



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

    __lowerCamelCase								=   True
except ImportError:
    __lowerCamelCase								=   False


# Cache this result has it's a C FFI call which can be pretty time-consuming
__lowerCamelCase								=   torch.distributed.is_available()




def       UpperCAmelCase__	(							__snake_case							)				->	List[Any]:
  # Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
  _A          =      importlib.util.find_spec(SCREAMING_SNAKE_CASE_							) is not None
  if package_exists:
    try:
      _A          =      importlib.metadata.metadata(SCREAMING_SNAKE_CASE_							)
      return True
    except importlib.metadata.PackageNotFoundError:
      return False




def       UpperCAmelCase__	(							)				->	List[str]:
  return _torch_distributed_available




def       UpperCAmelCase__	(							)				->	Any:
  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       UpperCAmelCase__	(							)				->	str:
  return importlib.metadata.version('''oneccl_bind_pt'''							)




def       UpperCAmelCase__	(							)				->	Any:
  return _is_package_available('''transformer_engine'''							)




@lru_cache
def       UpperCAmelCase__	(							__snake_case=True							)				->	Tuple:
  # 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
      _A          =      xm.xla_device()
      return True
    except RuntimeError:
      return False
  return _tpu_available




def       UpperCAmelCase__	(							)				->	Dict:
  return _is_package_available('''deepspeed'''							)




def       UpperCAmelCase__	(							__snake_case=False							)				->	Union[str, Any]:
  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       UpperCAmelCase__	(							)				->	Union[str, Any]:
  _A          =      _is_package_available('''bitsandbytes'''							)
  if package_exists:
    _A          =      version.parse(importlib.metadata.version('''bitsandbytes'''							)							)
    return compare_versions(SCREAMING_SNAKE_CASE_  ,			'''>='''  ,			'''0.39.0'''							)
  return False




def       UpperCAmelCase__	(							)				->	Union[str, Any]:
  _A          =      _is_package_available('''bitsandbytes'''							)
  if package_exists:
    _A          =      version.parse(importlib.metadata.version('''bitsandbytes'''							)							)
    return compare_versions(SCREAMING_SNAKE_CASE_  ,			'''>='''  ,			'''0.37.2'''							)
  return False




def       UpperCAmelCase__	(							)				->	Optional[int]:
  return _is_package_available('''bitsandbytes'''							)




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




def       UpperCAmelCase__	(							)				->	Union[str, Any]:
  return _is_package_available('''safetensors'''							)




def       UpperCAmelCase__	(							)				->	Optional[int]:
  return _is_package_available('''transformers'''							)




def       UpperCAmelCase__	(							)				->	Any:
  return _is_package_available('''datasets'''							)




def       UpperCAmelCase__	(							)				->	List[str]:
  return _is_package_available('''aim'''							)




def       UpperCAmelCase__	(							)				->	Tuple:
  return _is_package_available('''tensorboard'''							) or _is_package_available('''tensorboardX'''							)




def       UpperCAmelCase__	(							)				->	Union[str, Any]:
  return _is_package_available('''wandb'''							)




def       UpperCAmelCase__	(							)				->	Optional[Any]:
  return _is_package_available('''comet_ml'''							)




def       UpperCAmelCase__	(							)				->	Union[str, Any]:
  return _is_package_available('''boto3'''							)




def       UpperCAmelCase__	(							)				->	Tuple:
  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'''  ,			SCREAMING_SNAKE_CASE_							)
    return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''  ,			SCREAMING_SNAKE_CASE_							)
  return False




def       UpperCAmelCase__	(							)				->	Tuple:
  return _is_package_available('''sagemaker'''							)




def       UpperCAmelCase__	(							)				->	str:
  return _is_package_available('''tqdm'''							)




def       UpperCAmelCase__	(							)				->	str:
  return _is_package_available('''mlflow'''							)




def       UpperCAmelCase__	(							)				->	Optional[Any]:
  return is_torch_version('''>='''  ,			'''1.12'''							) and torch.backends.mps.is_available() and torch.backends.mps.is_built()




def       UpperCAmelCase__	(							)				->	Optional[int]:
  def get_major_and_minor_from_version(__snake_case							):
    return str(version.parse(SCREAMING_SNAKE_CASE_							).major							) + "." + str(version.parse(SCREAMING_SNAKE_CASE_							).minor							)

  _A          =      importlib.metadata.version('''torch'''							)
  if importlib.util.find_spec('''intel_extension_for_pytorch'''							) is None:
    return False
  _A          =      '''N/A'''
  try:
    _A          =      importlib.metadata.version('''intel_extension_for_pytorch'''							)
  except importlib.metadata.PackageNotFoundError:
    return False
  _A          =      get_major_and_minor_from_version(_torch_version							)
  _A          =      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       UpperCAmelCase__	(							__snake_case=False							)				->	Tuple:
  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
      _A          =      torch.npu.device_count()
      return torch.npu.is_available()
    except RuntimeError:
      return False
  return hasattr(SCREAMING_SNAKE_CASE_  ,			'''npu'''							) and torch.npu.is_available()




@lru_cache
def       UpperCAmelCase__	(							__snake_case=False							)				->	Any:
  if not parse_flag_from_env('''ACCELERATE_USE_XPU'''  ,			default=SCREAMING_SNAKE_CASE_							):
    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
      _A          =      torch.xpu.device_count()
      return torch.xpu.is_available()
    except RuntimeError:
      return False
  return hasattr(SCREAMING_SNAKE_CASE_  ,			'''xpu'''							) and torch.xpu.is_available()




from __future__ import annotations




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




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case							)				->	list[int]:
  return [
      duration_time + waiting_times[i]
      for i, duration_time in enumerate(__snake_case							)
  ]




def       UpperCAmelCase__	(							__snake_case							)				->	float:
  return sum(__snake_case							) / len(__snake_case							)




def       UpperCAmelCase__	(							__snake_case							)				->	float:
  return sum(__snake_case							) / len(__snake_case							)


if __name__ == "__main__":
    # process id's
    __lowerCamelCase								=   [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
    __lowerCamelCase								=   [1_9, 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
    __lowerCamelCase								=   calculate_waiting_times(duration_times)
    __lowerCamelCase								=   calculate_turnaround_times(duration_times, waiting_times)

    # get the average times
    __lowerCamelCase								=   calculate_average_waiting_time(waiting_times)
    __lowerCamelCase								=   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}''')