

'''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)









'''simple docstring'''

import random





def    A_  ( snake_case      ,       snake_case      ,       snake_case = False ):
  SCREAMING_SNAKE_CASE:dict						      =					{i: [] for i in range(snake_case )}

  # if probability is greater or equal than 1, then generate a complete graph
  if probability >= 1:
    return complete_graph(snake_case )
  # if probability is lower or equal than 0, then return a graph without edges
  if probability <= 0:
    return graph

  # for each couple of nodes, add an edge from u to v
  # if the number randomly generated is greater than probability probability
  for i in range(snake_case ):
    for j in range(i + 1      ,       snake_case ):
      if random.random() < probability:
        graph[i].append(snake_case )
        if not directed:
          # if the graph is undirected, add an edge in from j to i, either
          graph[j].append(snake_case )
  return graph





def    A_  ( snake_case ):
  return {
      i: [j for j in range(snake_case ) if i != j] for i in range(snake_case )
  }


if __name__ == "__main__":
    import doctest

    doctest.testmod()






