






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






def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[RNGType] = None						,	__lowerCamelCase    :  Optional[torch.Generator] = None	):
  # Get the proper rng state
  if rng_type == RNGType.TORCH:
    __UpperCAmelCase							:			Optional[Any]     =						torch.get_rng_state()
  elif rng_type == RNGType.CUDA:
    __UpperCAmelCase							:			Any     =						torch.cuda.get_rng_state()
  elif rng_type == RNGType.XLA:
    assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
    __UpperCAmelCase							:			Any     =						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."
    __UpperCAmelCase							:			Any     =						torch.npu.get_rng_state()
  elif rng_type == RNGType.XPU:
    assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
    __UpperCAmelCase							:			Dict     =						torch.xpu.get_rng_state()
  elif rng_type == RNGType.GENERATOR:
    assert generator is not None, "Need a generator to synchronize its seed."
    __UpperCAmelCase							:			Tuple     =						generator.get_state()

  # Broadcast the rng state from device 0 to other devices
  __UpperCAmelCase							:			Tuple     =						AcceleratorState()
  if state.distributed_type == DistributedType.TPU:
    __UpperCAmelCase							:			Dict     =						rng_state.to(xm.xla_device()	)
    xm.collective_broadcast([rng_state]	)
    xm.mark_step()
    __UpperCAmelCase							:			List[str]     =						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
  ):
    __UpperCAmelCase							:			Dict     =						rng_state.to(state.device	)
    torch.distributed.broadcast(__lowerCamelCase						,	0	)
    __UpperCAmelCase							:			List[str]     =						rng_state.cpu()
  elif state.distributed_type == DistributedType.MULTI_CPU:
    torch.distributed.broadcast(__lowerCamelCase						,	0	)

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







def        lowerCamelCase__   (					__lowerCamelCase    :  List[Union[str, RNGType]]						,	__lowerCamelCase    :  Optional[torch.Generator] = None	):
  for rng_type in rng_types:
    synchronize_rng_state(RNGType(__lowerCamelCase	)						,	generator=__lowerCamelCase	)










# 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


a	: Union[str, Any]      =				"Update an existing config file with the latest defaults while maintaining the old configuration."






def        lowerCamelCase__   (					__lowerCamelCase    :  str	):
  __UpperCAmelCase							:			Optional[Any]     =						args.config_file
  if config_file is None and Path(__lowerCamelCase	).exists():
    __UpperCAmelCase							:			int     =						default_config_file
  elif not Path(__lowerCamelCase	).exists():
    raise ValueError(f"""The passed config file located at {config_file} doesn't exist."""	)
  __UpperCAmelCase							:			Tuple     =						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        lowerCamelCase__   (					__lowerCamelCase    :  Dict						,	__lowerCamelCase    :  List[str]	):
  __UpperCAmelCase							:			List[str]     =						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        lowerCamelCase__   (					__lowerCamelCase    :  Optional[int]	):
  __UpperCAmelCase							:			Optional[Any]     =						update_config(__lowerCamelCase	)
  print(f"""Sucessfully updated the configuration file at {config_file}."""	)


