




'''simple docstring'''
# 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


_SCREAMING_SNAKE_CASE			=						"Create a default config file for Accelerate with only a few flags set."





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_="no"       ,SCREAMING_SNAKE_CASE_ = default_json_config_file       ,SCREAMING_SNAKE_CASE_ = False     )		->		Dict:
			A_									= Path(SCREAMING_SNAKE_CASE_     )
			path.parent.mkdir(parents=SCREAMING_SNAKE_CASE_       ,exist_ok=SCREAMING_SNAKE_CASE_     )
			if path.exists():
						print(
						    F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'     )
						return False
			A_									= mixed_precision.lower()
			if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
						raise ValueError(
						    F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'     )
			A_									= {
			    'compute_environment': 'LOCAL_MACHINE',
			    'mixed_precision': mixed_precision,
			}
			if torch.cuda.is_available():
						A_									= torch.cuda.device_count()
						A_									= num_gpus
						A_									= False
						if num_gpus > 1:
									A_									= 'MULTI_GPU'
						else:
									A_									= 'NO'
			elif is_xpu_available() and use_xpu:
						A_									= torch.xpu.device_count()
						A_									= num_xpus
						A_									= False
						if num_xpus > 1:
									A_									= 'MULTI_XPU'
						else:
									A_									= 'NO'
			elif is_npu_available():
						A_									= torch.npu.device_count()
						A_									= num_npus
						A_									= False
						if num_npus > 1:
									A_									= 'MULTI_NPU'
						else:
									A_									= 'NO'
			else:
						A_									= 0
						A_									= True
						A_									= 1
						A_									= 'NO'
			A_									= ClusterConfig(**SCREAMING_SNAKE_CASE_     )
			config.to_json_file(SCREAMING_SNAKE_CASE_     )
			return path





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Dict:
			A_									= parser.add_parser('default'       ,parents=SCREAMING_SNAKE_CASE_       ,help=SCREAMING_SNAKE_CASE_       ,formatter_class=SCREAMING_SNAKE_CASE_     )
			parser.add_argument(
			    '--config_file'       ,default=SCREAMING_SNAKE_CASE_       ,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\'.'
			    )       ,dest='save_location'       ,)

			parser.add_argument(
			    '--mixed_precision'       ,choices=['no', 'fp16', 'bf16']       ,type=SCREAMING_SNAKE_CASE_       ,help='Whether or not to use mixed precision training. '
			    'Choose between FP16 and BF16 (bfloat16) training. '
			    'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'       ,default='no'       ,)
			parser.set_defaults(func=SCREAMING_SNAKE_CASE_     )
			return parser






def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		Dict:
			A_									= write_basic_config(args.mixed_precision       ,args.save_location     )
			if config_file:
						print(F'accelerate configuration saved at {config_file}'     )










'''simple docstring'''
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		tuple[float, float]:

			# Check if the input is valid
			if not len(SCREAMING_SNAKE_CASE_     ) == len(SCREAMING_SNAKE_CASE_     ) == 3:
						raise ValueError('Please enter a valid equation.'     )
			if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
						raise ValueError('Both a & b of two equations can\'t be zero.'     )

			# Extract the coefficients
			A_		, A_		, A_									= equationa
			A_		, A_		, A_									= equationa

			# Calculate the determinants of the matrices
			A_									= aa * ba - aa * ba
			A_									= ca * ba - ca * ba
			A_									= aa * ca - aa * ca

			# Check if the system of linear equations has a solution (using Cramer's rule)
			if determinant == 0:
						if determinant_x == determinant_y == 0:
									raise ValueError('Infinite solutions. (Consistent system)'     )
						else:
									raise ValueError('No solution. (Inconsistent system)'     )
			else:
						if determinant_x == determinant_y == 0:
									# Trivial solution (Inconsistent system)
									return (0.0, 0.0)
						else:
									A_									= determinant_x / determinant
									A_									= determinant_y / determinant
									# Non-Trivial Solution (Consistent system)
									return (x, y)




