from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_lowercase							:      Tuple      							=2_9_9_7_9_2_4_5_8

# Symbols
_lowercase							,	_lowercase							,	_lowercase							,	_lowercase							:      Any      							=symbols('''ct x y z''')



def A__  (			lowercase:     float					)				->		float:
       if velocity > c:
              raise ValueError('Speed must not exceed light speed 299,792,458 [m/s]!'					)
       elif velocity < 1:
              # Usually the speed should be much higher than 1 (c order of magnitude)
              raise ValueError('Speed must be greater than or equal to 1!'					)

       return velocity / c



def A__  (			lowercase:     float					)				->		float:
       return 1 / sqrt(1 - beta(lowercase					) ** 2					)



def A__  (			lowercase:     float					)				->		np.ndarray:
       return np.array(
           [
               [gamma(lowercase					), -gamma(lowercase					) * beta(lowercase					), 0, 0],
               [-gamma(lowercase					) * beta(lowercase					), gamma(lowercase					), 0, 0],
               [0, 0, 1, 0],
               [0, 0, 0, 1],
           ]					)



def A__  (			lowercase:     float,		lowercase:     np.ndarray | None = None					)				->		np.ndarray:
       # Ensure event is not empty
       if event is None:
              A						:     List[str]         =np.array([ct, x, y, z]					)  # Symbolic four vector
       else:
              event[0] *= c  # x0 is ct (speed of light * time)

       return transformation_matrix(lowercase					) @ event


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Example of symbolic vector:
    _lowercase							:      Dict      							=transform(2_9_9_7_9_2_4_5)
    print('''Example of four vector: ''')
    print(f'''ct\' = {four_vector[0]}''')
    print(f'''x\' = {four_vector[1]}''')
    print(f'''y\' = {four_vector[2]}''')
    print(f'''z\' = {four_vector[3]}''')

    # Substitute symbols with numerical values
    _lowercase							:      Dict      							={ct: c, x: 1, y: 1, z: 1}
    _lowercase							:      Dict      							=[four_vector[i].subs(sub_dict) for i in range(4)]

    print(f'''\n{numerical_vector}''')







# Copyright 2021 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

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_lowercase							:      List[str]      							='''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''



def A__  (			)				->		List[Any]:
       A						:     Any         =_ask_options(
           'In which compute environment are you running?',		['This machine', 'AWS (Amazon SageMaker)'],		_convert_compute_environment,		)
       if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
              A						:     Tuple         =get_sagemaker_input()
       else:
              A						:     str         =get_cluster_input()
       return config



def A__  (			lowercase:     int=None					)				->		str:
       if subparsers is not None:
              A						:     List[str]         =subparsers.add_parser('config',		description=lowercase					)
       else:
              A						:     Union[str, Any]         =argparse.ArgumentParser('Accelerate config command',		description=lowercase					)

       parser.add_argument(
           '--config_file',		default=lowercase,		help=(
               'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
               'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
               'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
               'with \'huggingface\'.'
           ),		)

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



def A__  (			lowercase:     Tuple					)				->		List[Any]:
       A						:     Union[str, Any]         =get_user_input()
       if args.config_file is not None:
              A						:     Optional[Any]         =args.config_file
       else:
              if not os.path.isdir(lowercase					):
                     os.makedirs(lowercase					)
              A						:     Union[str, Any]         =default_yaml_config_file

       if config_file.endswith('.json'					):
              config.to_json_file(lowercase					)
       else:
              config.to_yaml_file(lowercase					)
       print(F'accelerate configuration saved at {config_file}'					)



def A__  (			)				->		Optional[int]:
       A						:     Any         =config_command_parser()
       A						:     int         =parser.parse_args()
       config_command(lowercase					)


if __name__ == "__main__":
    main()






