





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

import numpy as np




def     A_      (	lowercase_   ,		lowercase_	)      ->    Any:
       _snake_case   :				Optional[int]  					=    f'''{sampling_rate}'''
       _snake_case   :				Optional[Any]  					=    "1"
       _snake_case   :				List[str]  					=    "f32le"
       _snake_case   :				List[str]  					=    [
           "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:
                     _snake_case   :				Dict  					=    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
       _snake_case   :				Dict  					=    output_stream[0]
       _snake_case   :				int  					=    np.frombuffer(lowercase_   ,		np.floataa	)
       if audio.shape[0] == 0:
              raise ValueError('''Malformed soundfile'''	)
       return audio




def     A_      (	lowercase_   ,		lowercase_   ,		lowercase_ = "f32le"   ,		)      ->    Any:
       _snake_case   :				str  					=    f'''{sampling_rate}'''
       _snake_case   :				List[str]  					=    "1"
       if format_for_conversion == "s16le":
              _snake_case   :				List[Any]  					=    2
       elif format_for_conversion == "f32le":
              _snake_case   :				int  					=    4
       else:
              raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''	)

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

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




def     A_      (	lowercase_   ,		lowercase_   ,		lowercase_ = None   ,		lowercase_ = None   ,		lowercase_ = "f32le"   ,		)      ->    Optional[Any]:
       if stream_chunk_s is not None:
              _snake_case   :				str  					=    stream_chunk_s
       else:
              _snake_case   :				Optional[int]  					=    chunk_length_s

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

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

       _snake_case   :				Tuple  					=    int(round(sampling_rate * stride_length_s[0]	)	) * size_of_sample
       _snake_case   :				List[Any]  					=    int(round(sampling_rate * stride_length_s[1]	)	) * size_of_sample
       _snake_case   :				Optional[Any]  					=    datetime.datetime.now()
       _snake_case   :				int  					=    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
              _snake_case   :				List[str]  					=    np.frombuffer(item['''raw''']   ,		dtype=lowercase_	)
              _snake_case   :				List[str]  					=    (
                  item["stride"][0] // size_of_sample,
                  item["stride"][1] // size_of_sample,
              )
              _snake_case   :				Dict  					=    sampling_rate
              audio_time += delta
              if datetime.datetime.now() > audio_time + 10 * delta:
                     # We're late !! SKIP
                     continue
              yield item




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




def     A_      (	lowercase_   ,		lowercase_	)      ->    Dict:
       _snake_case   :				List[Any]  					=    2**24  # 16Mo
       try:
              with subprocess.Popen(lowercase_   ,		stdout=subprocess.PIPE   ,		bufsize=lowercase_	) as ffmpeg_process:
                     while True:
                            _snake_case   :				List[Any]  					=    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









import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


_lowerCAmelCase  :      int			 ="""scheduler_config.json"""

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   1
					__magic_name__        =   2
					__magic_name__        =   3
					__magic_name__        =   4
					__magic_name__        =   5
					__magic_name__        =   6
					__magic_name__        =   7
					__magic_name__        =   8
					__magic_name__        =   9
					__magic_name__        =   1_0
					__magic_name__        =   1_1
					__magic_name__        =   1_2
					__magic_name__        =   1_3
					__magic_name__        =   1_4

@dataclass
class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   42

class __UpperCamelCase     :






					'''simple docstring'''



					__magic_name__        =   SCHEDULER_CONFIG_NAME
					__magic_name__        =   []
					__magic_name__        =   True





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__=False				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= cls.load_config(
						    pretrained_model_name_or_path=lowerCamelCase__				,      subfolder=lowerCamelCase__				,      return_unused_kwargs=lowerCamelCase__				,      return_commit_hash=lowerCamelCase__				,      **lowerCamelCase__				,      )
						return cls.from_config(lowerCamelCase__				,      return_unused_kwargs=lowerCamelCase__				,      **lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = False				,      **lowerCamelCase__						):
						self.save_config(save_directory=lowerCamelCase__				,      push_to_hub=lowerCamelCase__				,      **lowerCamelCase__						)





					@property
					def    _UpperCAmelCase    (				self						):
						return self._get_compatibles()





					@classmethod
					def    _UpperCAmelCase    (				cls						):
						UpperCAmelCase__:	List[str]									= list(set([cls.__name__] + cls._compatibles						)						)
						UpperCAmelCase__:	Union[str, Any]									= importlib.import_module(__name__.split("."						)[0]						)
						UpperCAmelCase__:	int									= [
						    getattr(lowerCamelCase__				,      lowerCamelCase__						) for c in compatible_classes_str if hasattr(lowerCamelCase__				,      lowerCamelCase__						)
						]
						return compatible_classes