



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   SCREAMING_SNAKE_CASE_       (  _snake_case  :Tuple							)     ->    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   SCREAMING_SNAKE_CASE_       (  )     ->    bool:
   return _torch_distributed_available

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

def   SCREAMING_SNAKE_CASE_       (  )     ->    Dict:
   return _is_package_available('''transformer_engine'''							)

@lru_cache
def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Any=True							)     ->    Tuple:
   # 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   SCREAMING_SNAKE_CASE_       (  )     ->    int:
   return _is_package_available('''deepspeed'''							)

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Optional[int]=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   SCREAMING_SNAKE_CASE_       (  )     ->    int:
   _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   SCREAMING_SNAKE_CASE_       (  )     ->    Optional[int]:
   _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   SCREAMING_SNAKE_CASE_       (  )     ->    Dict:
   return _is_package_available('''bitsandbytes'''							)

def   SCREAMING_SNAKE_CASE_       (  )     ->    Optional[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   SCREAMING_SNAKE_CASE_       (  )     ->    int:
   return _is_package_available('''safetensors'''							)

def   SCREAMING_SNAKE_CASE_       (  )     ->    Tuple:
   return _is_package_available('''transformers'''							)

def   SCREAMING_SNAKE_CASE_       (  )     ->    int:
   return _is_package_available('''datasets'''							)

def   SCREAMING_SNAKE_CASE_       (  )     ->    str:
   return _is_package_available('''aim'''							)

def   SCREAMING_SNAKE_CASE_       (  )     ->    List[Any]:
   return _is_package_available('''tensorboard'''							) or _is_package_available('''tensorboardX'''							)

def   SCREAMING_SNAKE_CASE_       (  )     ->    Optional[Any]:
   return _is_package_available('''wandb'''							)

def   SCREAMING_SNAKE_CASE_       (  )     ->    str:
   return _is_package_available('''comet_ml'''							)

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

def   SCREAMING_SNAKE_CASE_       (  )     ->    str:
   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   SCREAMING_SNAKE_CASE_       (  )     ->    int:
   return _is_package_available('''sagemaker'''							)

def   SCREAMING_SNAKE_CASE_       (  )     ->    Tuple:
   return _is_package_available('''tqdm'''							)

def   SCREAMING_SNAKE_CASE_       (  )     ->    Any:
   return _is_package_available('''mlflow'''							)

def   SCREAMING_SNAKE_CASE_       (  )     ->    List[Any]:
   return is_torch_version('''>='''  ,      '''1.12'''							) and torch.backends.mps.is_available() and torch.backends.mps.is_built()

def   SCREAMING_SNAKE_CASE_       (  )     ->    Tuple:
   def get_major_and_minor_from_version(_snake_case  :Union[str, Any]							):
      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   SCREAMING_SNAKE_CASE_       (  _snake_case  :Optional[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   SCREAMING_SNAKE_CASE_       (  _snake_case  :Optional[int]=False							)     ->    List[str]:
   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()








# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


UpperCAmelCase_		   =				os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
UpperCAmelCase_		   =				os.path.join(hf_cache_home, """accelerate""")
UpperCAmelCase_		   =				os.path.join(cache_dir, """default_config.yaml""")
UpperCAmelCase_		   =				os.path.join(cache_dir, """default_config.yaml""")

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
   UpperCAmelCase_		   =				default_yaml_config_file
else:
   UpperCAmelCase_		   =				default_json_config_file

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :List[str]							)     ->    Union[str, Any]:
   if config_file is not None:
      if not os.path.isfile(_snake_case							):
         raise FileNotFoundError(
             F'''The passed configuration file `{config_file}` does not exist. '''
             '''Please pass an existing file to `accelerate launch`, or use the the default one '''
             '''created through `accelerate config` and run `accelerate launch` '''
             '''without the `--config_file` argument.'''							)
   else:
      _A						     =					default_config_file
   with open(_snake_case  ,      '''r'''  ,      encoding='''utf-8'''							) as f:
      if config_file.endswith('''.json'''							):
         if (
             json.load(_snake_case							).get('''compute_environment'''  ,      ComputeEnvironment.LOCAL_MACHINE							)
             == ComputeEnvironment.LOCAL_MACHINE
         ):
            _A						     =					ClusterConfig
         else:
            _A						     =					SageMakerConfig
         return config_class.from_json_file(json_file=_snake_case							)
      else:
         if (
             yaml.safe_load(_snake_case							).get('''compute_environment'''  ,      ComputeEnvironment.LOCAL_MACHINE							)
             == ComputeEnvironment.LOCAL_MACHINE
         ):
            _A						     =					ClusterConfig
         else:
            _A						     =					SageMakerConfig
         return config_class.from_yaml_file(yaml_file=_snake_case							)


@dataclass
class   lowerCamelCase__  :



 """simple docstring"""

 a__				:   ComputeEnvironment
 a__				:   Union[DistributedType, SageMakerDistributedType]
 a__				:   str
 a__				:   bool

 def 						snake_case_			(						self				:				List[str]			)      ->  Any:
    _A						     =					self.__dict__
    # For serialization, it's best to convert Enums to strings (or their underlying value type).
    for key, value in result.items():
       if isinstance(__lowerCAmelCase ,	__lowerCAmelCase			):
          _A						     =					value.value
       if isinstance(__lowerCAmelCase ,	__lowerCAmelCase			) and not bool(__lowerCAmelCase			):
          _A						     =					None
    _A						     =					{k: v for k, v in result.items() if v is not None}
    return result

 @classmethod
 def 						snake_case_			(						cls				:				List[str] ,	__lowerCAmelCase				:				int=None			)      ->  List[str]:
    _A						     =					default_json_config_file if json_file is None else json_file
    with open(__lowerCAmelCase ,	'''r''' ,	encoding='''utf-8'''			) as f:
       _A						     =					json.load(__lowerCAmelCase			)
    if "compute_environment" not in config_dict:
       _A						     =					ComputeEnvironment.LOCAL_MACHINE
    if "mixed_precision" not in config_dict:
       _A						     =					'''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
    if "fp16" in config_dict:  # Convert the config to the new format.
       del config_dict["fp16"]
    if "dynamo_backend" in config_dict:  # Convert the config to the new format.
       _A						     =					config_dict.pop('''dynamo_backend'''			)
       _A						     =					{} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
    if "use_cpu" not in config_dict:
       _A						     =					False
    return cls(**__lowerCAmelCase			)

 def 						snake_case_			(						self				:				Any ,	__lowerCAmelCase				:				List[str]			)      ->  str:
    with open(__lowerCAmelCase ,	'''w''' ,	encoding='''utf-8'''			) as f:
       _A						     =					json.dumps(self.to_dict() ,	indent=2 ,	sort_keys=__lowerCAmelCase			) + '''\n'''
       f.write(__lowerCAmelCase			)

 @classmethod
 def 						snake_case_			(						cls				:				int ,	__lowerCAmelCase				:				str=None			)      ->  Any:
    _A						     =					default_yaml_config_file if yaml_file is None else yaml_file
    with open(__lowerCAmelCase ,	'''r''' ,	encoding='''utf-8'''			) as f:
       _A						     =					yaml.safe_load(__lowerCAmelCase			)
    if "compute_environment" not in config_dict:
       _A						     =					ComputeEnvironment.LOCAL_MACHINE

    if "mixed_precision" not in config_dict:
       _A						     =					'''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
    if isinstance(config_dict['''mixed_precision'''] ,	__lowerCAmelCase			) and not config_dict["mixed_precision"]:
       _A						     =					'''no'''
    if "fp16" in config_dict:  # Convert the config to the new format.
       del config_dict["fp16"]
    if "dynamo_backend" in config_dict:  # Convert the config to the new format.
       _A						     =					config_dict.pop('''dynamo_backend'''			)
       _A						     =					{} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
    if "use_cpu" not in config_dict:
       _A						     =					False
    return cls(**__lowerCAmelCase			)

 def 						snake_case_			(						self				:				Union[str, Any] ,	__lowerCAmelCase				:				str			)      ->  int:
    with open(__lowerCAmelCase ,	'''w''' ,	encoding='''utf-8'''			) as f:
       yaml.safe_dump(self.to_dict() ,	__lowerCAmelCase			)

 def 						snake_case_			(						self				:				Optional[int]			)      ->  Dict:
    if isinstance(self.compute_environment ,	__lowerCAmelCase			):
       _A						     =					ComputeEnvironment(self.compute_environment			)
    if isinstance(self.distributed_type ,	__lowerCAmelCase			):
       if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
          _A						     =					SageMakerDistributedType(self.distributed_type			)
       else:
          _A						     =					DistributedType(self.distributed_type			)
    if self.dynamo_config is None:
       _A						     =					{}


@dataclass
class   lowerCamelCase__  (					_A):



 """simple docstring"""

 a__				:   int
 a__				:   int   =     0
 a__				:   int   =     1
 a__				:   Optional[str]   =     None
 a__				:   Optional[str]   =     None
 a__				:   Optional[int]   =     None
 a__				:   Optional[str]   =     "static"
 a__				:   Optional[bool]   =     False
 a__				:   str   =     "main"

 # args for deepspeed_plugin
 a__				:   dict   =     None
 # args for fsdp
 a__				:   dict   =     None
 # args for megatron_lm
 a__				:   dict   =     None
 # args for ipex
 a__				:   dict   =     None
 # args for TPU
 a__				:   bool   =     False

 # args for TPU pods
 a__				:   str   =     None
 a__				:   str   =     None
 a__				:   bool   =     False
 a__				:   bool   =     False
 a__				:   str   =     None
 a__				:   List[str]   =     None
 a__				:   List[str]   =     None
 a__				:   List[str]   =     None

 # args for dynamo
 a__				:   dict   =     None

 def 						snake_case_			(						self				:				str			)      ->  Any:
    if self.deepspeed_config is None:
       _A						     =					{}
    if self.fsdp_config is None:
       _A						     =					{}
    if self.megatron_lm_config is None:
       _A						     =					{}
    if self.ipex_config is None:
       _A						     =					{}
    return super().__post_init__()


@dataclass
class   lowerCamelCase__  (					_A):



 """simple docstring"""

 a__				:   str
 a__				:   str
 a__				:   Optional[str]   =     None
 a__				:   Optional[str]   =     None
 a__				:   str   =     "us-east-1"
 a__				:   int   =     1
 a__				:   str   =     "all"
 a__				:   str   =     f'''accelerate-sagemaker-{num_machines}'''
 a__				:   str   =     SAGEMAKER_PYTORCH_VERSION
 a__				:   str   =     SAGEMAKER_TRANSFORMERS_VERSION
 a__				:   str   =     SAGEMAKER_PYTHON_VERSION
 a__				:   str   =     None
 a__				:   str   =     None
 a__				:   dict   =     None
 a__				:   dict   =     None



