

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


__UpperCAmelCase    				=     'Run commands across TPU VMs for initial setup before running `accelerate launch`.'







def 	_snake_case		(    SCREAMING_SNAKE_CASE=None				)       ->      Dict:

     """simple docstring"""







     if subparsers is not None:
          _lowerCAmelCase   :					str        =    subparsers.add_parser("tpu-config"		,							description=_description				)
     else:
          _lowerCAmelCase   :					int        =    argparse.ArgumentParser("Accelerate tpu-config command"		,							description=_description				)
     # Core arguments
     _lowerCAmelCase   :					Tuple        =    parser.add_argument_group(
         "Config Arguments"		,							"Arguments that can be configured through `accelerate config`."				)
     config_args.add_argument(
         "--config_file"		,							type=SCREAMING_SNAKE_CASE		,							default=SCREAMING_SNAKE_CASE		,							help="Path to the config file to use for accelerate."		,							)
     config_args.add_argument(
         "--tpu_name"		,							default=SCREAMING_SNAKE_CASE		,							help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."		,							)
     config_args.add_argument(
         "--tpu_zone"		,							default=SCREAMING_SNAKE_CASE		,							help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."		,							)
     _lowerCAmelCase   :					Tuple        =    parser.add_argument_group("TPU Arguments"		,							"Arguments for options ran inside the TPU."				)
     pod_args.add_argument(
         "--use_alpha"		,							action="store_true"		,							help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."		,							)
     pod_args.add_argument(
         "--command_file"		,							default=SCREAMING_SNAKE_CASE		,							help="The path to the file containing the commands to run on the pod on startup."		,							)
     pod_args.add_argument(
         "--command"		,							action="append"		,							nargs="+"		,							help="A command to run on the pod. Can be passed multiple times."		,							)
     pod_args.add_argument(
         "--install_accelerate"		,							action="store_true"		,							help="Whether to install accelerate on the pod. Defaults to False."		,							)
     pod_args.add_argument(
         "--accelerate_version"		,							default="latest"		,							help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."		,							)
     pod_args.add_argument(
         "--debug"		,							action="store_true"		,							help="If set, will print the command that would be run instead of running it."				)

     if subparsers is not None:
          parser.set_defaults(func=SCREAMING_SNAKE_CASE				)
     return parser







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      List[str]:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    None

     # Get the default from the config file if it exists.
     if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					str        =    load_config_from_file(args.config_file				)
          if not args.command_file and defaults.command_file is not None and not args.command:
               _lowerCAmelCase   :					str        =    defaults.command_file
          if not args.command and defaults.commands is not None:
               _lowerCAmelCase   :					Any        =    defaults.commands
          if not args.tpu_name:
               _lowerCAmelCase   :					List[str]        =    defaults.tpu_name
          if not args.tpu_zone:
               _lowerCAmelCase   :					int        =    defaults.tpu_zone
     if args.accelerate_version == "dev":
          _lowerCAmelCase   :					Optional[Any]        =    "git+https://github.com/huggingface/accelerate.git"
     elif args.accelerate_version == "latest":
          _lowerCAmelCase   :					List[Any]        =    "accelerate -U"
     elif isinstance(parse(args.accelerate_version				)		,							SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					str        =    f'''accelerate=={args.accelerate_version}'''

     if not args.command_file and not args.command:
          raise ValueError("You must specify either a command file or a command to run on the pod."				)

     if args.command_file:
          with open(args.command_file		,							"r"				) as f:
               _lowerCAmelCase   :					Union[str, Any]        =    [f.read().splitlines()]

    # To turn list of lists into list of strings
     if isinstance(args.command[0]		,							SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Union[str, Any]        =    [line for cmd in args.command for line in cmd]
     # Default to the shared folder and install accelerate
     _lowerCAmelCase   :					List[str]        =    ["cd /usr/share"]
     if args.install_accelerate:
          new_cmd += [f'''pip install {args.accelerate_version}''']
     new_cmd += args.command
     _lowerCAmelCase   :					int        =    "; ".join(SCREAMING_SNAKE_CASE				)

     # Then send it to gcloud
     # Eventually try to use google-api-core to do this instead of subprocess
     _lowerCAmelCase   :					Optional[int]        =    ["gcloud"]
     if args.use_alpha:
          cmd += ["alpha"]
     cmd += [
         "compute",
         "tpus",
         "tpu-vm",
         "ssh",
         args.tpu_name,
         "--zone",
         args.tpu_zone,
         "--command",
         args.command,
         "--worker",
         "all",
     ]
     if args.debug:
          print(f'''Running {' '.join(SCREAMING_SNAKE_CASE				)}'''				)
          return
     subprocess.run(SCREAMING_SNAKE_CASE				)
     print("Successfully setup pod."				)







def 	_snake_case		(    )       ->      Optional[int]:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    tpu_command_parser()
     _lowerCAmelCase   :					int        =    parser.parse_args()

     tpu_command_launcher(SCREAMING_SNAKE_CASE				)









from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np





class A__    ( A ):



 """simple docstring"""







 def    __magic_name__       (		self :	List[Any]     ,						A_ :	float       ):






      '''simple docstring'''




      return 0.0







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      tuple[int | float, int | float]:

     """simple docstring"""







     _lowerCAmelCase   :					Union[str, Any]        =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
     _lowerCAmelCase   :					Optional[Any]        =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
     return lowest, highest







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    512
     _lowerCAmelCase   :					Tuple        =    [1] + [0] * (size - 1)
     _lowerCAmelCase   :					Optional[Any]        =    [filter_type.process(SCREAMING_SNAKE_CASE				) for item in inputs]

     _lowerCAmelCase   :					Dict        =    [0] * (samplerate - size)  # zero-padding
     outputs += filler
     _lowerCAmelCase   :					Dict        =    np.abs(np.fft.fft(SCREAMING_SNAKE_CASE				)				)
     _lowerCAmelCase   :					str        =    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
     _lowerCAmelCase   :					Tuple        =    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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					Tuple        =    512
     _lowerCAmelCase   :					Tuple        =    [1] + [0] * (size - 1)
     _lowerCAmelCase   :					Dict        =    [filter_type.process(SCREAMING_SNAKE_CASE				) for item in inputs]

     _lowerCAmelCase   :					Tuple        =    [0] * (samplerate - size)  # zero-padding
     outputs += filler
     _lowerCAmelCase   :					Tuple        =    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()






