
"""simple docstring"""





import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler






class        _UpperCAmelCase:



		def __init__(   self							,						__a)			->			Optional[int]:






							'''simple docstring'''
							if isinstance(__a							,						__a):
												# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
												# modified it, it will not be accepted here again, since `auto` values would have been overridden
												_UpperCamelCase									=    deepcopy(__a)
							elif os.path.exists(__a):
												with io.open(__a							,						'''r'''							,						encoding='''utf-8''') as f:
																	_UpperCamelCase									=    json.load(__a)
							else:
												try:
																	_UpperCamelCase									=    baseaa.urlsafe_baadecode(__a).decode('''utf-8''')
																	_UpperCamelCase									=    json.loads(__a)
												except (UnicodeDecodeError, AttributeError, ValueError):
																	raise ValueError(
																	    F'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}''')

							_UpperCamelCase									=    config

							self.set_stage_and_offload()



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							# zero stage - this is done as early as possible, before model is created, to allow
							# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
							# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
							_UpperCamelCase									=    self.get_value('''zero_optimization.stage'''							,						-1)

							# offload
							_UpperCamelCase									=    False
							if self.is_zeroa() or self.is_zeroa():
												_UpperCamelCase									=    set(['''cpu''', '''nvme'''])
												_UpperCamelCase									=    set(
												    [
												        self.get_value('''zero_optimization.offload_optimizer.device'''),
												        self.get_value('''zero_optimization.offload_param.device'''),
												    ])
												if len(offload_devices & offload_devices_valid) > 0:
																	_UpperCamelCase									=    True



		def 		UpperCAmelCase				(   self							,						__a)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.config

							# find the config node of interest if it exists
							_UpperCamelCase									=    ds_key_long.split('''.''')
							_UpperCamelCase									=    nodes.pop()
							for node in nodes:
												_UpperCamelCase									=    config.get(__a)
												if config is None:
																	return None, ds_key

							return config, ds_key



		def 		UpperCAmelCase				(   self							,						__a							,						__a=None)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.find_config_node(__a)
							if config is None:
												return default
							return config.get(__a							,						__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a=False)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    self.config

							# find the config node of interest if it exists
							_UpperCamelCase									=    ds_key_long.split('''.''')
							for node in nodes:
												_UpperCamelCase									=    config
												_UpperCamelCase									=    config.get(__a)
												if config is None:
																	if must_exist:
																						raise ValueError(F'''Can\'t find {ds_key_long} entry in the config: {self.config}''')
																	else:
																						return

        # if found remove it
							if parent_config is not None:
												parent_config.pop(__a)



		def 		UpperCAmelCase				(   self							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_value(__a)
							return False if value is None else bool(__a)



		def 		UpperCAmelCase				(   self							,						__a)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_value(__a)
							return False if value is None else not bool(__a)



		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							return self._stage == 2



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							return self._stage == 3




		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							return self._offload






class        _UpperCAmelCase:



		def __init__(   self							,						__a)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    engine




		def 		UpperCAmelCase				(   self							,						__a							,						**__a)			->			List[str]:






							'''simple docstring'''
							# runs backpropagation and handles mixed precision
							self.engine.backward(__a							,						**__a)

							# Deepspeed's `engine.step` performs the following operations:
							# - gradient accumulation check
							# - gradient clipping
							# - optimizer step
							# - zero grad
							# - checking overflow
							# - lr_scheduler step (only if engine.lr_scheduler is not None)
							self.engine.step()
							# and this plugin overrides the above calls with no-ops when Accelerate runs under
							# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
							# training loop that works transparently under many training regimes.






class        _UpperCAmelCase(       lowerCamelCase      ):



		def __init__(   self							,						__a)			->			List[Any]:






							'''simple docstring'''
							super().__init__(__a							,						device_placement=__a							,						scaler=__a)
							_UpperCamelCase									=    hasattr(self.optimizer							,						'''overflow''')



		def 		UpperCAmelCase				(   self							,						__a=None)			->			Tuple:






							'''simple docstring'''
							pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




		@property
		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							if self.__has_overflow__:
												return self.optimizer.overflow
							return False






class        _UpperCAmelCase(       lowerCamelCase      ):



		def __init__(   self							,						__a							,						__a)			->			Dict:






							'''simple docstring'''
							super().__init__(__a							,						__a)




		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a=0.001							,						__a=0							,						**__a)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    params
							_UpperCamelCase									=    lr
							_UpperCamelCase									=    weight_decay
							_UpperCamelCase									=    kwargs






class        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a=None							,						__a=0							,						**__a)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    optimizer
							_UpperCamelCase									=    total_num_steps
							_UpperCamelCase									=    warmup_num_steps
							_UpperCamelCase									=    kwargs








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

import numpy as np


def 				__A(lowerCAmelCase    ,	lowerCAmelCase			)		->					np.array:

		"""simple docstring"""






		_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(lowerCAmelCase    ,	stdin=subprocess.PIPE    ,	stdout=subprocess.PIPE			) as ffmpeg_process:
						_UpperCamelCase         =						ffmpeg_process.communicate(lowerCAmelCase			)
		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(lowerCAmelCase    ,	np.floataa			)
		if audio.shape[0] == 0:
				raise ValueError("""Malformed soundfile"""			)
		return audio


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = "f32le"    ,	)		->					Union[str, Any]:

		"""simple docstring"""






		_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(lowerCAmelCase    ,	lowerCAmelCase			)
		for item in iterator:
				yield item


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = "f32le"    ,	)		->					Any:

		"""simple docstring"""






		if stream_chunk_s is not None:
				_UpperCamelCase         =						stream_chunk_s
		else:
				_UpperCamelCase         =						chunk_length_s

		_UpperCamelCase         =						ffmpeg_microphone(lowerCAmelCase    ,	lowerCAmelCase    ,	format_for_conversion=lowerCAmelCase			)
		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(lowerCAmelCase    ,	(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=lowerCAmelCase			)
		for item in chunk_bytes_iter(lowerCAmelCase    ,	lowerCAmelCase    ,	stride=(stride_left, stride_right)    ,	stream=lowerCAmelCase			):
				# Put everything back in numpy scale
				_UpperCamelCase         =						np.frombuffer(item["""raw"""]    ,	dtype=lowerCAmelCase			)
				_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 + 1_0 * delta:
						# We're late !! SKIP
						continue
				yield item


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = False			)		->					str:

		"""simple docstring"""






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







def 				__A(lowerCAmelCase    ,	lowerCAmelCase			)		->					Tuple:

		"""simple docstring"""






		_UpperCamelCase         =						2**2_4  # 16Mo
		try:
				with subprocess.Popen(lowerCAmelCase    ,	stdout=subprocess.PIPE    ,	bufsize=lowerCAmelCase			) as ffmpeg_process:
						while True:
								_UpperCamelCase         =						ffmpeg_process.stdout.read(lowerCAmelCase			)
								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



