





# 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


__UpperCAmelCase       :   Union[str, Any]				       =							"Create a default config file for Accelerate with only a few flags set."







def 			A__							(    SCREAMING_SNAKE_CASE__="no"					,					SCREAMING_SNAKE_CASE__ = default_json_config_file					,					SCREAMING_SNAKE_CASE__ = False)   ->    Optional[Any]:
  __snake_case:						Any   =						Path(SCREAMING_SNAKE_CASE__)
  path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__					,					exist_ok=SCREAMING_SNAKE_CASE__)
  if path.exists():
    print(
        F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.''')
    return False
  __snake_case:						Any   =						mixed_precision.lower()
  if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
    raise ValueError(
        F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}''')
  __snake_case:						Optional[int]   =						{
      """compute_environment""": """LOCAL_MACHINE""",
      """mixed_precision""": mixed_precision,
  }
  if torch.cuda.is_available():
    __snake_case:						str   =						torch.cuda.device_count()
    __snake_case:						Tuple   =						num_gpus
    __snake_case:						Tuple   =						False
    if num_gpus > 1:
      __snake_case:						str   =						"""MULTI_GPU"""
    else:
      __snake_case:						Any   =						"""NO"""
  elif is_xpu_available() and use_xpu:
    __snake_case:						Dict   =						torch.xpu.device_count()
    __snake_case:						List[Any]   =						num_xpus
    __snake_case:						List[Any]   =						False
    if num_xpus > 1:
      __snake_case:						List[Any]   =						"""MULTI_XPU"""
    else:
      __snake_case:						List[Any]   =						"""NO"""
  elif is_npu_available():
    __snake_case:						Optional[Any]   =						torch.npu.device_count()
    __snake_case:						List[str]   =						num_npus
    __snake_case:						Any   =						False
    if num_npus > 1:
      __snake_case:						int   =						"""MULTI_NPU"""
    else:
      __snake_case:						Tuple   =						"""NO"""
  else:
    __snake_case:						str   =						0
    __snake_case:						List[str]   =						True
    __snake_case:						Tuple   =						1
    __snake_case:						Optional[int]   =						"""NO"""
  __snake_case:						Optional[Any]   =						ClusterConfig(**SCREAMING_SNAKE_CASE__)
  config.to_json_file(SCREAMING_SNAKE_CASE__)
  return path







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    int:
  __snake_case:						str   =						parser.add_parser("""default"""					,					parents=SCREAMING_SNAKE_CASE__					,					help=SCREAMING_SNAKE_CASE__					,					formatter_class=SCREAMING_SNAKE_CASE__)
  parser.add_argument(
      """--config_file"""					,					default=SCREAMING_SNAKE_CASE__					,					help=(
          """The path to use to store the config file. 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'."""
      )					,					dest="""save_location"""					,					)

  parser.add_argument(
      """--mixed_precision"""					,					choices=["""no""", """fp16""", """bf16"""]					,					type=SCREAMING_SNAKE_CASE__					,					help="""Whether or not to use mixed precision training. """
      """Choose between FP16 and BF16 (bfloat16) training. """
      """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."""					,					default="""no"""					,					)
  parser.set_defaults(func=SCREAMING_SNAKE_CASE__)
  return parser







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Union[str, Any]:
  __snake_case:						Optional[Any]   =						write_basic_config(args.mixed_precision					,					args.save_location)
  if config_file:
    print(F'''accelerate configuration saved at {config_file}''')













def 			A__							(    SCREAMING_SNAKE_CASE__ = 200)   ->    int:
  __snake_case:						Optional[int]   =						[1, 2, 5, 10, 20, 50, 100, 200]
  __snake_case:						List[Any]   =						[0] * (pence + 1)
  __snake_case:						int   =						1  # base case: 1 way to make 0 pence

  for coin in coins:
    for i in range(SCREAMING_SNAKE_CASE__					,					pence + 1					,					1):
      number_of_ways[i] += number_of_ways[i - coin]
  return number_of_ways[pence]


if __name__ == "__main__":
      assert solution(200) == 73_682






