

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available





def  __lowerCamelCase     (			*UpperCamelCase__				):







       '''simple docstring'''






       if not isinstance(UpperCamelCase__       ,							UpperCamelCase__				):
              snake_case_          =   list(UpperCamelCase__				)
       for i in range(len(UpperCamelCase__				)				):
              snake_case_          =   None
       gc.collect()
       if is_xpu_available():
              torch.xpu.empty_cache()
       elif is_npu_available():
              torch.npu.empty_cache()
       else:
              torch.cuda.empty_cache()
       return objects





def  __lowerCamelCase     (			UpperCamelCase__				):







       '''simple docstring'''






       snake_case_          =   [
           'CUDA out of memory.',  # CUDA OOM
           'cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.',  # CUDNN SNAFU
           'DefaultCPUAllocator: can\'t allocate memory',  # CPU OOM
       ]
       if isinstance(UpperCamelCase__       ,							UpperCamelCase__				) and len(exception.args				) == 1:
              return any(err in exception.args[0] for err in _statements				)
       return False





def  __lowerCamelCase     (			UpperCamelCase__ = None       ,							UpperCamelCase__ = 128				):







       '''simple docstring'''






       if function is None:
              return functools.partial(UpperCamelCase__       ,							starting_batch_size=UpperCamelCase__				)

       snake_case_          =   starting_batch_size

       def decorator(*UpperCamelCase__       ,							**UpperCamelCase__				):
              nonlocal batch_size
              gc.collect()
              if is_xpu_available():
                     torch.xpu.empty_cache()
              elif is_npu_available():
                     torch.npu.empty_cache()
              else:
                     torch.cuda.empty_cache()
              snake_case_          =   list(inspect.signature(UpperCamelCase__				).parameters.keys()				)
              # Guard against user error
              if len(UpperCamelCase__				) < (len(UpperCamelCase__				) + 1):
                     snake_case_          =   ', '.join([F'''{arg}={value}''' for arg, value in zip(params[1:]       ,							args[1:]				)]				)
                     raise TypeError(
                         F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
                         F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`'''				)
              while True:
                     if batch_size == 0:
                            raise RuntimeError('No executable batch size found, reached zero.'				)
                     try:
                            return function(UpperCamelCase__       ,							*UpperCamelCase__       ,							**UpperCamelCase__				)
                     except Exception as e:
                            if should_reduce_batch_size(UpperCamelCase__				):
                                   gc.collect()
                                   if is_xpu_available():
                                          torch.xpu.empty_cache()
                                   elif is_npu_available():
                                          torch.npu.empty_cache()
                                   else:
                                          torch.cuda.empty_cache()
                                   batch_size //= 2
                            else:
                                   raise

       return decorator







from typing import TYPE_CHECKING

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


_UpperCAmelCase		:       Dict						    =	{
    """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:
					_UpperCAmelCase		:       Optional[int]						    =	["""BlipImageProcessor"""]


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

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:       Any						    =	[
					    """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

					_UpperCAmelCase		:       Optional[int]						    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




