



'''simple docstring'''





from typing import TYPE_CHECKING

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


__a  			=						{"configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig", "BeitOnnxConfig"]}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						["BeitFeatureExtractor"]
       __a  			=						["BeitImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "BEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BeitForImageClassification",
           "BeitForMaskedImageModeling",
           "BeitForSemanticSegmentation",
           "BeitModel",
           "BeitPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "FlaxBeitForImageClassification",
           "FlaxBeitForMaskedImageModeling",
           "FlaxBeitModel",
           "FlaxBeitPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_beit import BeitFeatureExtractor
              from .image_processing_beit import BeitImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_beit import (
                  BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BeitForImageClassification,
                  BeitForMaskedImageModeling,
                  BeitForSemanticSegmentation,
                  BeitModel,
                  BeitPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_beit import (
                  FlaxBeitForImageClassification,
                  FlaxBeitForMaskedImageModeling,
                  FlaxBeitModel,
                  FlaxBeitPreTrainedModel,
              )


else:
       import sys

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





'''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  			=						"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)
