




"""simple docstring"""






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
			import tensorflow as tf


lowercase__     						=						logging.get_logger(__name__)






@dataclass
class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			[
			    """no_inference""",
			    """no_cuda""",
			    """no_tpu""",
			    """no_speed""",
			    """no_memory""",
			    """no_env_print""",
			    """no_multi_process""",
			]
			def __init__(  self  ,      **lowercase  ):
							for deprecated_arg in self.deprecated_args:
											if deprecated_arg in kwargs:
															_lowerCamelCase     :			Any    =    deprecated_arg[3:]
															_lowerCamelCase     :			int    =    not kwargs.pop(lowercase  )
															logger.warning(
															    F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
															    F''' {positive_arg}={kwargs[positive_arg]}'''  )
							_lowerCamelCase     :			Any    =    kwargs.pop('tpu_name'  ,      self.tpu_name  )
							_lowerCamelCase     :			Dict    =    kwargs.pop('device_idx'  ,      self.device_idx  )
							_lowerCamelCase     :			int    =    kwargs.pop('eager_mode'  ,      self.eager_mode  )
							_lowerCamelCase     :			List[str]    =    kwargs.pop('use_xla'  ,      self.use_xla  )
							super().__init__(**lowercase  )

			lowerCamelCase__      =			field(
			    default=lowercase,	metadata={"""help""": """Name of TPU"""},	)
			lowerCamelCase__      =			field(
			    default=0,	metadata={"""help""": """CPU / GPU device index. Defaults to 0."""},	)
			lowerCamelCase__      =			field(default=lowercase,	metadata={"""help""": """Benchmark models in eager model."""}						)
			lowerCamelCase__      =			field(
			    default=lowercase,	metadata={
			        """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
			    },	)
			@cached_property
			def 					A_    (  self  ):
							requires_backends(self  ,      ['tf']  )
							_lowerCamelCase     :			int    =    None
							if self.tpu:
											try:
															if self.tpu_name:
																			_lowerCamelCase     :			List[Any]    =    tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name  )
															else:
																			_lowerCamelCase     :			str    =    tf.distribute.cluster_resolver.TPUClusterResolver()
											except ValueError:
															_lowerCamelCase     :			List[str]    =    None
							return tpu
			@cached_property
			def 					A_    (  self  ):
							requires_backends(self  ,      ['tf']  )
							if self.is_tpu:
											tf.config.experimental_connect_to_cluster(self._setup_tpu  )
											tf.tpu.experimental.initialize_tpu_system(self._setup_tpu  )

											_lowerCamelCase     :			Any    =    tf.distribute.TPUStrategy(self._setup_tpu  )
							else:
											# currently no multi gpu is allowed
											if self.is_gpu:
															# TODO: Currently only single GPU is supported
															tf.config.set_visible_devices(self.gpu_list[self.device_idx]  ,      'GPU'  )
															_lowerCamelCase     :			Dict    =    tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''  )
											else:
															tf.config.set_visible_devices([]  ,      'GPU'  )  # disable GPU
															_lowerCamelCase     :			int    =    tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''  )

							return strategy
			@property
			def 					A_    (  self  ):
							requires_backends(self  ,      ['tf']  )
							return self._setup_tpu is not None
			@property
			def 					A_    (  self  ):
							requires_backends(self  ,      ['tf']  )
							return self._setup_strategy
			@property
			def 					A_    (  self  ):
							requires_backends(self  ,      ['tf']  )
							return tf.config.list_physical_devices('GPU'  )
			@property
			def 					A_    (  self  ):
							requires_backends(self  ,      ['tf']  )
							if self.cuda:
											return len(self.gpu_list  )
							return 0






			@property
			def 					A_    (  self  ):
							return self.n_gpu > 0





"""simple docstring"""






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

import numpy as np







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Tuple    =    f'''{sampling_rate}'''
				_lowerCamelCase     :			str    =    '1'
				_lowerCamelCase     :			str    =    'f32le'
				_lowerCamelCase     :			Union[str, Any]    =    [
				    '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:
												_lowerCamelCase     :			str    =    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
				_lowerCamelCase     :			List[Any]    =    output_stream[0]
				_lowerCamelCase     :			Tuple    =    np.frombuffer(lowercase__							,    np.floataa		)
				if audio.shape[0] == 0:
								raise ValueError('Malformed soundfile'		)
				return audio







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

				_lowerCamelCase     :			Dict    =    platform.system()
				if system == "Linux":
								_lowerCamelCase     :			Optional[int]    =    'alsa'
								_lowerCamelCase     :			Optional[Any]    =    'default'
				elif system == "Darwin":
								_lowerCamelCase     :			Optional[int]    =    'avfoundation'
								_lowerCamelCase     :			Any    =    ':0'
				elif system == "Windows":
								_lowerCamelCase     :			Tuple    =    'dshow'
								_lowerCamelCase     :			Tuple    =    'default'

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







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = "f32le"							,    ):
				if stream_chunk_s is not None:
								_lowerCamelCase     :			int    =    stream_chunk_s
				else:
								_lowerCamelCase     :			Optional[Any]    =    chunk_length_s

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

				if stride_length_s is None:
								_lowerCamelCase     :			Union[str, Any]    =    chunk_length_s / 6
				_lowerCamelCase     :			Optional[int]    =    int(round(sampling_rate * chunk_length_s		)		) * size_of_sample
				if isinstance(lowercase__							,    (int, float)		):
								_lowerCamelCase     :			Any    =    [stride_length_s, stride_length_s]

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







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







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			int    =    2**24  # 16Mo
				try:
								with subprocess.Popen(lowercase__							,    stdout=subprocess.PIPE							,    bufsize=lowercase__		) as ffmpeg_process:
												while True:
																_lowerCamelCase     :			Optional[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