
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

 a_       :Union[str, Any]				  =				True
except ImportError:
 a_       :str				  =				False


# Cache this result has it's a C FFI call which can be pretty time-consuming
a_       :Optional[Any]				  =				torch.distributed.is_available()
def 	lowercase_							(A			:			Union[str, Any]				):
       snake_case__							:     Tuple            =     importlib.util.find_spec(A				) is not None
       if package_exists:
              try:
                     snake_case__							:     Optional[int]            =     importlib.metadata.metadata(A				)
                     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_							(A			:			List[str]=True				):

       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__							:     str            =     xm.xla_device()
                     return True
              except RuntimeError:
                     return False
       return _tpu_available
def 	lowercase_							():
       return _is_package_available('deepspeed'				)
def 	lowercase_							(A			:			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 	lowercase_							():
       snake_case__							:     str            =     _is_package_available('bitsandbytes'				)
       if package_exists:
              snake_case__							:     List[Any]            =     version.parse(importlib.metadata.version('bitsandbytes'				)				)
              return compare_versions(A		, '>='		, '0.39.0'				)
       return False
def 	lowercase_							():
       snake_case__							:     List[Any]            =     _is_package_available('bitsandbytes'				)
       if package_exists:
              snake_case__							:     int            =     version.parse(importlib.metadata.version('bitsandbytes'				)				)
              return compare_versions(A		, '>='		, '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:
              snake_case__							:     Tuple            =     importlib.util.find_spec('megatron'				) is not None
              if package_exists:
                     try:
                            snake_case__							:     int            =     parse(importlib.metadata.version('megatron-lm'				)				)
                            return compare_versions(A		, '>='		, '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'		, A				)
              return parse_flag_from_env('ACCELERATE_ENABLE_RICH'		, A				)
       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(A			:			str				):
              return str(version.parse(A				).major				) + "." + str(version.parse(A				).minor				)

       snake_case__							:     str            =     importlib.metadata.version('torch'				)
       if importlib.util.find_spec('intel_extension_for_pytorch'				) is None:
              return False
       snake_case__							:     Optional[int]            =     'N/A'
       try:
              snake_case__							:     Dict            =     importlib.metadata.version('intel_extension_for_pytorch'				)
       except importlib.metadata.PackageNotFoundError:
              return False
       snake_case__							:     int            =     get_major_and_minor_from_version(_torch_version				)
       snake_case__							:     Union[str, 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_							(A			:			Any=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__							:     int            =     torch.npu.device_count()
                     return torch.npu.is_available()
              except RuntimeError:
                     return False
       return hasattr(A		, 'npu'				) and torch.npu.is_available()





@lru_cache
def 	lowercase_							(A			:			Optional[Any]=False				):

       if not parse_flag_from_env('ACCELERATE_USE_XPU'		, default=A				):
              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__							:     Dict            =     torch.xpu.device_count()
                     return torch.xpu.is_available()
              except RuntimeError:
                     return False
       return hasattr(A		, 'xpu'				) and torch.xpu.is_available()











def 	lowercase_							(A			:			int		, A			:			int				):
       return (input_a, input_a).count(0				) % 2
def 	lowercase_							():
       assert xor_gate(0		, 0				) == 0
       assert xor_gate(0		, 1				) == 1
       assert xor_gate(1		, 0				) == 1
       assert xor_gate(1		, 1				) == 0


if __name__ == "__main__":
 print(xor_gate(0, 0))
 print(xor_gate(0, 1))






