





'''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[int]		=				True
except ImportError:
   UpperCAmelCase       :		Optional[Any]		=				False


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




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						List[str]         =  importlib.util.find_spec(lowerCAmelCase_						) is not None
      if package_exists:
            try:
                  _snake_case		:						Optional[Any]         =  importlib.metadata.metadata(lowerCAmelCase_						)
                  return True
            except importlib.metadata.PackageNotFoundError:
                  return False




def  _a     (				):





      """simple docstring"""



      return _torch_distributed_available




def  _a     (				):





      """simple docstring"""



      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  _a     (				):





      """simple docstring"""



      return importlib.metadata.version('''oneccl_bind_pt'''						)




def  _a     (				):





      """simple docstring"""



      return _is_package_available('''transformer_engine'''						)




@lru_cache
def  _a     (				lowerCAmelCase_=True						):





      """simple docstring"""



      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		:						Union[str, Any]         =  xm.xla_device()
                  return True
            except RuntimeError:
                  return False
      return _tpu_available




def  _a     (				):





      """simple docstring"""



      return _is_package_available('''deepspeed'''						)




def  _a     (				lowerCAmelCase_=False						):





      """simple docstring"""



      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  _a     (				):





      """simple docstring"""



      _snake_case		:						str         =  _is_package_available('''bitsandbytes'''						)
      if package_exists:
            _snake_case		:						Any         =  version.parse(importlib.metadata.version('''bitsandbytes'''						)						)
            return compare_versions(lowerCAmelCase_      ,   '''>='''      ,   '''0.39.0'''						)
      return False




def  _a     (				):





      """simple docstring"""



      _snake_case		:						Union[str, Any]         =  _is_package_available('''bitsandbytes'''						)
      if package_exists:
            _snake_case		:						List[str]         =  version.parse(importlib.metadata.version('''bitsandbytes'''						)						)
            return compare_versions(lowerCAmelCase_      ,   '''>='''      ,   '''0.37.2'''						)
      return False




def  _a     (				):





      """simple docstring"""



      return _is_package_available('''bitsandbytes'''						)




def  _a     (				):





      """simple docstring"""



      if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''      ,   '''False'''						)						) == 1:
            _snake_case		:						Tuple         =  importlib.util.find_spec('''megatron'''						) is not None
            if package_exists:
                  try:
                        _snake_case		:						List[str]         =  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  _a     (				):





      """simple docstring"""



      return _is_package_available('''safetensors'''						)




def  _a     (				):





      """simple docstring"""



      return _is_package_available('''transformers'''						)




def  _a     (				):





      """simple docstring"""



      return _is_package_available('''datasets'''						)




def  _a     (				):





      """simple docstring"""



      return _is_package_available('''aim'''						)




def  _a     (				):





      """simple docstring"""



      return _is_package_available('''tensorboard'''						) or _is_package_available('''tensorboardX'''						)




def  _a     (				):





      """simple docstring"""



      return _is_package_available('''wandb'''						)




def  _a     (				):





      """simple docstring"""



      return _is_package_available('''comet_ml'''						)




def  _a     (				):





      """simple docstring"""



      return _is_package_available('''boto3'''						)




def  _a     (				):





      """simple docstring"""



      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  _a     (				):





      """simple docstring"""



      return _is_package_available('''sagemaker'''						)




def  _a     (				):





      """simple docstring"""



      return _is_package_available('''tqdm'''						)




def  _a     (				):





      """simple docstring"""



      return _is_package_available('''mlflow'''						)




def  _a     (				):





      """simple docstring"""



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




def  _a     (				):





      """simple docstring"""



      def get_major_and_minor_from_version(lowerCAmelCase_						):
            return str(version.parse(lowerCAmelCase_						).major						) + "." + str(version.parse(lowerCAmelCase_						).minor						)

      _snake_case		:						int         =  importlib.metadata.version('''torch'''						)
      if importlib.util.find_spec('''intel_extension_for_pytorch'''						) is None:
            return False
      _snake_case		:						Union[str, Any]         =  '''N/A'''
      try:
            _snake_case		:						Any         =  importlib.metadata.version('''intel_extension_for_pytorch'''						)
      except importlib.metadata.PackageNotFoundError:
            return False
      _snake_case		:						List[str]         =  get_major_and_minor_from_version(_torch_version						)
      _snake_case		:						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  _a     (				lowerCAmelCase_=False						):





      """simple docstring"""



      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		:						Union[str, Any]         =  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  _a     (				lowerCAmelCase_=False						):





      """simple docstring"""



      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		:						List[str]         =  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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase       :		Dict		=				{'configuration_hubert': ['HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'HubertConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Union[str, Any]		=				[
       'HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'HubertForCTC',
       'HubertForSequenceClassification',
       'HubertModel',
       'HubertPreTrainedModel',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Optional[int]		=				[
       'TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFHubertForCTC',
       'TFHubertModel',
       'TFHubertPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_hubert import (
          HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          HubertForCTC,
          HubertForSequenceClassification,
          HubertModel,
          HubertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_hubert import (
          TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFHubertForCTC,
          TFHubertModel,
          TFHubertPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase       :		Optional[int]		=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


