


"""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,
)
__lowercase    =				logging.getLogger(__name__)


def 					lowercase      (    A_	)->				Tuple:
  '''simple docstring'''







  a						:		int											=      git.Repo(search_parent_directories=A_	)
  a						:		Optional[int]											=      {
      "repo_id": str(A_	),
      "repo_sha": str(repo.head.object.hexsha	),
      "repo_branch": str(repo.active_branch	),
  }

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


def 					lowercase      (    A_	)->				Dict:
  '''simple docstring'''







  if params.n_gpu <= 0:
    a						:		List[str]											=      0
    a						:		Union[str, Any]											=      -1
    a						:		Any											=      True
    a						:		Optional[int]											=      False
    return

  assert torch.cuda.is_available()

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

    a						:		Optional[Any]											=      int(os.environ["WORLD_SIZE"]	)
    a						:		int											=      int(os.environ["N_GPU_NODE"]	)
    a						:		Union[str, Any]											=      int(os.environ["RANK"]	)

    # number of nodes / node ID
    a						:		str											=      params.world_size // params.n_gpu_per_node
    a						:		List[Any]											=      params.global_rank // params.n_gpu_per_node
    a						:		Optional[int]											=      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

    a						:		Optional[Any]											=      1
    a						:		Tuple											=      0
    a						:		Union[str, Any]											=      0
    a						:		Optional[int]											=      0
    a						:		Any											=      1
    a						:		Any											=      1
    a						:		Optional[int]											=      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
  a						:		Optional[int]											=      params.node_id == 0 and params.local_rank == 0
  a						:		Optional[int]											=      params.n_nodes > 1

  # summary
  a						:		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 					lowercase      (    A_	)->				int:
  '''simple docstring'''







  np.random.seed(args.seed	)
  torch.manual_seed(args.seed	)
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed	)










"""simple docstring"""







__lowercase    =				frozenset(
    [
        """prompt""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """negative_prompt"""])

__lowercase    =				frozenset([])

__lowercase    =				frozenset(["""image"""])

__lowercase    =				frozenset(
    [
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

__lowercase    =				frozenset(["""image"""])

__lowercase    =				frozenset(
    [
        """prompt""",
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """image""", """negative_prompt"""])

__lowercase    =				frozenset(
    [
        # Text guided image variation with an image mask
        """prompt""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""])

__lowercase    =				frozenset(
    [
        # image variation with an image mask
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

__lowercase    =				frozenset(["""image""", """mask_image"""])

__lowercase    =				frozenset(
    [
        """example_image""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

__lowercase    =				frozenset(["""example_image""", """image""", """mask_image"""])

__lowercase    =				frozenset(["""class_labels"""])

__lowercase    =				frozenset(["""class_labels"""])

__lowercase    =				frozenset(["""batch_size"""])

__lowercase    =				frozenset([])

__lowercase    =				frozenset(["""batch_size"""])

__lowercase    =				frozenset([])

__lowercase    =				frozenset(
    [
        """prompt""",
        """audio_length_in_s""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """negative_prompt"""])
__lowercase    =				frozenset(["""input_tokens"""])

__lowercase    =				frozenset(["""input_tokens"""])






