





'''simple docstring'''


from typing import TYPE_CHECKING

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


a__			:					int       						=						{
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__			:					Dict       						=						['AlbertTokenizer']

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__			:					Optional[Any]       						=						['AlbertTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__			:					Optional[Any]       						=						[
        'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'AlbertForMaskedLM',
        'AlbertForMultipleChoice',
        'AlbertForPreTraining',
        'AlbertForQuestionAnswering',
        'AlbertForSequenceClassification',
        'AlbertForTokenClassification',
        'AlbertModel',
        'AlbertPreTrainedModel',
        'load_tf_weights_in_albert',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__			:					Optional[int]       						=						[
        'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFAlbertForMaskedLM',
        'TFAlbertForMultipleChoice',
        'TFAlbertForPreTraining',
        'TFAlbertForQuestionAnswering',
        'TFAlbertForSequenceClassification',
        'TFAlbertForTokenClassification',
        'TFAlbertMainLayer',
        'TFAlbertModel',
        'TFAlbertPreTrainedModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__			:					Any       						=						[
        'FlaxAlbertForMaskedLM',
        'FlaxAlbertForMultipleChoice',
        'FlaxAlbertForPreTraining',
        'FlaxAlbertForQuestionAnswering',
        'FlaxAlbertForSequenceClassification',
        'FlaxAlbertForTokenClassification',
        'FlaxAlbertModel',
        'FlaxAlbertPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_albert import AlbertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_albert_fast import AlbertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_albert import (
            ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            AlbertForMaskedLM,
            AlbertForMultipleChoice,
            AlbertForPreTraining,
            AlbertForQuestionAnswering,
            AlbertForSequenceClassification,
            AlbertForTokenClassification,
            AlbertModel,
            AlbertPreTrainedModel,
            load_tf_weights_in_albert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_albert import (
            TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFAlbertForMaskedLM,
            TFAlbertForMultipleChoice,
            TFAlbertForPreTraining,
            TFAlbertForQuestionAnswering,
            TFAlbertForSequenceClassification,
            TFAlbertForTokenClassification,
            TFAlbertMainLayer,
            TFAlbertModel,
            TFAlbertPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_albert import (
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForPreTraining,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertModel,
            FlaxAlbertPreTrainedModel,
        )
else:
    import sys

    a__			:					Dict       						=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










# 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     a     (		lowerCamelCase_=None							):






				'''simple docstring'''
				if subparsers is not None:
								lowercase__  					=     subparsers.add_parser('''test'''							)
				else:
								lowercase__  					=     argparse.ArgumentParser('''Accelerate test command'''							)

				parser.add_argument(
				    '''--config_file'''						,  default=lowerCamelCase_						,  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=lowerCamelCase_							)
				return parser






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     os.path.sep.join(__file__.split(os.path.sep							)[:-2] + ['''test_utils''', '''scripts''', '''test_script.py''']							)

				if args.config_file is None:
								lowercase__  					=     script_name
				else:
								lowercase__  					=     F"""--config_file={args.config_file} {script_name}"""

				lowercase__  					=     ['''accelerate-launch'''] + test_args.split()
				lowercase__  					=     execute_subprocess_async(lowerCamelCase_						,  env=os.environ.copy()							)
				if result.returncode == 0:
								print('''Test is a success! You are ready for your distributed training!'''							)






def     a     (		):






				'''simple docstring'''
				lowercase__  					=     test_command_parser()
				lowercase__  					=     parser.parse_args()
				test_command(lowerCamelCase_							)


if __name__ == "__main__":
							main()




