






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

import numpy as np

def   lowerCAmelCase__						(     _a							:      str	,   _a							:      Tuple     ):
		snake_case_					:				List[Any]    			=							F'''{sampling_rate}'''
		snake_case_					:				Tuple    			=							"1"
		snake_case_					:				int    			=							"f32le"
		snake_case_					:				Tuple    			=							[
		    "ffmpeg",
		    "-i",
		    "pipe:0",
		    "-ac",
		    ac,
		    "-ar",
		    ar,
		    "-f",
		    format_for_conversion,
		    "-hide_banner",
		    "-loglevel",
		    "quiet",
		    "pipe:1",
		]

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

def   lowerCAmelCase__						(     _a							:      int	,   _a							:      List[str]	,   _a							:      Any = "f32le"	,   ):
		snake_case_					:				Dict    			=							F'''{sampling_rate}'''
		snake_case_					:				Optional[Any]    			=							"1"
		if format_for_conversion == "s16le":
				snake_case_					:				Optional[int]    			=							2
		elif format_for_conversion == "f32le":
				snake_case_					:				Any    			=							4
		else:
				raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''     )

		snake_case_					:				str    			=							platform.system()
		if system == "Linux":
				snake_case_					:				Dict    			=							"alsa"
				snake_case_					:				Any    			=							"default"
		elif system == "Darwin":
				snake_case_					:				Optional[int]    			=							"avfoundation"
				snake_case_					:				Optional[int]    			=							":0"
		elif system == "Windows":
				snake_case_					:				Optional[Any]    			=							"dshow"
				snake_case_					:				Optional[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_					:				Any    			=							int(round(sampling_rate * chunk_length_s     )     ) * size_of_sample
		snake_case_					:				str    			=							_ffmpeg_stream(_UpperCamelCase	,   _UpperCamelCase     )
		for item in iterator:
				yield item

def   lowerCAmelCase__						(     _a							:      str	,   _a							:      Optional[int]	,   _a							:      List[Any] = None	,   _a							:      Union[str, Any] = None	,   _a							:      str = "f32le"	,   ):




		if stream_chunk_s is not None:
				snake_case_					:				Optional[Any]    			=							stream_chunk_s
		else:
				snake_case_					:				str    			=							chunk_length_s

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

		if stride_length_s is None:
				snake_case_					:				Tuple    			=							chunk_length_s / 6
		snake_case_					:				Any    			=							int(round(sampling_rate * chunk_length_s     )     ) * size_of_sample
		if isinstance(_UpperCamelCase	,   (int, float)     ):
				snake_case_					:				Optional[int]    			=							[stride_length_s, stride_length_s]

		snake_case_					:				Any    			=							int(round(sampling_rate * stride_length_s[0]     )     ) * size_of_sample
		snake_case_					:				str    			=							int(round(sampling_rate * stride_length_s[1]     )     ) * size_of_sample
		snake_case_					:				int    			=							datetime.datetime.now()
		snake_case_					:				Optional[Any]    			=							datetime.timedelta(seconds=_UpperCamelCase     )
		for item in chunk_bytes_iter(_UpperCamelCase	,   _UpperCamelCase	,   stride=(stride_left, stride_right)	,   stream=_UpperCamelCase     ):
				# Put everything back in numpy scale
				snake_case_					:				Dict    			=							np.frombuffer(item["raw"]	,   dtype=_UpperCamelCase     )
				snake_case_					:				Any    			=							(
				    item["stride"][0] // size_of_sample,
				    item["stride"][1] // size_of_sample,
				)
				snake_case_					:				Any    			=							sampling_rate
				audio_time += delta
				if datetime.datetime.now() > audio_time + 10 * delta:
						# We're late !! SKIP
						continue
				yield item

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

def   lowerCAmelCase__						(     _a							:      int	,   _a							:      Optional[Any]     ):
		snake_case_					:				Dict    			=							2**24  # 16Mo
		try:
				with subprocess.Popen(_UpperCamelCase	,   stdout=subprocess.PIPE	,   bufsize=_UpperCamelCase     ) as ffmpeg_process:
						while True:
								snake_case_					:				Union[str, Any]    			=							ffmpeg_process.stdout.read(_UpperCamelCase     )
								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




"""simple docstring"""


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__						)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =field(default="""image-classification"""			,metadata={"""include_in_asdict_even_if_is_default""": True}						)
							__UpperCAmelCase		:       ClassVar[Features]							 =Features({"""image""": Image()}						)
							__UpperCAmelCase		:       ClassVar[Features]							 =Features({"""labels""": ClassLabel}						)
							__UpperCAmelCase		:       str							 ="image"
							__UpperCAmelCase		:       str							 ="labels"

							def      snake_case							(	self       ,  __a      ):
										if self.label_column not in features:
													raise ValueError(f"Column {self.label_column} is not present in features."      )
										if not isinstance(features[self.label_column]       ,  __a      ):
													raise ValueError(f"Column {self.label_column} is not a ClassLabel."      )
										__lowerCAmelCase							       =     copy.deepcopy(self      )
										__lowerCAmelCase							       =     self.label_schema.copy()
										__lowerCAmelCase							       =     features[self.label_column]
										__lowerCAmelCase							       =     label_schema
										return task_template



							@property
							def      snake_case							(	self      ):
										return {
										    self.image_column: "image",
										    self.label_column: "labels",
										}




