

import fcntl
import os
import socket

import torch
import torch.distributed as dist






def 					lowerCamelCase_     (							*UpperCAmelCase_							:     Any	)						->				Tuple:
	with open(UpperCamelCase__							,       'r'	) as fh:
		fcntl.flock(UpperCamelCase__							,       fcntl.LOCK_EX	)
		try:
			print(*UpperCamelCase__	)
		finally:
			fcntl.flock(UpperCamelCase__							,       fcntl.LOCK_UN	)


lowerCAmelCase__								=		int(os.environ["""LOCAL_RANK"""])
torch.cuda.set_device(local_rank)
lowerCAmelCase__								=		torch.device("""cuda""", local_rank)
lowerCAmelCase__								=		socket.gethostname()

lowerCAmelCase__								=		f'[{hostname}-{local_rank}]'

try:
							# test distributed
							dist.init_process_group("""nccl""")
							dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
							dist.barrier()

							# test cuda is available and can allocate memory
							torch.cuda.is_available()
							torch.ones(1).cuda(local_rank)

							# global rank
							lowerCAmelCase__								=		dist.get_rank()
							lowerCAmelCase__								=		dist.get_world_size()

							printflock(f'{gpu} is OK (global rank: {rank}/{world_size})')

							dist.barrier()
							if rank == 0:
														printflock(f'pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}')

except Exception:
							printflock(f'{gpu} is broken')
							raise







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase__								=		{
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		["""BlipImageProcessor"""]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """BlipModel""",
							    """BlipPreTrainedModel""",
							    """BlipForConditionalGeneration""",
							    """BlipForQuestionAnswering""",
							    """BlipVisionModel""",
							    """BlipTextModel""",
							    """BlipForImageTextRetrieval""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFBlipModel""",
							    """TFBlipPreTrainedModel""",
							    """TFBlipForConditionalGeneration""",
							    """TFBlipForQuestionAnswering""",
							    """TFBlipVisionModel""",
							    """TFBlipTextModel""",
							    """TFBlipForImageTextRetrieval""",
							]

if TYPE_CHECKING:
							from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
							from .processing_blip import BlipProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_blip import BlipImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blip import (
														    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlipForConditionalGeneration,
														    BlipForImageTextRetrieval,
														    BlipForQuestionAnswering,
														    BlipModel,
														    BlipPreTrainedModel,
														    BlipTextModel,
														    BlipVisionModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blip import (
														    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFBlipForConditionalGeneration,
														    TFBlipForImageTextRetrieval,
														    TFBlipForQuestionAnswering,
														    TFBlipModel,
														    TFBlipPreTrainedModel,
														    TFBlipTextModel,
														    TFBlipVisionModel,
														)

else:
							import sys

							lowerCAmelCase__								=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




