


from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter
def        UpperCamelCase_(				_A   :List[Any]				,     _A   :Union[str, Any]				,     _A   :Union[str, Any] = 1 / sqrt(2  )  )->		List[Any]:
			UpperCamelCase__					=       tau * frequency / samplerate
			UpperCamelCase__					=       sin(a__  )
			UpperCamelCase__					=       cos(a__  )
			UpperCamelCase__					=       _sin / (2 * q_factor)

			UpperCamelCase__					=       (1 - _cos) / 2
			UpperCamelCase__					=       1 - _cos

			UpperCamelCase__					=       1 + alpha
			UpperCamelCase__					=       -2 * _cos
			UpperCamelCase__					=       1 - alpha

			UpperCamelCase__					=       IIRFilter(2  )
			filt.set_coefficients([aa, aa, aa]				,     [ba, ba, ba]  )
			return filt
def        UpperCamelCase_(				_A   :Tuple				,     _A   :Tuple				,     _A   :Dict = 1 / sqrt(2  )  )->		Dict:
			UpperCamelCase__					=       tau * frequency / samplerate
			UpperCamelCase__					=       sin(a__  )
			UpperCamelCase__					=       cos(a__  )
			UpperCamelCase__					=       _sin / (2 * q_factor)

			UpperCamelCase__					=       (1 + _cos) / 2
			UpperCamelCase__					=       -1 - _cos

			UpperCamelCase__					=       1 + alpha
			UpperCamelCase__					=       -2 * _cos
			UpperCamelCase__					=       1 - alpha

			UpperCamelCase__					=       IIRFilter(2  )
			filt.set_coefficients([aa, aa, aa]				,     [ba, ba, ba]  )
			return filt
def        UpperCamelCase_(				_A   :Optional[Any]				,     _A   :Tuple				,     _A   :Optional[Any] = 1 / sqrt(2  )  )->		Union[str, Any]:
			UpperCamelCase__					=       tau * frequency / samplerate
			UpperCamelCase__					=       sin(a__  )
			UpperCamelCase__					=       cos(a__  )
			UpperCamelCase__					=       _sin / (2 * q_factor)

			UpperCamelCase__					=       _sin / 2
			UpperCamelCase__					=       0
			UpperCamelCase__					=       -ba

			UpperCamelCase__					=       1 + alpha
			UpperCamelCase__					=       -2 * _cos
			UpperCamelCase__					=       1 - alpha

			UpperCamelCase__					=       IIRFilter(2  )
			filt.set_coefficients([aa, aa, aa]				,     [ba, ba, ba]  )
			return filt
def        UpperCamelCase_(				_A   :int				,     _A   :Tuple				,     _A   :int = 1 / sqrt(2  )  )->		Dict:
			UpperCamelCase__					=       tau * frequency / samplerate
			UpperCamelCase__					=       sin(a__  )
			UpperCamelCase__					=       cos(a__  )
			UpperCamelCase__					=       _sin / (2 * q_factor)

			UpperCamelCase__					=       1 - alpha
			UpperCamelCase__					=       -2 * _cos
			UpperCamelCase__					=       1 + alpha

			UpperCamelCase__					=       IIRFilter(2  )
			filt.set_coefficients([ba, ba, ba]				,     [ba, ba, ba]  )
			return filt
def        UpperCamelCase_(				_A   :Optional[Any]				,     _A   :Dict				,     _A   :Optional[Any]				,     _A   :Any = 1 / sqrt(2  )				,     )->		Tuple:
			UpperCamelCase__					=       tau * frequency / samplerate
			UpperCamelCase__					=       sin(a__  )
			UpperCamelCase__					=       cos(a__  )
			UpperCamelCase__					=       _sin / (2 * q_factor)
			UpperCamelCase__					=       10 ** (gain_db / 40)

			UpperCamelCase__					=       1 + alpha * big_a
			UpperCamelCase__					=       -2 * _cos
			UpperCamelCase__					=       1 - alpha * big_a
			UpperCamelCase__					=       1 + alpha / big_a
			UpperCamelCase__					=       -2 * _cos
			UpperCamelCase__					=       1 - alpha / big_a

			UpperCamelCase__					=       IIRFilter(2  )
			filt.set_coefficients([aa, aa, aa]				,     [ba, ba, ba]  )
			return filt
def        UpperCamelCase_(				_A   :Any				,     _A   :Optional[int]				,     _A   :Tuple				,     _A   :str = 1 / sqrt(2  )				,     )->		Optional[Any]:
			UpperCamelCase__					=       tau * frequency / samplerate
			UpperCamelCase__					=       sin(a__  )
			UpperCamelCase__					=       cos(a__  )
			UpperCamelCase__					=       _sin / (2 * q_factor)
			UpperCamelCase__					=       10 ** (gain_db / 40)
			UpperCamelCase__					=       (big_a + 1) - (big_a - 1) * _cos
			UpperCamelCase__					=       (big_a + 1) + (big_a - 1) * _cos
			UpperCamelCase__					=       (big_a - 1) - (big_a + 1) * _cos
			UpperCamelCase__					=       (big_a - 1) + (big_a + 1) * _cos
			UpperCamelCase__					=       2 * sqrt(a__  ) * alpha

			UpperCamelCase__					=       big_a * (pmc + aaa)
			UpperCamelCase__					=       2 * big_a * mpc
			UpperCamelCase__					=       big_a * (pmc - aaa)
			UpperCamelCase__					=       ppmc + aaa
			UpperCamelCase__					=       -2 * pmpc
			UpperCamelCase__					=       ppmc - aaa

			UpperCamelCase__					=       IIRFilter(2  )
			filt.set_coefficients([aa, aa, aa]				,     [ba, ba, ba]  )
			return filt







def        UpperCamelCase_(				_A   :Union[str, Any]				,     _A   :Dict				,     _A   :Optional[Any]				,     _A   :Union[str, Any] = 1 / sqrt(2  )				,     )->		List[str]:
			UpperCamelCase__					=       tau * frequency / samplerate
			UpperCamelCase__					=       sin(a__  )
			UpperCamelCase__					=       cos(a__  )
			UpperCamelCase__					=       _sin / (2 * q_factor)
			UpperCamelCase__					=       10 ** (gain_db / 40)
			UpperCamelCase__					=       (big_a + 1) - (big_a - 1) * _cos
			UpperCamelCase__					=       (big_a + 1) + (big_a - 1) * _cos
			UpperCamelCase__					=       (big_a - 1) - (big_a + 1) * _cos
			UpperCamelCase__					=       (big_a - 1) + (big_a + 1) * _cos
			UpperCamelCase__					=       2 * sqrt(a__  ) * alpha

			UpperCamelCase__					=       big_a * (ppmc + aaa)
			UpperCamelCase__					=       -2 * big_a * pmpc
			UpperCamelCase__					=       big_a * (ppmc - aaa)
			UpperCamelCase__					=       pmc + aaa
			UpperCamelCase__					=       2 * mpc
			UpperCamelCase__					=       pmc - aaa

			UpperCamelCase__					=       IIRFilter(2  )
			filt.set_coefficients([aa, aa, aa]				,     [ba, ba, ba]  )
			return filt






import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__UpperCamelCase    		=       logging.get_logger(__name__)
def        UpperCamelCase_(				)->		Any:
			# Get the sagemaker specific mp parameters from smp_options variable.
			UpperCamelCase__					=       os.getenv("SM_HP_MP_PARAMETERS"				,     "{}"  )
			try:
						# Parse it and check the field "partitions" is included, it is required for model parallel.
						UpperCamelCase__					=       json.loads(_A  )
						if "partitions" not in smp_options:
									return False
			except json.JSONDecodeError:
						return False

			# Get the sagemaker specific framework parameters from mpi_options variable.
			UpperCamelCase__					=       os.getenv("SM_FRAMEWORK_PARAMS"				,     "{}"  )
			try:
						# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
						UpperCamelCase__					=       json.loads(_A  )
						if not mpi_options.get("sagemaker_mpi_enabled"				,     _A  ):
									return False
			except json.JSONDecodeError:
						return False
			# Lastly, check if the `smdistributed` module is present.
			return importlib.util.find_spec("smdistributed"  ) is not None


if is_sagemaker_model_parallel_available():
						import smdistributed.modelparallel.torch as smp

						smp.init()







@dataclass
class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					str    =		field(
			    default=''					,	metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}					,	)




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						super().__post_init__()
						warnings.warn(
						    "`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
						    "`TrainingArguments` instead."      ,						snake_case      ,						)




			@cached_property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						logger.info("PyTorch: setting up devices"     )
						if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
									logger.warning(
									    "torch.distributed process group is initialized, but local_rank == -1. "
									    "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"     )
						if self.no_cuda:
									UpperCamelCase__					=       torch.device("cpu"     )
									UpperCamelCase__					=       0
						elif is_sagemaker_model_parallel_available():
									UpperCamelCase__					=       smp.local_rank()
									UpperCamelCase__					=       torch.device("cuda"      ,						snake_case     )
									UpperCamelCase__					=       1
						elif is_sagemaker_dp_enabled():
									import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

									torch.distributed.init_process_group(backend="smddp"      ,						timeout=self.ddp_timeout_delta     )
									UpperCamelCase__					=       int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"     )     )
									UpperCamelCase__					=       torch.device("cuda"      ,						self.local_rank     )
									UpperCamelCase__					=       1
						elif self.local_rank == -1:
									# if n_gpu is > 1 we'll use nn.DataParallel.
									# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
									# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
									# trigger an error that a device index is missing. Index 0 takes into account the
									# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
									# will use the first GPU in that env, i.e. GPU#1
									UpperCamelCase__					=       torch.device("cuda:0" if torch.cuda.is_available() else "cpu"     )
									# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
									# the default value.
									UpperCamelCase__					=       torch.cuda.device_count()
						else:
									# Here, we'll use torch.distributed.
									# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
									if not torch.distributed.is_initialized():
												torch.distributed.init_process_group(backend="nccl"      ,						timeout=self.ddp_timeout_delta     )
									UpperCamelCase__					=       torch.device("cuda"      ,						self.local_rank     )
									UpperCamelCase__					=       1

						if device.type == "cuda":
									torch.cuda.set_device(snake_case     )

						return device




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						if is_sagemaker_model_parallel_available():
									return smp.dp_size()

						return super().world_size




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return not is_sagemaker_model_parallel_available()







			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return False


