





# coding=utf-8
# Copyright 2023 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 platform
import sys


__lowercase  :   int						      ="""3"""

print("""Python version:""", sys.version)

print("""OS platform:""", platform.platform())
print("""OS architecture:""", platform.machine())

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())
except ImportError:
     print("""Torch version:""", None)

try:
     import transformers

     print("""transformers version:""", transformers.__version__)
except ImportError:
     print("""transformers version:""", None)











__lowercase  :   Dict						      ="""0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
     from .utils import rich




