






'''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.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


SCREAMING_SNAKE_CASE__     =       'Run commands across TPU VMs for initial setup before running `accelerate launch`.'







def    lowercase__			(				__UpperCamelCase=None   )->			List[str]:
      if subparsers is not None:
            UpperCamelCase      = subparsers.add_parser("""tpu-config"""		,						description=_description   )
      else:
            UpperCamelCase      = argparse.ArgumentParser("""Accelerate tpu-config command"""		,						description=_description   )
      # Core arguments
      UpperCamelCase      = parser.add_argument_group(
          """Config Arguments"""		,						"""Arguments that can be configured through `accelerate config`."""   )
      config_args.add_argument(
          """--config_file"""		,						type=__UpperCamelCase		,						default=__UpperCamelCase		,						help="""Path to the config file to use for accelerate."""		,						)
      config_args.add_argument(
          """--tpu_name"""		,						default=__UpperCamelCase		,						help="""The name of the TPU to use. If not specified, will use the TPU specified in the config file."""		,						)
      config_args.add_argument(
          """--tpu_zone"""		,						default=__UpperCamelCase		,						help="""The zone of the TPU to use. If not specified, will use the zone specified in the config file."""		,						)
      UpperCamelCase      = parser.add_argument_group("""TPU Arguments"""		,						"""Arguments for options ran inside the TPU."""   )
      pod_args.add_argument(
          """--use_alpha"""		,						action="""store_true"""		,						help="""Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."""		,						)
      pod_args.add_argument(
          """--command_file"""		,						default=__UpperCamelCase		,						help="""The path to the file containing the commands to run on the pod on startup."""		,						)
      pod_args.add_argument(
          """--command"""		,						action="""append"""		,						nargs="""+"""		,						help="""A command to run on the pod. Can be passed multiple times."""		,						)
      pod_args.add_argument(
          """--install_accelerate"""		,						action="""store_true"""		,						help="""Whether to install accelerate on the pod. Defaults to False."""		,						)
      pod_args.add_argument(
          """--accelerate_version"""		,						default="""latest"""		,						help="""The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."""		,						)
      pod_args.add_argument(
          """--debug"""		,						action="""store_true"""		,						help="""If set, will print the command that would be run instead of running it."""   )

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







def    lowercase__			(				__UpperCamelCase   )->			List[Any]:
      UpperCamelCase      = None

      # Get the default from the config file if it exists.
      if args.config_file is not None or os.path.isfile(__UpperCamelCase   ):
            UpperCamelCase      = load_config_from_file(args.config_file   )
            if not args.command_file and defaults.command_file is not None and not args.command:
                  UpperCamelCase      = defaults.command_file
            if not args.command and defaults.commands is not None:
                  UpperCamelCase      = defaults.commands
            if not args.tpu_name:
                  UpperCamelCase      = defaults.tpu_name
            if not args.tpu_zone:
                  UpperCamelCase      = defaults.tpu_zone
      if args.accelerate_version == "dev":
            UpperCamelCase      = """git+https://github.com/huggingface/accelerate.git"""
      elif args.accelerate_version == "latest":
            UpperCamelCase      = """accelerate -U"""
      elif isinstance(parse(args.accelerate_version   )		,						__UpperCamelCase   ):
            UpperCamelCase      = F"accelerate=={args.accelerate_version}"

      if not args.command_file and not args.command:
            raise ValueError("""You must specify either a command file or a command to run on the pod."""   )

      if args.command_file:
            with open(args.command_file		,						"""r"""   ) as f:
                  UpperCamelCase      = [f.read().splitlines()]

    # To turn list of lists into list of strings
      if isinstance(args.command[0]		,						__UpperCamelCase   ):
            UpperCamelCase      = [line for cmd in args.command for line in cmd]
      # Default to the shared folder and install accelerate
      UpperCamelCase      = ["""cd /usr/share"""]
      if args.install_accelerate:
            new_cmd += [F"pip install {args.accelerate_version}"]
      new_cmd += args.command
      UpperCamelCase      = """; """.join(__UpperCamelCase   )

      # Then send it to gcloud
      # Eventually try to use google-api-core to do this instead of subprocess
      UpperCamelCase      = ["""gcloud"""]
      if args.use_alpha:
            cmd += ["alpha"]
      cmd += [
          "compute",
          "tpus",
          "tpu-vm",
          "ssh",
          args.tpu_name,
          "--zone",
          args.tpu_zone,
          "--command",
          args.command,
          "--worker",
          "all",
      ]
      if args.debug:
            print(F"Running {' '.join(__UpperCamelCase   )}"   )
            return
      subprocess.run(__UpperCamelCase   )
      print("""Successfully setup pod."""   )







def    lowercase__			(				)->			Any:
      UpperCamelCase      = tpu_command_parser()
      UpperCamelCase      = parser.parse_args()

      tpu_command_launcher(__UpperCamelCase   )








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__     =       {
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       ['MobileBertTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MobileBertForMaskedLM',
         'MobileBertForMultipleChoice',
         'MobileBertForNextSentencePrediction',
         'MobileBertForPreTraining',
         'MobileBertForQuestionAnswering',
         'MobileBertForSequenceClassification',
         'MobileBertForTokenClassification',
         'MobileBertLayer',
         'MobileBertModel',
         'MobileBertPreTrainedModel',
         'load_tf_weights_in_mobilebert',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFMobileBertForMaskedLM',
         'TFMobileBertForMultipleChoice',
         'TFMobileBertForNextSentencePrediction',
         'TFMobileBertForPreTraining',
         'TFMobileBertForQuestionAnswering',
         'TFMobileBertForSequenceClassification',
         'TFMobileBertForTokenClassification',
         'TFMobileBertMainLayer',
         'TFMobileBertModel',
         'TFMobileBertPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mobilebert import (
         MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileBertConfig,
         MobileBertOnnxConfig,
     )
     from .tokenization_mobilebert import MobileBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mobilebert_fast import MobileBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilebert import (
              MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileBertForMaskedLM,
              MobileBertForMultipleChoice,
              MobileBertForNextSentencePrediction,
              MobileBertForPreTraining,
              MobileBertForQuestionAnswering,
              MobileBertForSequenceClassification,
              MobileBertForTokenClassification,
              MobileBertLayer,
              MobileBertModel,
              MobileBertPreTrainedModel,
              load_tf_weights_in_mobilebert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilebert import (
              TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileBertForMaskedLM,
              TFMobileBertForMultipleChoice,
              TFMobileBertForNextSentencePrediction,
              TFMobileBertForPreTraining,
              TFMobileBertForQuestionAnswering,
              TFMobileBertForSequenceClassification,
              TFMobileBertForTokenClassification,
              TFMobileBertMainLayer,
              TFMobileBertModel,
              TFMobileBertPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE__     =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
