
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

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


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





def        lowerCAmelCase       (				snake_case__   :      Optional[Any] )->			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(snake_case__ ) is not None
    if package_exists:
        try:
            A_								=					importlib.metadata.metadata(snake_case__ )
            return True
        except importlib.metadata.PackageNotFoundError:
            return False





def        lowerCAmelCase       (				)->			bool:
    return _torch_distributed_available





def        lowerCAmelCase       (				)->			Union[str, 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        lowerCAmelCase       (				)->			List[str]:
    return importlib.metadata.version("oneccl_bind_pt" )





def        lowerCAmelCase       (				)->			Tuple:
    return _is_package_available("transformer_engine" )





@lru_cache
def        lowerCAmelCase       (				snake_case__   :      Optional[Any]=True )->			Optional[Any]:
    # 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        lowerCAmelCase       (				)->			Optional[Any]:
    return _is_package_available("deepspeed" )





def        lowerCAmelCase       (				snake_case__   :      Dict=False )->			int:
    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        lowerCAmelCase       (				)->			List[Any]:
    A_								=					_is_package_available("bitsandbytes" )
    if package_exists:
        A_								=					version.parse(importlib.metadata.version("bitsandbytes" ) )
        return compare_versions(snake_case__				,					">="				,					"0.39.0" )
    return False





def        lowerCAmelCase       (				)->			Optional[Any]:
    A_								=					_is_package_available("bitsandbytes" )
    if package_exists:
        A_								=					version.parse(importlib.metadata.version("bitsandbytes" ) )
        return compare_versions(snake_case__				,					">="				,					"0.37.2" )
    return False





def        lowerCAmelCase       (				)->			Any:
    return _is_package_available("bitsandbytes" )





def        lowerCAmelCase       (				)->			int:
    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(snake_case__				,					">="				,					"2.2.0" )
            except Exception as e:
                warnings.warn(f'Parse Megatron version failed. Exception:{e}' )
                return False





def        lowerCAmelCase       (				)->			Dict:
    return _is_package_available("safetensors" )





def        lowerCAmelCase       (				)->			List[str]:
    return _is_package_available("transformers" )





def        lowerCAmelCase       (				)->			str:
    return _is_package_available("datasets" )





def        lowerCAmelCase       (				)->			List[str]:
    return _is_package_available("aim" )





def        lowerCAmelCase       (				)->			Tuple:
    return _is_package_available("tensorboard" ) or _is_package_available("tensorboardX" )





def        lowerCAmelCase       (				)->			Dict:
    return _is_package_available("wandb" )





def        lowerCAmelCase       (				)->			List[Any]:
    return _is_package_available("comet_ml" )





def        lowerCAmelCase       (				)->			Optional[int]:
    return _is_package_available("boto3" )





def        lowerCAmelCase       (				)->			Dict:
    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"				,					snake_case__ )
        return parse_flag_from_env("ACCELERATE_ENABLE_RICH"				,					snake_case__ )
    return False





def        lowerCAmelCase       (				)->			int:
    return _is_package_available("sagemaker" )





def        lowerCAmelCase       (				)->			int:
    return _is_package_available("tqdm" )





def        lowerCAmelCase       (				)->			int:
    return _is_package_available("mlflow" )





def        lowerCAmelCase       (				)->			str:
    return is_torch_version(">="				,					"1.12" ) and torch.backends.mps.is_available() and torch.backends.mps.is_built()





def        lowerCAmelCase       (				)->			List[str]:
    def get_major_and_minor_from_version(snake_case__   :      List[str] ):
        return str(version.parse(snake_case__ ).major ) + "." + str(version.parse(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        lowerCAmelCase       (				snake_case__   :      List[Any]=False )->			int:
    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(snake_case__				,					"npu" ) and torch.npu.is_available()





@lru_cache
def        lowerCAmelCase       (				snake_case__   :      List[Any]=False )->			Dict:
    if not parse_flag_from_env("ACCELERATE_USE_XPU"				,					default=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(snake_case__				,					"xpu" ) and torch.xpu.is_available()








import sys

sys.set_int_max_str_digits(0)  # type: ignore





def        lowerCAmelCase       (				snake_case__   :      int )->			bool:
    A_								=					[0] * 11
    A_								=					[0] * 11

    # mark last 9 numbers
    for _ in range(9 ):
        A_								=					1
        A_								=					number // 10
    # flag
    A_								=					True

    # check last 9 numbers for pandigitality

    for x in range(9 ):
        if not check_last[x + 1]:
            A_								=					False
    if not f:
        return f

    # mark first 9 numbers
    A_								=					int(str(snake_case__ )[:9] )

    for _ in range(9 ):
        A_								=					1
        A_								=					number // 10

    # check first 9 numbers for pandigitality

    for x in range(9 ):
        if not check_front[x + 1]:
            A_								=					False
    return f





def        lowerCAmelCase       (				snake_case__   :      int )->			bool:
    A_								=					[0] * 11

    # mark last 9 numbers
    for _ in range(9 ):
        A_								=					1
        A_								=					number // 10
    # flag
    A_								=					True

    # check last 9 numbers for pandigitality

    for x in range(9 ):
        if not check_last[x + 1]:
            A_								=					False
    return f





def        lowerCAmelCase       (				)->			int:
    A_								=					1
    A_								=					1
    A_								=					2
    # temporary Fibonacci numbers

    A_								=					1
    A_								=					1
    A_								=					2
    # temporary Fibonacci numbers mod 1e9

    # mod m=1e9, done for fast optimisation
    A_								=					[0] * 1000000
    A_								=					1000000000

    for x in range(1000000 ):
        A_								=					(aa + ba) % m
        A_								=					ba % m
        A_								=					ca % m
        if checka(snake_case__ ):
            A_								=					1

    for x in range(1000000 ):
        A_								=					a + b
        A_								=					b
        A_								=					c
        # perform check only if in tocheck
        if tocheck[x + 3] and check(snake_case__ ):
            return x + 3  # first 2 already done
    return -1


if __name__ == "__main__":
     print(f"""{solution() = }""")






