

'''simple docstring'''



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					:			List[str]       =	'Create a default config file for Accelerate with only a few flags set.'







def   a__			(    a__="no" ,       a__ = default_json_config_file ,       a__ = False		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = 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
     __SCREAMING_SNAKE_CASE      = 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}'		)
     __SCREAMING_SNAKE_CASE      = {
         'compute_environment': 'LOCAL_MACHINE',
         'mixed_precision': mixed_precision,
     }
     if torch.cuda.is_available():
          __SCREAMING_SNAKE_CASE      = torch.cuda.device_count()
          __SCREAMING_SNAKE_CASE      = num_gpus
          __SCREAMING_SNAKE_CASE      = False
          if num_gpus > 1:
               __SCREAMING_SNAKE_CASE      = 'MULTI_GPU'
          else:
               __SCREAMING_SNAKE_CASE      = 'NO'
     elif is_xpu_available() and use_xpu:
          __SCREAMING_SNAKE_CASE      = torch.xpu.device_count()
          __SCREAMING_SNAKE_CASE      = num_xpus
          __SCREAMING_SNAKE_CASE      = False
          if num_xpus > 1:
               __SCREAMING_SNAKE_CASE      = 'MULTI_XPU'
          else:
               __SCREAMING_SNAKE_CASE      = 'NO'
     elif is_npu_available():
          __SCREAMING_SNAKE_CASE      = torch.npu.device_count()
          __SCREAMING_SNAKE_CASE      = num_npus
          __SCREAMING_SNAKE_CASE      = False
          if num_npus > 1:
               __SCREAMING_SNAKE_CASE      = 'MULTI_NPU'
          else:
               __SCREAMING_SNAKE_CASE      = 'NO'
     else:
          __SCREAMING_SNAKE_CASE      = 0
          __SCREAMING_SNAKE_CASE      = True
          __SCREAMING_SNAKE_CASE      = 1
          __SCREAMING_SNAKE_CASE      = 'NO'
     __SCREAMING_SNAKE_CASE      = ClusterConfig(**_SCREAMING_SNAKE_CASE		)
     config.to_json_file(_SCREAMING_SNAKE_CASE		)
     return path







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = 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__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = write_basic_config(args.mixed_precision ,       args.save_location		)
     if config_file:
          print(F'accelerate configuration saved at {config_file}'		)













"""simple docstring"""





# Copyright 2023 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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A											=						{
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						[
      """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MgpstrModel""",
      """MgpstrPreTrainedModel""",
      """MgpstrForSceneTextRecognition""",
  ]

if TYPE_CHECKING:
  from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
  from .processing_mgp_str import MgpstrProcessor
  from .tokenization_mgp_str import MgpstrTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mgp_str import (
        MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
        MgpstrForSceneTextRecognition,
        MgpstrModel,
        MgpstrPreTrainedModel,
    )
else:
  import sys

  __A											=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
