


"""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


lowercase_			     =  "Run commands across TPU VMs for initial setup before running `accelerate launch`."

def        lowercase							(		lowerCAmelCase__ :     Optional[Any]=None			) ->      Optional[int]:
		if subparsers is not None:
				__a           =			subparsers.add_parser('''tpu-config'''    ,					description=_description			)
		else:
				__a           =			argparse.ArgumentParser('''Accelerate tpu-config command'''    ,					description=_description			)
		# Core arguments
		__a           =			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.'''    ,					)
		__a           =			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        lowercase							(		lowerCAmelCase__ :     Optional[Any]			) ->      Optional[Any]:
		__a           =			None

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

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

		# Then send it to gcloud
		# Eventually try to use google-api-core to do this instead of subprocess
		__a           =			['''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        lowercase							(		) ->      str:
		__a           =			tpu_command_parser()
		__a           =			parser.parse_args()

		tpu_command_launcher(lowerCAmelCase__			)










"""simple docstring"""




from typing import TYPE_CHECKING

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


lowercase_			     =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["RoFormerTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RoFormerForCausalLM",
				    "RoFormerForMaskedLM",
				    "RoFormerForMultipleChoice",
				    "RoFormerForQuestionAnswering",
				    "RoFormerForSequenceClassification",
				    "RoFormerForTokenClassification",
				    "RoFormerLayer",
				    "RoFormerModel",
				    "RoFormerPreTrainedModel",
				    "load_tf_weights_in_roformer",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRoFormerForCausalLM",
				    "TFRoFormerForMaskedLM",
				    "TFRoFormerForMultipleChoice",
				    "TFRoFormerForQuestionAnswering",
				    "TFRoFormerForSequenceClassification",
				    "TFRoFormerForTokenClassification",
				    "TFRoFormerLayer",
				    "TFRoFormerModel",
				    "TFRoFormerPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "FlaxRoFormerForMaskedLM",
				    "FlaxRoFormerForMultipleChoice",
				    "FlaxRoFormerForQuestionAnswering",
				    "FlaxRoFormerForSequenceClassification",
				    "FlaxRoFormerForTokenClassification",
				    "FlaxRoFormerModel",
				    "FlaxRoFormerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
				from .tokenization_roformer import RoFormerTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_roformer_fast import RoFormerTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roformer import (
								    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RoFormerForCausalLM,
								    RoFormerForMaskedLM,
								    RoFormerForMultipleChoice,
								    RoFormerForQuestionAnswering,
								    RoFormerForSequenceClassification,
								    RoFormerForTokenClassification,
								    RoFormerLayer,
								    RoFormerModel,
								    RoFormerPreTrainedModel,
								    load_tf_weights_in_roformer,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roformer import (
								    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRoFormerForCausalLM,
								    TFRoFormerForMaskedLM,
								    TFRoFormerForMultipleChoice,
								    TFRoFormerForQuestionAnswering,
								    TFRoFormerForSequenceClassification,
								    TFRoFormerForTokenClassification,
								    TFRoFormerLayer,
								    TFRoFormerModel,
								    TFRoFormerPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roformer import (
								    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlaxRoFormerForMaskedLM,
								    FlaxRoFormerForMultipleChoice,
								    FlaxRoFormerForQuestionAnswering,
								    FlaxRoFormerForSequenceClassification,
								    FlaxRoFormerForTokenClassification,
								    FlaxRoFormerModel,
								    FlaxRoFormerPreTrainedModel,
								)


else:
				import sys

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






