




'''simple docstring'''





# Copyright 2022 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.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


_SCREAMING_SNAKE_CASE							:     Tuple   	=				"""Update an existing config file with the latest defaults while maintaining the old configuration."""


def   UpperCamelCase_(			snake_case							: Tuple  ):

							'''simple docstring'''






							snake_case_			      =   args.config_file
							if config_file is None and Path(__lowerCamelCase  ).exists():
														snake_case_			      =   default_config_file
							elif not Path(__lowerCamelCase  ).exists():
														raise ValueError(f'The passed config file located at {config_file} doesn\'t exist.'  )
							snake_case_			      =   load_config_from_file(__lowerCamelCase  )

							if config_file.endswith(".json"  ):
														config.to_json_file(__lowerCamelCase  )
							else:
														config.to_yaml_file(__lowerCamelCase  )
							return config_file


def   UpperCamelCase_(			snake_case							: Any							,       snake_case							: int  ):

							'''simple docstring'''






							snake_case_			      =   parser.add_parser("update"							,       parents=__lowerCamelCase							,       help=__lowerCamelCase							,       formatter_class=__lowerCamelCase  )
							parser.add_argument(
							    "--config_file"							,       default=__lowerCamelCase							,       help=(
							        "The path to the config file to update. Will default to a file named default_config.yaml in the cache "
							        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
							        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
							        "with 'huggingface'."
							    )							,       )

							parser.set_defaults(func=__lowerCamelCase  )
							return parser




def   UpperCamelCase_(			snake_case							: str  ):

							'''simple docstring'''






							snake_case_			      =   update_config(__lowerCamelCase  )
							print(f'Sucessfully updated the configuration file at {config_file}.'  )










'''simple docstring'''





import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm


def   UpperCamelCase_(			snake_case							: int							,       snake_case							: bool = False  ):

							'''simple docstring'''






							if device_specific:
														seed += AcceleratorState().process_index
							random.seed(snake_case  )
							np.random.seed(snake_case  )
							torch.manual_seed(snake_case  )
							if is_xpu_available():
														torch.xpu.manual_seed_all(snake_case  )
							elif is_npu_available():
														torch.npu.manual_seed_all(snake_case  )
							else:
														torch.cuda.manual_seed_all(snake_case  )
							# ^^ safe to call this function even if cuda is not available
							if is_tpu_available():
														xm.set_rng_state(snake_case  )


def   UpperCamelCase_(			snake_case							: Optional[RNGType] = None							,       snake_case							: Optional[torch.Generator] = None  ):

							'''simple docstring'''






							if rng_type == RNGType.TORCH:
														snake_case_			      =   torch.get_rng_state()
							elif rng_type == RNGType.CUDA:
														snake_case_			      =   torch.cuda.get_rng_state()
							elif rng_type == RNGType.XLA:
														assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
														snake_case_			      =   torch.tensor(xm.get_rng_state()  )
							elif rng_type == RNGType.NPU:
														assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
														snake_case_			      =   torch.npu.get_rng_state()
							elif rng_type == RNGType.XPU:
														assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
														snake_case_			      =   torch.xpu.get_rng_state()
							elif rng_type == RNGType.GENERATOR:
														assert generator is not None, "Need a generator to synchronize its seed."
														snake_case_			      =   generator.get_state()

							# Broadcast the rng state from device 0 to other devices
							snake_case_			      =   AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														snake_case_			      =   rng_state.to(xm.xla_device()  )
														xm.collective_broadcast([rng_state]  )
														xm.mark_step()
														snake_case_			      =   rng_state.cpu()
							elif (
							    state.distributed_type in CUDA_DISTRIBUTED_TYPES
							    or state.distributed_type == DistributedType.MULTI_NPU
							    or state.distributed_type == DistributedType.MULTI_XPU
							):
														snake_case_			      =   rng_state.to(state.device  )
														torch.distributed.broadcast(snake_case							,       0  )
														snake_case_			      =   rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(snake_case							,       0  )

							# Set the broadcast rng state
							if rng_type == RNGType.TORCH:
														torch.set_rng_state(snake_case  )
							elif rng_type == RNGType.CUDA:
														torch.cuda.set_rng_state(snake_case  )
							elif rng_type == RNGType.NPU:
														torch.npu.set_rng_state(snake_case  )
							elif rng_type == RNGType.XPU:
														torch.xpu.set_rng_state(snake_case  )
							elif rng_type == RNGType.XLA:
														xm.set_rng_state(rng_state.item()  )
							elif rng_type == RNGType.GENERATOR:
														generator.set_state(snake_case  )




def   UpperCamelCase_(			snake_case							: List[Union[str, RNGType]]							,       snake_case							: Optional[torch.Generator] = None  ):

							'''simple docstring'''






							for rng_type in rng_types:
														synchronize_rng_state(RNGType(snake_case  )							,       generator=snake_case  )




