





'''simple docstring'''







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,
)
snake_case_     :       Union[str, Any]						=  logging.getLogger(__name__)





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			str   ) ->	int:
						UpperCAmelCase_			:      Optional[Any]     						=  git.Repo(search_parent_directories=UpperCAmelCase__   )
						UpperCAmelCase_			:      List[Any]     						=  {
						    '''repo_id''': str(UpperCAmelCase__   ),
						    '''repo_sha''': str(repo.head.object.hexsha   ),
						    '''repo_branch''': str(repo.active_branch   ),
						}

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





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			str   ) ->	str:
						if params.n_gpu <= 0:
												UpperCAmelCase_			:      int     						=  0
												UpperCAmelCase_			:      Any     						=  -1
												UpperCAmelCase_			:      Optional[int]     						=  True
												UpperCAmelCase_			:      List[str]     						=  False
												return

						assert torch.cuda.is_available()

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

												UpperCAmelCase_			:      List[str]     						=  int(os.environ['''WORLD_SIZE''']   )
												UpperCAmelCase_			:      Any     						=  int(os.environ['''N_GPU_NODE''']   )
												UpperCAmelCase_			:      List[str]     						=  int(os.environ['''RANK''']   )

												# number of nodes / node ID
												UpperCAmelCase_			:      Optional[int]     						=  params.world_size // params.n_gpu_per_node
												UpperCAmelCase_			:      str     						=  params.global_rank // params.n_gpu_per_node
												UpperCAmelCase_			:      Tuple     						=  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_			:      Optional[Any]     						=  1
												UpperCAmelCase_			:      int     						=  0
												UpperCAmelCase_			:      Any     						=  0
												UpperCAmelCase_			:      str     						=  0
												UpperCAmelCase_			:      Optional[Any]     						=  1
												UpperCAmelCase_			:      str     						=  1
												UpperCAmelCase_			:      List[str]     						=  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_			:      str     						=  params.node_id == 0 and params.local_rank == 0
						UpperCAmelCase_			:      Optional[int]     						=  params.n_nodes > 1

						# summary
						UpperCAmelCase_			:      Union[str, Any]     						=  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        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[Any]   ) ->	Tuple:
						np.random.seed(args.seed   )
						torch.manual_seed(args.seed   )
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed   )












'''simple docstring'''







snake_case_     :       int						=  {
    "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",
}





