

"""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


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






def     a__    (    __SCREAMING_SNAKE_CASE="no"					, __SCREAMING_SNAKE_CASE = default_json_config_file					, __SCREAMING_SNAKE_CASE = False )	->	Optional[Any]:
		__lowerCAmelCase:       Any          =			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
		__lowerCAmelCase:       Any          =			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}" )
		__lowerCAmelCase:       Optional[int]          =			{
		    """compute_environment""": """LOCAL_MACHINE""",
		    """mixed_precision""": mixed_precision,
		}
		if torch.cuda.is_available():
				__lowerCAmelCase:       str          =			torch.cuda.device_count()
				__lowerCAmelCase:       Tuple          =			num_gpus
				__lowerCAmelCase:       Tuple          =			False
				if num_gpus > 1:
						__lowerCAmelCase:       str          =			"""MULTI_GPU"""
				else:
						__lowerCAmelCase:       Any          =			"""NO"""
		elif is_xpu_available() and use_xpu:
				__lowerCAmelCase:       Dict          =			torch.xpu.device_count()
				__lowerCAmelCase:       List[Any]          =			num_xpus
				__lowerCAmelCase:       List[Any]          =			False
				if num_xpus > 1:
						__lowerCAmelCase:       List[Any]          =			"""MULTI_XPU"""
				else:
						__lowerCAmelCase:       List[Any]          =			"""NO"""
		elif is_npu_available():
				__lowerCAmelCase:       Optional[Any]          =			torch.npu.device_count()
				__lowerCAmelCase:       List[str]          =			num_npus
				__lowerCAmelCase:       Any          =			False
				if num_npus > 1:
						__lowerCAmelCase:       int          =			"""MULTI_NPU"""
				else:
						__lowerCAmelCase:       Tuple          =			"""NO"""
		else:
				__lowerCAmelCase:       str          =			0
				__lowerCAmelCase:       List[str]          =			True
				__lowerCAmelCase:       Tuple          =			1
				__lowerCAmelCase:       Optional[int]          =			"""NO"""
		__lowerCAmelCase:       Optional[Any]          =			ClusterConfig(**SCREAMING_SNAKE_CASE__ )
		config.to_json_file(SCREAMING_SNAKE_CASE__ )
		return path






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	int:
		__lowerCAmelCase:       str          =			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     a__    (    __SCREAMING_SNAKE_CASE )	->	Union[str, Any]:
		__lowerCAmelCase:       Optional[Any]          =			write_basic_config(args.mixed_precision					, args.save_location )
		if config_file:
				print(F"accelerate configuration saved at {config_file}" )









from statistics import mean

import numpy as np







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    list:
  __snake_case:						List[Any]   =						0
  # Number of processes finished
  __snake_case:						Union[str, Any]   =						0
  # Displays the finished process.
  # If it is 0, the performance is completed if it is 1, before the performance.
  __snake_case:						Dict   =						[0] * no_of_process
  # List to include calculation results
  __snake_case:						Tuple   =						[0] * no_of_process

  # Sort by arrival time.
  __snake_case:						int   =						[burst_time[i] for i in np.argsort(SCREAMING_SNAKE_CASE__)]
  __snake_case:						Any   =						[process_name[i] for i in np.argsort(SCREAMING_SNAKE_CASE__)]
  arrival_time.sort()

  while no_of_process > finished_process_count:
    __snake_case:						Tuple   =						0
    while finished_process[i] == 1:
      i += 1
    if current_time < arrival_time[i]:
      __snake_case:						Any   =						arrival_time[i]

    __snake_case:						List[Any]   =						0
    # Index showing the location of the process being performed
    __snake_case:						Union[str, Any]   =						0
    # Saves the current response ratio.
    __snake_case:						Optional[Any]   =						0
    for i in range(0					,					SCREAMING_SNAKE_CASE__):
      if finished_process[i] == 0 and arrival_time[i] <= current_time:
        __snake_case:						Tuple   =						(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
            i
        ]
      if response_ratio < temp:
        __snake_case:						Union[str, Any]   =						temp
        __snake_case:						Optional[int]   =						i

        # Calculate the turn around time
    __snake_case:						Optional[Any]   =						current_time + burst_time[loc] - arrival_time[loc]
    current_time += burst_time[loc]
    # Indicates that the process has been performed.
    __snake_case:						Optional[int]   =						1
    # Increase finished_process_count by 1
    finished_process_count += 1

  return turn_around_time







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    list:
  __snake_case:						Union[str, Any]   =						[0] * no_of_process
  for i in range(0					,					SCREAMING_SNAKE_CASE__):
    __snake_case:						Optional[int]   =						turn_around_time[i] - burst_time[i]
  return waiting_time


if __name__ == "__main__":
      __UpperCAmelCase       :   Optional[Any]				       =							5
      __UpperCAmelCase       :   Tuple				       =							["A", "B", "C", "D", "E"]
      __UpperCAmelCase       :   str				       =							[1, 2, 3, 4, 5]
      __UpperCAmelCase       :   Dict				       =							[1, 2, 3, 4, 5]

      __UpperCAmelCase       :   List[str]				       =							calculate_turn_around_time(
          process_name, arrival_time, burst_time, no_of_process
      )
      __UpperCAmelCase       :   List[str]				       =							calculate_waiting_time(
          process_name, turn_around_time, burst_time, no_of_process
      )

      print("Process name \tArrival time \tBurst time \tTurn around time \tWaiting time")
      for i in range(0, no_of_process):
            print(
                f'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
                f'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
            )

      print(f'average waiting time : {mean(waiting_time):.5f}')
      print(f'average turn around time : {mean(turn_around_time):.5f}')






