




"""simple docstring"""

import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
		import torch_xla.core.xla_model as xm  # noqa: F401

		UpperCamelCase__			:     Optional[Any]			       =				True
except ImportError:
		UpperCamelCase__			:     Dict			       =				False


# Cache this result has it's a C FFI call which can be pretty time-consuming
UpperCamelCase__			:     List[str]			       =				torch.distributed.is_available()




def    snake_case__						(							lowerCAmelCase :		str		):
					# Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
					__snake_case        =						importlib.util.find_spec(lowerCAmelCase		) is not None
					if package_exists:
										try:
															__snake_case        =						importlib.metadata.metadata(lowerCAmelCase		)
															return True
										except importlib.metadata.PackageNotFoundError:
															return False




def    snake_case__						(							):
					return _torch_distributed_available




def    snake_case__						(							):
					try:
										pass
					except ImportError:
										print(
										    """Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not"""
										    """ detected. If you see \"ValueError: Invalid backend: 'ccl'\" error, please install Intel(R) oneCCL"""
										    """ Bindings for PyTorch*."""		)
					return (
					    importlib.util.find_spec("""torch_ccl"""		) is not None
					    or importlib.util.find_spec("""oneccl_bindings_for_pytorch"""		) is not None
					)




def    snake_case__						(							):
					return importlib.metadata.version("""oneccl_bind_pt"""		)




def    snake_case__						(							):
					return _is_package_available("""transformer_engine"""		)




@lru_cache
def    snake_case__						(							lowerCAmelCase :		Dict=True		):
					# Due to bugs on the amp series GPUs, we disable torch-xla on them
					if torch.cuda.is_available():
										return False
					if _tpu_available and check_device:
										try:
															# Will raise a RuntimeError if no XLA configuration is found
															__snake_case        =						xm.xla_device()
															return True
										except RuntimeError:
															return False
					return _tpu_available




def    snake_case__						(							):
					return _is_package_available("""deepspeed"""		)




def    snake_case__						(							lowerCAmelCase :		Union[str, Any]=False		):
					if is_tpu_available():
										return not ignore_tpu
					if torch.cuda.is_available():
										return torch.cuda.is_bfaa_supported()
					if is_npu_available():
										return False
					return True




def    snake_case__						(							):
					__snake_case        =						_is_package_available("""bitsandbytes"""		)
					if package_exists:
										__snake_case        =						version.parse(importlib.metadata.version("""bitsandbytes"""		)		)
										return compare_versions(lowerCAmelCase					,       """>="""					,       """0.39.0"""		)
					return False




def    snake_case__						(							):
					__snake_case        =						_is_package_available("""bitsandbytes"""		)
					if package_exists:
										__snake_case        =						version.parse(importlib.metadata.version("""bitsandbytes"""		)		)
										return compare_versions(lowerCAmelCase					,       """>="""					,       """0.37.2"""		)
					return False




def    snake_case__						(							):
					return _is_package_available("""bitsandbytes"""		)




def    snake_case__						(							):
					if strtobool(os.environ.get("""ACCELERATE_USE_MEGATRON_LM"""					,       """False"""		)		) == 1:
										__snake_case        =						importlib.util.find_spec("""megatron"""		) is not None
										if package_exists:
															try:
																				__snake_case        =						parse(importlib.metadata.version("""megatron-lm"""		)		)
																				return compare_versions(lowerCAmelCase					,       """>="""					,       """2.2.0"""		)
															except Exception as e:
																				warnings.warn(F'''Parse Megatron version failed. Exception:{e}'''		)
																				return False




def    snake_case__						(							):
					return _is_package_available("""safetensors"""		)




def    snake_case__						(							):
					return _is_package_available("""transformers"""		)




def    snake_case__						(							):
					return _is_package_available("""datasets"""		)




def    snake_case__						(							):
					return _is_package_available("""aim"""		)




def    snake_case__						(							):
					return _is_package_available("""tensorboard"""		) or _is_package_available("""tensorboardX"""		)




def    snake_case__						(							):
					return _is_package_available("""wandb"""		)




def    snake_case__						(							):
					return _is_package_available("""comet_ml"""		)




def    snake_case__						(							):
					return _is_package_available("""boto3"""		)




def    snake_case__						(							):
					if _is_package_available("""rich"""		):
										if "ACCELERATE_DISABLE_RICH" in os.environ:
															warnings.warn(
															    """`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`."""		)
															return not parse_flag_from_env("""ACCELERATE_DISABLE_RICH"""					,       lowerCAmelCase		)
										return parse_flag_from_env("""ACCELERATE_ENABLE_RICH"""					,       lowerCAmelCase		)
					return False




def    snake_case__						(							):
					return _is_package_available("""sagemaker"""		)




def    snake_case__						(							):
					return _is_package_available("""tqdm"""		)




def    snake_case__						(							):
					return _is_package_available("""mlflow"""		)




def    snake_case__						(							):
					return is_torch_version(""">="""					,       """1.12"""		) and torch.backends.mps.is_available() and torch.backends.mps.is_built()




def    snake_case__						(							):
					def get_major_and_minor_from_version(lowerCAmelCase :		Any		):
										return str(version.parse(lowerCAmelCase		).major		) + "." + str(version.parse(lowerCAmelCase		).minor		)

					__snake_case        =						importlib.metadata.version("""torch"""		)
					if importlib.util.find_spec("""intel_extension_for_pytorch"""		) is None:
										return False
					__snake_case        =						"""N/A"""
					try:
										__snake_case        =						importlib.metadata.version("""intel_extension_for_pytorch"""		)
					except importlib.metadata.PackageNotFoundError:
										return False
					__snake_case        =						get_major_and_minor_from_version(_torch_version		)
					__snake_case        =						get_major_and_minor_from_version(_ipex_version		)
					if torch_major_and_minor != ipex_major_and_minor:
										warnings.warn(
										    F'''Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,'''
										    F''' but PyTorch {_torch_version} is found. Please switch to the matching version and run again.'''		)
										return False
					return True




@lru_cache
def    snake_case__						(							lowerCAmelCase :		List[str]=False		):
					if importlib.util.find_spec("""torch"""		) is None or importlib.util.find_spec("""torch_npu"""		) is None:
										return False

					import torch
					import torch_npu  # noqa: F401

					if check_device:
										try:
															# Will raise a RuntimeError if no NPU is found
															__snake_case        =						torch.npu.device_count()
															return torch.npu.is_available()
										except RuntimeError:
															return False
					return hasattr(lowerCAmelCase					,       """npu"""		) and torch.npu.is_available()




@lru_cache
def    snake_case__						(							lowerCAmelCase :		Tuple=False		):
					if not parse_flag_from_env("""ACCELERATE_USE_XPU"""					,       default=lowerCAmelCase		):
										return False
					if is_ipex_available():
										import torch

										if is_torch_version("""<="""					,       """1.12"""		):
															return False
					else:
										return False

					import intel_extension_for_pytorch  # noqa: F401

					if check_device:
										try:
															# Will raise a RuntimeError if no XPU  is found
															__snake_case        =						torch.xpu.device_count()
															return torch.xpu.is_available()
										except RuntimeError:
															return False
					return hasattr(lowerCAmelCase					,       """xpu"""		) and torch.xpu.is_available()








"""simple docstring"""

from __future__ import annotations




def    snake_case__						(							lowerCAmelCase :		list[int]		):
					__snake_case        =						[0] * len(lowerCAmelCase		)
					for i in range(1					,       len(lowerCAmelCase		)		):
										__snake_case        =						duration_times[i - 1] + waiting_times[i - 1]
					return waiting_times




def    snake_case__						(							lowerCAmelCase :		list[int]					,       lowerCAmelCase :		list[int]		):
					return [
					    duration_time + waiting_times[i]
					    for i, duration_time in enumerate(lowerCAmelCase		)
					]




def    snake_case__						(							lowerCAmelCase :		list[int]		):
					return sum(lowerCAmelCase		) / len(lowerCAmelCase		)




def    snake_case__						(							lowerCAmelCase :		list[int]		):
					return sum(lowerCAmelCase		) / len(lowerCAmelCase		)


if __name__ == "__main__":
		# process id's
		UpperCamelCase__			:     str			       =				[1, 2, 3]

		# ensure that we actually have processes
		if len(processes) == 0:
				print("Zero amount of processes")
				raise SystemExit(0)

		# duration time of all processes
		UpperCamelCase__			:     Any			       =				[19, 8, 9]

		# ensure we can match each id to a duration time
		if len(duration_times) != len(processes):
				print("Unable to match all id's with their duration time")
				raise SystemExit(0)

		# get the waiting times and the turnaround times
		UpperCamelCase__			:     Optional[int]			       =				calculate_waiting_times(duration_times)
		UpperCamelCase__			:     Any			       =				calculate_turnaround_times(duration_times, waiting_times)

		# get the average times
		UpperCamelCase__			:     Optional[Any]			       =				calculate_average_waiting_time(waiting_times)
		UpperCamelCase__			:     Dict			       =				calculate_average_turnaround_time(turnaround_times)

		# print all the results
		print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
		for i, process in enumerate(processes):
				print(
				    f'{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'
				    f'{turnaround_times[i]}'
				)
		print(f'Average waiting time = {average_waiting_time}')
		print(f'Average turn around time = {average_turnaround_time}')


