

"""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			      =		True
except ImportError:
 _UpperCamelCase			      =		False


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







def 		_lowercase			(					lowercase__						):
      # Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
      __lowerCAmelCase :						int = importlib.util.find_spec(SCREAMING_SNAKE_CASE_						) is not None
      if package_exists:
            try:
                  __lowerCAmelCase :						Union[str, Any] = importlib.metadata.metadata(SCREAMING_SNAKE_CASE_						)
                  return True
            except importlib.metadata.PackageNotFoundError:
                  return False







def 		_lowercase			(					):
      return _torch_distributed_available







def 		_lowercase			(					):
      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 		_lowercase			(					):
      return importlib.metadata.version('''oneccl_bind_pt'''						)







def 		_lowercase			(					):
      return _is_package_available('''transformer_engine'''						)







@lru_cache
def 		_lowercase			(					lowercase__=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
                  __lowerCAmelCase :						int = xm.xla_device()
                  return True
            except RuntimeError:
                  return False
      return _tpu_available







def 		_lowercase			(					):
      return _is_package_available('''deepspeed'''						)







def 		_lowercase			(					lowercase__=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 		_lowercase			(					):
      __lowerCAmelCase :						Optional[int] = _is_package_available('''bitsandbytes'''						)
      if package_exists:
            __lowerCAmelCase :						str = version.parse(importlib.metadata.version('''bitsandbytes'''						)						)
            return compare_versions(SCREAMING_SNAKE_CASE_       ,     '''>='''       ,     '''0.39.0'''						)
      return False







def 		_lowercase			(					):
      __lowerCAmelCase :						int = _is_package_available('''bitsandbytes'''						)
      if package_exists:
            __lowerCAmelCase :						Optional[int] = version.parse(importlib.metadata.version('''bitsandbytes'''						)						)
            return compare_versions(SCREAMING_SNAKE_CASE_       ,     '''>='''       ,     '''0.37.2'''						)
      return False







def 		_lowercase			(					):
      return _is_package_available('''bitsandbytes'''						)







def 		_lowercase			(					):
      if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''       ,     '''False'''						)						) == 1:
            __lowerCAmelCase :						int = importlib.util.find_spec('''megatron'''						) is not None
            if package_exists:
                  try:
                        __lowerCAmelCase :						Dict = 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 		_lowercase			(					):
      return _is_package_available('''safetensors'''						)







def 		_lowercase			(					):
      return _is_package_available('''transformers'''						)







def 		_lowercase			(					):
      return _is_package_available('''datasets'''						)







def 		_lowercase			(					):
      return _is_package_available('''aim'''						)







def 		_lowercase			(					):
      return _is_package_available('''tensorboard'''						) or _is_package_available('''tensorboardX'''						)







def 		_lowercase			(					):
      return _is_package_available('''wandb'''						)







def 		_lowercase			(					):
      return _is_package_available('''comet_ml'''						)







def 		_lowercase			(					):
      return _is_package_available('''boto3'''						)







def 		_lowercase			(					):
      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 		_lowercase			(					):
      return _is_package_available('''sagemaker'''						)







def 		_lowercase			(					):
      return _is_package_available('''tqdm'''						)







def 		_lowercase			(					):
      return _is_package_available('''mlflow'''						)







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







def 		_lowercase			(					):
      def get_major_and_minor_from_version(lowercase__						):
            return str(version.parse(SCREAMING_SNAKE_CASE_						).major						) + "." + str(version.parse(SCREAMING_SNAKE_CASE_						).minor						)

      __lowerCAmelCase :						List[Any] = importlib.metadata.version('''torch'''						)
      if importlib.util.find_spec('''intel_extension_for_pytorch'''						) is None:
            return False
      __lowerCAmelCase :						Dict = '''N/A'''
      try:
            __lowerCAmelCase :						Tuple = importlib.metadata.version('''intel_extension_for_pytorch'''						)
      except importlib.metadata.PackageNotFoundError:
            return False
      __lowerCAmelCase :						Optional[Any] = get_major_and_minor_from_version(_torch_version						)
      __lowerCAmelCase :						Optional[Any] = 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 		_lowercase			(					lowercase__=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
                  __lowerCAmelCase :						int = 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 		_lowercase			(					lowercase__=False						):
      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
                  __lowerCAmelCase :						Union[str, Any] = torch.xpu.device_count()
                  return torch.xpu.is_available()
            except RuntimeError:
                  return False
      return hasattr(SCREAMING_SNAKE_CASE_       ,     '''xpu'''						) and torch.xpu.is_available()





def 		_lowercase			(					lowercase__						):
      return "".join(chr(ord(lowercase__						) + 3_2						) if '''A''' <= char <= '''Z''' else char for char in word						)


if __name__ == "__main__":
 from doctest import testmod

 testmod()


