






'''simple docstring'''







# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import sys

import transformers


_A	:			List[str]         ='''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
			import torch

			print('''Torch version:''', torch.__version__)
			print('''Cuda available:''', torch.cuda.is_available())
			print('''Cuda version:''', torch.version.cuda)
			print('''CuDNN version:''', torch.backends.cudnn.version())
			print('''Number of GPUs available:''', torch.cuda.device_count())
			print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
			print('''Torch version:''', None)

try:
			import deepspeed

			print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
			print('''DeepSpeed version:''', None)

try:
			import tensorflow as tf

			print('''TensorFlow version:''', tf.__version__)
			print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
			print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
			print('''TensorFlow version:''', None)









'''simple docstring'''







from typing import TYPE_CHECKING

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


_A	:			Union[str, Any]         ={
    '''configuration_swiftformer''': [
        '''SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwiftFormerConfig''',
        '''SwiftFormerOnnxConfig''',
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			Optional[Any]         =[
			    '''SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''SwiftFormerForImageClassification''',
			    '''SwiftFormerModel''',
			    '''SwiftFormerPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_swiftformer import (
			    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SwiftFormerConfig,
			    SwiftFormerOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swiftformer import (
						    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwiftFormerForImageClassification,
						    SwiftFormerModel,
						    SwiftFormerPreTrainedModel,
						)

else:
			import sys

			_A	:			Tuple         =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

