import os
import sys

import transformers


A   :     Optional[int]       =				"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_tf_available, is_torch_available


A:   Optional[Any]								=		{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TapasForMaskedLM",
          "TapasForQuestionAnswering",
          "TapasForSequenceClassification",
          "TapasModel",
          "TapasPreTrainedModel",
          "load_tf_weights_in_tapas",
      ]
try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[str]								=		[
          "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFTapasForMaskedLM",
          "TFTapasForQuestionAnswering",
          "TFTapasForSequenceClassification",
          "TFTapasModel",
          "TFTapasPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
      from .tokenization_tapas import TapasTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tapas import (
                TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TapasForMaskedLM,
                TapasForQuestionAnswering,
                TapasForSequenceClassification,
                TapasModel,
                TapasPreTrainedModel,
                load_tf_weights_in_tapas,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_tapas import (
                TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTapasForMaskedLM,
                TFTapasForQuestionAnswering,
                TFTapasForSequenceClassification,
                TFTapasModel,
                TFTapasPreTrainedModel,
            )


else:
      import sys

      A:   Optional[Any]								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
