






# 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


lowerCamelCase : str						   =       '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''
def       snake_case_		(     lowerCAmelCase_       :						Optional[Any]=None      ):
							if subparsers is not None:
														__lowercase      :							Any				=     subparsers.add_parser("""tpu-config"""       ,       description=_description      )
							else:
														__lowercase      :							Dict				=     argparse.ArgumentParser("""Accelerate tpu-config command"""       ,       description=_description      )
							# Core arguments
							__lowercase      :							List[Any]				=     parser.add_argument_group(
							    """Config Arguments"""       ,       """Arguments that can be configured through `accelerate config`."""      )
							config_args.add_argument(
							    """--config_file"""       ,       type=lowerCAmelCase_       ,       default=lowerCAmelCase_       ,       help="""Path to the config file to use for accelerate."""       ,       )
							config_args.add_argument(
							    """--tpu_name"""       ,       default=lowerCAmelCase_       ,       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=lowerCAmelCase_       ,       help="""The zone of the TPU to use. If not specified, will use the zone specified in the config file."""       ,       )
							__lowercase      :							int				=     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=lowerCAmelCase_       ,       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=lowerCAmelCase_      )
							return parser
def       snake_case_		(     lowerCAmelCase_       :						Any      ):
							__lowercase      :							int				=     None

							# Get the default from the config file if it exists.
							if args.config_file is not None or os.path.isfile(lowerCAmelCase_      ):
														__lowercase      :							Any				=     load_config_from_file(args.config_file      )
														if not args.command_file and defaults.command_file is not None and not args.command:
																					__lowercase      :							int				=     defaults.command_file
														if not args.command and defaults.commands is not None:
																					__lowercase      :							Dict				=     defaults.commands
														if not args.tpu_name:
																					__lowercase      :							Optional[int]				=     defaults.tpu_name
														if not args.tpu_zone:
																					__lowercase      :							Union[str, Any]				=     defaults.tpu_zone
							if args.accelerate_version == "dev":
														__lowercase      :							List[Any]				=     """git+https://github.com/huggingface/accelerate.git"""
							elif args.accelerate_version == "latest":
														__lowercase      :							Dict				=     """accelerate -U"""
							elif isinstance(parse(args.accelerate_version      )       ,       lowerCAmelCase_      ):
														__lowercase      :							List[Any]				=     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:
																					__lowercase      :							List[Any]				=     [f.read().splitlines()]

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

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							__lowercase      :							Tuple				=     ["""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(lowerCAmelCase_      )}"      )
														return
							subprocess.run(lowerCAmelCase_      )
							print("""Successfully setup pod."""      )



def       snake_case_		(     ):
							__lowercase      :							Tuple				=     tpu_command_parser()
							__lowercase      :							Tuple				=     parser.parse_args()

							tpu_command_launcher(lowerCAmelCase_      )







from typing import TYPE_CHECKING

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


lowerCamelCase : int						   =       {
    '''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],
    '''convert_funnel_original_tf_checkpoint_to_pytorch''': [],
    '''tokenization_funnel''': ['''FunnelTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : List[str]						   =       ['''FunnelTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : Union[str, Any]						   =       [
	    '''FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''FunnelBaseModel''',
	    '''FunnelForMaskedLM''',
	    '''FunnelForMultipleChoice''',
	    '''FunnelForPreTraining''',
	    '''FunnelForQuestionAnswering''',
	    '''FunnelForSequenceClassification''',
	    '''FunnelForTokenClassification''',
	    '''FunnelModel''',
	    '''FunnelPreTrainedModel''',
	    '''load_tf_weights_in_funnel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : str						   =       [
	    '''TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFFunnelBaseModel''',
	    '''TFFunnelForMaskedLM''',
	    '''TFFunnelForMultipleChoice''',
	    '''TFFunnelForPreTraining''',
	    '''TFFunnelForQuestionAnswering''',
	    '''TFFunnelForSequenceClassification''',
	    '''TFFunnelForTokenClassification''',
	    '''TFFunnelModel''',
	    '''TFFunnelPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
	from .tokenization_funnel import FunnelTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_funnel_fast import FunnelTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_funnel import (
		    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FunnelBaseModel,
		    FunnelForMaskedLM,
		    FunnelForMultipleChoice,
		    FunnelForPreTraining,
		    FunnelForQuestionAnswering,
		    FunnelForSequenceClassification,
		    FunnelForTokenClassification,
		    FunnelModel,
		    FunnelPreTrainedModel,
		    load_tf_weights_in_funnel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_funnel import (
		    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFFunnelBaseModel,
		    TFFunnelForMaskedLM,
		    TFFunnelForMultipleChoice,
		    TFFunnelForPreTraining,
		    TFFunnelForQuestionAnswering,
		    TFFunnelForSequenceClassification,
		    TFFunnelForTokenClassification,
		    TFFunnelModel,
		    TFFunnelPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase : Any						   =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)