

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

     lowercase_									=    True
except ImportError:
     lowercase_									=    False


# Cache this result has it's a C FFI call which can be pretty time-consuming
lowercase_									=    torch.distributed.is_available()
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Tuple )				-> Tuple:





    '''simple docstring'''






    A__        =  importlib.util.find_spec(_UpperCAmelCase ) is not None
    if package_exists:
        try:
            A__        =  importlib.metadata.metadata(_UpperCAmelCase )
            return True
        except importlib.metadata.PackageNotFoundError:
            return False
def 		_snake_case(       )				-> Any:





    '''simple docstring'''






    return _torch_distributed_available
def 		_snake_case(       )				-> int:





    '''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 		_snake_case(       )				-> int:





    '''simple docstring'''






    return importlib.metadata.version('oneccl_bind_pt' )
def 		_snake_case(       )				-> Union[str, Any]:





    '''simple docstring'''






    return _is_package_available('transformer_engine' )
@lru_cache
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Tuple=True )				-> int:





    '''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
            A__        =  xm.xla_device()
            return True
        except RuntimeError:
            return False
    return _tpu_available
def 		_snake_case(       )				-> str:





    '''simple docstring'''






    return _is_package_available('deepspeed' )
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Optional[int]=False )				-> Optional[int]:





    '''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 		_snake_case(       )				-> Any:





    '''simple docstring'''






    A__        =  _is_package_available('bitsandbytes' )
    if package_exists:
        A__        =  version.parse(importlib.metadata.version('bitsandbytes' ) )
        return compare_versions(_UpperCAmelCase       ,     '>='       ,     '0.39.0' )
    return False
def 		_snake_case(       )				-> List[Any]:





    '''simple docstring'''






    A__        =  _is_package_available('bitsandbytes' )
    if package_exists:
        A__        =  version.parse(importlib.metadata.version('bitsandbytes' ) )
        return compare_versions(_UpperCAmelCase       ,     '>='       ,     '0.37.2' )
    return False
def 		_snake_case(       )				-> Tuple:





    '''simple docstring'''






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





    '''simple docstring'''






    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(_UpperCAmelCase       ,     '>='       ,     '2.2.0' )
            except Exception as e:
                warnings.warn(f'Parse Megatron version failed. Exception:{e}' )
                return False
def 		_snake_case(       )				-> Dict:





    '''simple docstring'''






    return _is_package_available('safetensors' )
def 		_snake_case(       )				-> List[Any]:





    '''simple docstring'''






    return _is_package_available('transformers' )
def 		_snake_case(       )				-> List[Any]:





    '''simple docstring'''






    return _is_package_available('datasets' )
def 		_snake_case(       )				-> List[str]:





    '''simple docstring'''






    return _is_package_available('aim' )
def 		_snake_case(       )				-> Tuple:





    '''simple docstring'''






    return _is_package_available('tensorboard' ) or _is_package_available('tensorboardX' )
def 		_snake_case(       )				-> int:





    '''simple docstring'''






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





    '''simple docstring'''






    return _is_package_available('comet_ml' )
def 		_snake_case(       )				-> Any:





    '''simple docstring'''






    return _is_package_available('boto3' )
def 		_snake_case(       )				-> List[str]:





    '''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'       ,     _UpperCAmelCase )
        return parse_flag_from_env('ACCELERATE_ENABLE_RICH'       ,     _UpperCAmelCase )
    return False
def 		_snake_case(       )				-> Any:





    '''simple docstring'''






    return _is_package_available('sagemaker' )
def 		_snake_case(       )				-> int:





    '''simple docstring'''






    return _is_package_available('tqdm' )
def 		_snake_case(       )				-> Any:





    '''simple docstring'''






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





    '''simple docstring'''






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





    '''simple docstring'''






    def get_major_and_minor_from_version(SCREAMING_SNAKE_CASE__     :      Any ):
        return str(version.parse(_UpperCAmelCase ).major ) + "." + str(version.parse(_UpperCAmelCase ).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 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Tuple=False )				-> List[Any]:





    '''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
            A__        =  torch.npu.device_count()
            return torch.npu.is_available()
        except RuntimeError:
            return False
    return hasattr(_UpperCAmelCase       ,     'npu' ) and torch.npu.is_available()

@lru_cache
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      str=False )				-> List[str]:





    '''simple docstring'''






    if not parse_flag_from_env('ACCELERATE_USE_XPU'       ,     default=_UpperCAmelCase ):
        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(_UpperCAmelCase       ,     'xpu' ) and torch.xpu.is_available()






from __future__ import annotations

class        _A			:



       def __init__(						self				:	str	,						__SCREAMING_SNAKE_CASE				:	int = 0):


           '''simple docstring'''

           __a					=							value
           __a					=							None
           __a					=							None





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase							):
    if treea is None:
        return treea
    if treea is None:
        return treea

    __a					=							treea.value + treea.value
    __a					=							merge_two_binary_trees(treea.left  ,					treea.left							)
    __a					=							merge_two_binary_trees(treea.right  ,					treea.right							)
    return treea





def 	__snake_case      (     _UpperCAmelCase							):
    if root:
        print(root.value							)
        print_preorder(root.left							)
        print_preorder(root.right							)


if __name__ == "__main__":
 __snake_case		:List[Any] 						=	Node(1)
 __snake_case		:Optional[Any] 						=	Node(2)
 __snake_case		:Dict 						=	Node(3)
 __snake_case		:int 						=	Node(4)

 __snake_case		:List[str] 						=	Node(2)
 __snake_case		:List[str] 						=	Node(4)
 __snake_case		:Tuple 						=	Node(6)
 __snake_case		:str 						=	Node(9)
 __snake_case		:List[str] 						=	Node(5)

 print('''Tree1 is: ''')
 print_preorder(treea)
 print('''Tree2 is: ''')
 print_preorder(treea)
 __snake_case		:Union[str, Any] 						=	merge_two_binary_trees(treea, treea)
 print('''Merged Tree is: ''')
 print_preorder(merged_tree)





