




'''simple docstring'''





import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand







def 		_A(       UpperCamelCase__							:			Optional[Any]				)    -> Dict:
							'''simple docstring'''


							return EnvironmentCommand()







def 		_A(       UpperCamelCase__							:			int				)    -> Union[str, Any]:
							'''simple docstring'''


							return EnvironmentCommand(args.accelerate_config_file				)







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						@staticmethod
						def     UpperCAmelCase_      ( lowerCamelCase__ :     ArgumentParser				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    parser.add_parser('''env'''				)
													download_parser.set_defaults(func=lowerCamelCase__				)
													download_parser.add_argument(
													    '''--accelerate-config_file'''				,							default=lowerCamelCase__				,							help='''The accelerate config file to use for the default values in the launching script.'''				,							)
													download_parser.set_defaults(func=lowerCamelCase__				)






						def __init__( self :     Any				,							lowerCamelCase__ :     Tuple				,							*lowerCamelCase__ :     Optional[Any]				)    ->						None:
													"""simple docstring"""


													__lowercase      =    accelerate_config_file






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    '''not installed'''
													if is_safetensors_available():
																				import safetensors

																				__lowercase      =    safetensors.__version__
													elif importlib.util.find_spec('''safetensors'''				) is not None:
																				import safetensors

																				__lowercase      =    f'{safetensors.__version__} but is ignored because of PyTorch version too old.'

													__lowercase      =    '''not installed'''
													__lowercase      =    __lowercase      =    '''not found'''
													if is_accelerate_available():
																				import accelerate
																				from accelerate.commands.config import default_config_file, load_config_from_file

																				__lowercase      =    accelerate.__version__
																				# Get the default from the config file.
																				if self._accelerate_config_file is not None or os.path.isfile(lowerCamelCase__				):
																											__lowercase      =    load_config_from_file(self._accelerate_config_file				).to_dict()

																				__lowercase      =    (
																				    '''\n'''.join([f'\t- {prop}: {val}' for prop, val in accelerate_config.items()]				)
																				    if isinstance(lowerCamelCase__				,							lowerCamelCase__				)
																				    else f'\t{accelerate_config}'
																				)

													__lowercase      =    '''not installed'''
													__lowercase      =    '''NA'''
													if is_torch_available():
																				import torch

																				__lowercase      =    torch.__version__
																				__lowercase      =    torch.cuda.is_available()

													__lowercase      =    '''not installed'''
													__lowercase      =    '''NA'''
													if is_tf_available():
																				import tensorflow as tf

																				__lowercase      =    tf.__version__
																				try:
																											# deprecated in v2.1
																											__lowercase      =    tf.test.is_gpu_available()
																				except AttributeError:
																											# returns list of devices, convert to bool
																											__lowercase      =    bool(tf.config.list_physical_devices('''GPU'''				)				)

													__lowercase      =    '''not installed'''
													__lowercase      =    '''not installed'''
													__lowercase      =    '''not installed'''
													__lowercase      =    '''NA'''
													if is_flax_available():
																				import flax
																				import jax
																				import jaxlib

																				__lowercase      =    flax.__version__
																				__lowercase      =    jax.__version__
																				__lowercase      =    jaxlib.__version__
																				__lowercase      =    jax.lib.xla_bridge.get_backend().platform

													__lowercase      =    {
													    '''`transformers` version''': version,
													    '''Platform''': platform.platform(),
													    '''Python version''': platform.python_version(),
													    '''Huggingface_hub version''': huggingface_hub.__version__,
													    '''Safetensors version''': f'{safetensors_version}',
													    '''Accelerate version''': f'{accelerate_version}',
													    '''Accelerate config''': f'{accelerate_config_str}',
													    '''PyTorch version (GPU?)''': f'{pt_version} ({pt_cuda_available})',
													    '''Tensorflow version (GPU?)''': f'{tf_version} ({tf_cuda_available})',
													    '''Flax version (CPU?/GPU?/TPU?)''': f'{flax_version} ({jax_backend})',
													    '''Jax version''': f'{jax_version}',
													    '''JaxLib version''': f'{jaxlib_version}',
													    '''Using GPU in script?''': '''<fill in>''',
													    '''Using distributed or parallel set-up in script?''': '''<fill in>''',
													}

													print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''				)
													print(self.format_dict(lowerCamelCase__				)				)

													return info






						@staticmethod
						def     UpperCAmelCase_      ( lowerCamelCase__ :     str				)    ->						Union[str, Any]:
													"""simple docstring"""


													return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]				) + "\n"












from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     float				)    ->						float:
													"""simple docstring"""


													return 0.0







def 		_A(       UpperCamelCase__							:			np.ndarray				,       UpperCamelCase__							:			int				)    -> tuple[int | float, int | float]:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			FilterType				,       UpperCamelCase__							:			int				)    -> None:
							'''simple docstring'''


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

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

							# 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
							__lowercase      =    get_bounds(UpperCamelCase__				,       UpperCamelCase__				)
							plt.ylim(max([-80, bounds[0]]				)				,       min([80, bounds[1]]				)				)
							plt.ylabel('''Gain (dB)'''				)

							plt.plot(UpperCamelCase__				)
							plt.show()







def 		_A(       UpperCamelCase__							:			FilterType				,       UpperCamelCase__							:			int				)    -> None:
							'''simple docstring'''


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

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

							# 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(UpperCamelCase__				,       -2 * pi				)				)
							plt.show()






