






import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
__a:     Any   					=					logging.getLogger(__name__)




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						List[Any]:
       _UpperCAmelCase							=    git.Repo(search_parent_directories=__snake_case )
       _UpperCAmelCase							=    {
           """repo_id""": str(__snake_case ),
           """repo_sha""": str(repo.head.object.hexsha ),
           """repo_branch""": str(repo.active_branch ),
       }

       with open(os.path.join(__snake_case   ,      """git_log.json""" )   ,      """w""" ) as f:
              json.dump(__snake_case   ,      __snake_case   ,      indent=4 )




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Dict:
       if params.n_gpu <= 0:
              _UpperCAmelCase							=    0
              _UpperCAmelCase							=    -1
              _UpperCAmelCase							=    True
              _UpperCAmelCase							=    False
              return

       assert torch.cuda.is_available()

       logger.info("""Initializing GPUs""" )
       if params.n_gpu > 1:
              assert params.local_rank != -1

              _UpperCAmelCase							=    int(os.environ["""WORLD_SIZE"""] )
              _UpperCAmelCase							=    int(os.environ["""N_GPU_NODE"""] )
              _UpperCAmelCase							=    int(os.environ["""RANK"""] )

              # number of nodes / node ID
              _UpperCAmelCase							=    params.world_size // params.n_gpu_per_node
              _UpperCAmelCase							=    params.global_rank // params.n_gpu_per_node
              _UpperCAmelCase							=    True

              assert params.n_nodes == int(os.environ["""N_NODES"""] )
              assert params.node_id == int(os.environ["""NODE_RANK"""] )

       # local job (single GPU)
       else:
              assert params.local_rank == -1

              _UpperCAmelCase							=    1
              _UpperCAmelCase							=    0
              _UpperCAmelCase							=    0
              _UpperCAmelCase							=    0
              _UpperCAmelCase							=    1
              _UpperCAmelCase							=    1
              _UpperCAmelCase							=    False

       # sanity checks
       assert params.n_nodes >= 1
       assert 0 <= params.node_id < params.n_nodes
       assert 0 <= params.local_rank <= params.global_rank < params.world_size
       assert params.world_size == params.n_nodes * params.n_gpu_per_node

       # define whether this is the master process / if we are in multi-node distributed mode
       _UpperCAmelCase							=    params.node_id == 0 and params.local_rank == 0
       _UpperCAmelCase							=    params.n_nodes > 1

       # summary
       _UpperCAmelCase							=    f"""--- Global rank: {params.global_rank} - """
       logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes )
       logger.info(PREFIX + """Node ID        : %i""" % params.node_id )
       logger.info(PREFIX + """Local rank     : %i""" % params.local_rank )
       logger.info(PREFIX + """World size     : %i""" % params.world_size )
       logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node )
       logger.info(PREFIX + """Master         : %s""" % str(params.is_master ) )
       logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node ) )
       logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu ) )
       logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname() )

       # set GPU device
       torch.cuda.set_device(params.local_rank )

       # initialize multi-GPU
       if params.multi_gpu:
              logger.info("""Initializing PyTorch distributed""" )
              torch.distributed.init_process_group(
                  init_method="""env://"""   ,      backend="""nccl"""   ,      )





def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Any:
       np.random.seed(args.seed )
       torch.manual_seed(args.seed )
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed )







__a:     List[Any]   					=					{
    '''Pillow''': '''Pillow''',
    '''accelerate''': '''accelerate>=0.11.0''',
    '''compel''': '''compel==0.1.8''',
    '''black''': '''black~=23.1''',
    '''datasets''': '''datasets''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.13.2''',
    '''requests-mock''': '''requests-mock==1.10.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''invisible-watermark''': '''invisible-watermark''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2''',
    '''jaxlib''': '''jaxlib>=0.1.65''',
    '''Jinja2''': '''Jinja2''',
    '''k-diffusion''': '''k-diffusion>=0.0.12''',
    '''torchsde''': '''torchsde''',
    '''note_seq''': '''note_seq''',
    '''librosa''': '''librosa''',
    '''numpy''': '''numpy''',
    '''omegaconf''': '''omegaconf''',
    '''parameterized''': '''parameterized''',
    '''protobuf''': '''protobuf>=3.20.3,<4''',
    '''pytest''': '''pytest''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''ruff''': '''ruff>=0.0.241''',
    '''safetensors''': '''safetensors''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''scipy''': '''scipy''',
    '''onnx''': '''onnx''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''tensorboard''': '''tensorboard''',
    '''torch''': '''torch>=1.4''',
    '''torchvision''': '''torchvision''',
    '''transformers''': '''transformers>=4.25.1''',
    '''urllib3''': '''urllib3<=2.0.0''',
}