunk_s
	else:
		UpperCAmelCase__:	Optional[int]									= chunk_length_s

	UpperCAmelCase__:	Dict									= ffmpeg_microphone(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,format_for_conversion=SCREAMING_SNAKE_CASE      )
	if format_for_conversion == "s16le":
		UpperCAmelCase__:	List[str]									= np.intaa
		UpperCAmelCase__:	List[Any]									= 2
	elif format_for_conversion == "f32le":
		UpperCAmelCase__:	Any									= np.floataa
		UpperCAmelCase__:	str									= 4
	else:
		raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"      )

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

	UpperCAmelCase__:	Tuple									= int(round(sampling_rate * stride_length_s[0]      )      ) * size_of_sample
	UpperCAmelCase__:	List[Any]									= int(round(sampling_rate * stride_length_s[1]      )      ) * size_of_sample
	UpperCAmelCase__:	Optional[Any]									= datetime.datetime.now()
	UpperCAmelCase__:	int									= datetime.timedelta(seconds=SCREAMING_SNAKE_CASE      )
	for item in chunk_bytes_iter(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,stride=(stride_left, stride_right)	,stream=SCREAMING_SNAKE_CASE      ):
		# Put everything back in numpy scale
		UpperCAmelCase__:	List[str]									= np.frombuffer(item["raw"]	,dtype=SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	List[str]									= (
		    item["stride"][0] // size_of_sample,
		    item["stride"][1] // size_of_sample,
		)
		UpperCAmelCase__:	Dict									= sampling_rate
		audio_time += delta
		if datetime.datetime.now() > audio_time + 1_0 * delta:
			# We're late !! SKIP
			continue
		yield item



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



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[Any]									= 2**2_4  # 16Mo
	try:
		with subprocess.Popen(SCREAMING_SNAKE_CASE	,stdout=subprocess.PIPE	,bufsize=SCREAMING_SNAKE_CASE      ) as ffmpeg_process:
			while True:
				UpperCAmelCase__:	List[Any]									= ffmpeg_process.stdout.read(SCREAMING_SNAKE_CASE      )
				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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase  :      Union[str, Any]			 ={
    """configuration_clap""": [
        """CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ClapAudioConfig""",
        """ClapConfig""",
        """ClapTextConfig""",
    ],
    """processing_clap""": ["""ClapProcessor"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Union[str, Any]			 =[
					    """CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ClapModel""",
					    """ClapPreTrainedModel""",
					    """ClapTextModel""",
					    """ClapTextModelWithProjection""",
					    """ClapAudioModel""",
					    """ClapAudioModelWithProjection""",
					]
					_lowerCAmelCase  :      List[str]			 =["""ClapFeatureExtractor"""]

if TYPE_CHECKING:
					from .configuration_clap import (
					    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
					    ClapAudioConfig,
					    ClapConfig,
					    ClapTextConfig,
					)
					from .processing_clap import ClapProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clap import ClapFeatureExtractor
										from .modeling_clap import (
										    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ClapAudioModel,
										    ClapAudioModelWithProjection,
										    ClapModel,
										    ClapPreTrainedModel,
										    ClapTextModel,
										    ClapTextModelWithProjection,
										)


else:
					import sys

					_lowerCAmelCase  :      Optional[int]			 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)