
'''simple docstring'''
# 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 argparse
import os

from accelerate.test_utils import execute_subprocess_async

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE=None       ):
    """simple docstring"""






    if subparsers is not None:
        snake_case_ :    Tuple     			=						subparsers.add_parser("test"       )
    else:
        snake_case_ :    List[str]     			=						argparse.ArgumentParser("Accelerate test command"       )

    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'."
        ),		)

    if subparsers is not None:
        parser.set_defaults(func=__SCREAMING_SNAKE_CASE       )
    return parser

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Tuple     			=						os.path.sep.join(__file__.split(os.path.sep       )[:-2] + ["test_utils", "scripts", "test_script.py"]       )

    if args.config_file is None:
        snake_case_ :    Union[str, Any]     			=						script_name
    else:
        snake_case_ :    Tuple     			=						f'--config_file={args.config_file} {script_name}'

    snake_case_ :    Optional[Any]     			=						["accelerate-launch"] + test_args.split()
    snake_case_ :    Union[str, Any]     			=						execute_subprocess_async(__SCREAMING_SNAKE_CASE,		env=os.environ.copy()       )
    if result.returncode == 0:
        print("Test is a success! You are ready for your distributed training!"       )

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    Optional[int]     			=						test_command_parser()
    snake_case_ :    Tuple     			=						parser.parse_args()
    test_command(__SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
  main()






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_											=						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BioGptForCausalLM",
      "BioGptForTokenClassification",
      "BioGptForSequenceClassification",
      "BioGptModel",
      "BioGptPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

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




