



import gc
import threading
import time

import psutil
import torch



class   UpperCAmelCase_    :







							def __init__(   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    psutil.Process()
														_lowerCAmelCase     :							Union[str, Any]    =    False







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    -1

														while True:
																					_lowerCAmelCase     :							Dict    =    max(self.process.memory_info().rss,			self.cpu_memory_peak)

																					# can't sleep or will not catch the peak right (this comment is here on purpose)
																					if not self.peak_monitoring:
																												break







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    True
														_lowerCAmelCase     :							List[str]    =    threading.Thread(target=self.peak_monitor)
														_lowerCAmelCase     :							Any    =    True
														self.thread.start()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    False
														self.thread.join()
														return self.cpu_memory_peak



_snake_case		=  PeakCPUMemory()






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							List[str]    =    {"time": time.time()}

							gc.collect()
							torch.cuda.empty_cache()

							# CPU mem
							_lowerCAmelCase     :							Optional[Any]    =    psutil.Process().memory_info().rss
							cpu_peak_tracker.start()

							# GPU mem
							for i in range(torch.cuda.device_count()  ):
														_lowerCAmelCase     :							Union[str, Any]    =    torch.cuda.memory_allocated(SCREAMING_SNAKE_CASE_  )
							torch.cuda.reset_peak_memory_stats()

							return measures






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    {"time": time.time() - start_measures["time"]}

							gc.collect()
							torch.cuda.empty_cache()

							# CPU mem
							_lowerCAmelCase     :							Optional[int]    =    (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
							_lowerCAmelCase     :							Dict    =    (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

							# GPU mem
							for i in range(torch.cuda.device_count()  ):
														_lowerCAmelCase     :							Union[str, Any]    =    (torch.cuda.memory_allocated(SCREAMING_SNAKE_CASE_  ) - start_measures[str(SCREAMING_SNAKE_CASE_  )]) / 2**20
														_lowerCAmelCase     :							int    =    (torch.cuda.max_memory_allocated(SCREAMING_SNAKE_CASE_  ) - start_measures[str(SCREAMING_SNAKE_CASE_  )]) / 2**20

							return measures






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							print(F"{description}:"  )
							print(F"- Time: {measures['time']:.2f}s"  )
							for i in range(torch.cuda.device_count()  ):
														print(F"- GPU {i} allocated: {measures[str(SCREAMING_SNAKE_CASE_  )]:.2f}MiB"  )
														_lowerCAmelCase     :							Dict    =    measures[F"{i}-peak"]
														print(F"- GPU {i} peak: {peak:.2f}MiB"  )
							print(F"- CPU RAM allocated: {measures['cpu']:.2f}MiB"  )
							print(F"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"  )




"""simple docstring"""




from typing import TYPE_CHECKING

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


__UpperCamelCase						=      {
    '''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						=      ['''BlipImageProcessor''']


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      [
	    '''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						=      [
	    '''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						=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


