


from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class    UpperCAmelCase					(       __snake_case    ):




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		float						):







						"""simple docstring"""







						return 0.0






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		np.ndarray       ,  SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			UpperCamelCase         =					min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
			UpperCamelCase         =					max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
			return lowest, highest






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		FilterType       ,  SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			UpperCamelCase         =					512
			UpperCamelCase         =					[1] + [0] * (size - 1)
			UpperCamelCase         =					[filter_type.process(SCREAMING_SNAKE_CASE_	) for item in inputs]

			UpperCamelCase         =					[0] * (samplerate - size)  # zero-padding
			outputs += filler
			UpperCamelCase         =					np.abs(np.fft.fft(SCREAMING_SNAKE_CASE_	)	)
			UpperCamelCase         =					20 * np.logaa(SCREAMING_SNAKE_CASE_	)

			# Frequencies on log scale from 24 to nyquist frequency
			plt.xlim(24       ,  samplerate / 2 - 1	)
			plt.xlabel("""Frequency (Hz)"""	)
			plt.xscale("""log"""	)

			# Display within reasonable bounds
			UpperCamelCase         =					get_bounds(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)
			plt.ylim(max([-80, bounds[0]]	)       ,  min([80, bounds[1]]	)	)
			plt.ylabel("""Gain (dB)"""	)

			plt.plot(SCREAMING_SNAKE_CASE_	)
			plt.show()






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		FilterType       ,  SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			UpperCamelCase         =					512
			UpperCamelCase         =					[1] + [0] * (size - 1)
			UpperCamelCase         =					[filter_type.process(SCREAMING_SNAKE_CASE_	) for item in inputs]

			UpperCamelCase         =					[0] * (samplerate - size)  # zero-padding
			outputs += filler
			UpperCamelCase         =					np.angle(np.fft.fft(SCREAMING_SNAKE_CASE_	)	)

			# Frequencies on log scale from 24 to nyquist frequency
			plt.xlim(24       ,  samplerate / 2 - 1	)
			plt.xlabel("""Frequency (Hz)"""	)
			plt.xscale("""log"""	)

			plt.ylim(-2 * pi       ,  2 * pi	)
			plt.ylabel("""Phase shift (Radians)"""	)
			plt.plot(np.unwrap(SCREAMING_SNAKE_CASE_       ,  -2 * pi	)	)
			plt.show()








import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available






def 							_lowercase    (		*SCREAMING_SNAKE_CASE_   :		List[Any]	):





			"""simple docstring"""




			if not isinstance(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	):
						UpperCamelCase         =					list(SCREAMING_SNAKE_CASE_	)
			for i in range(len(SCREAMING_SNAKE_CASE_	)	):
						UpperCamelCase         =					None
			gc.collect()
			if is_xpu_available():
						torch.xpu.empty_cache()
			elif is_npu_available():
						torch.npu.empty_cache()
			else:
						torch.cuda.empty_cache()
			return objects






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Exception	):





			"""simple docstring"""




			UpperCamelCase         =					[
			    """CUDA out of memory.""",  # CUDA OOM
			    """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
			    """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
			]
			if isinstance(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	) and len(exception.args	) == 1:
						return any(err in exception.args[0] for err in _statements	)
			return False






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		callable = None       ,  SCREAMING_SNAKE_CASE_   :		int = 128	):





			"""simple docstring"""




			if function is None:
						return functools.partial(SCREAMING_SNAKE_CASE_       ,  starting_batch_size=SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					starting_batch_size

			def decorator(*SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  **SCREAMING_SNAKE_CASE_   :		Tuple	):
						nonlocal batch_size
						gc.collect()
						if is_xpu_available():
									torch.xpu.empty_cache()
						elif is_npu_available():
									torch.npu.empty_cache()
						else:
									torch.cuda.empty_cache()
						UpperCamelCase         =					list(inspect.signature(SCREAMING_SNAKE_CASE_	).parameters.keys()	)
						# Guard against user error
						if len(SCREAMING_SNAKE_CASE_	) < (len(SCREAMING_SNAKE_CASE_	) + 1):
									UpperCamelCase         =					""", """.join([f'{arg}={value}' for arg, value in zip(params[1:]       ,  args[1:]	)]	)
									raise TypeError(
									    f'Batch size was passed into `{function.__name__}` as the first argument when called.'
									    f'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'	)
						while True:
									if batch_size == 0:
												raise RuntimeError("""No executable batch size found, reached zero."""	)
									try:
												return function(SCREAMING_SNAKE_CASE_       ,  *SCREAMING_SNAKE_CASE_       ,  **SCREAMING_SNAKE_CASE_	)
									except Exception as e:
												if should_reduce_batch_size(SCREAMING_SNAKE_CASE_	):
															gc.collect()
															if is_xpu_available():
																		torch.xpu.empty_cache()
															elif is_npu_available():
																		torch.npu.empty_cache()
															else:
																		torch.cuda.empty_cache()
															batch_size //= 2
												else:
															raise

			return decorator




