






import gc
import threading
import time

import psutil
import torch
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Union[str, Any]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    psutil.Process()
         _UpperCAmelCase							=    False


  def  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    -1

         while True:
                _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Optional[Any]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    threading.Thread(target=self.peak_monitor							)
         _UpperCAmelCase							=    True
         self.thread.start()





  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    False
         self.thread.join()
         return self.cpu_memory_peak



__a:     Union[str, Any]   					=					PeakCPUMemory()




def       _SCREAMING_SNAKE_CASE (							)				->						List[Any]:
       # Time
       _UpperCAmelCase							=    {"""time""": time.time()}

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

       # CPU mem
       _UpperCAmelCase							=    psutil.Process().memory_info().rss
       cpu_peak_tracker.start()

       # GPU mem
       for i in range(torch.cuda.device_count() ):
              _UpperCAmelCase							=    torch.cuda.memory_allocated(__snake_case )
       torch.cuda.reset_peak_memory_stats()

       return measures




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Union[str, Any]:
       # Time
       _UpperCAmelCase							=    {"""time""": time.time() - start_measures["""time"""]}

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

       # CPU mem
       _UpperCAmelCase							=    (psutil.Process().memory_info().rss - start_measures["""cpu"""]) / 2**2_0
       _UpperCAmelCase							=    (cpu_peak_tracker.stop() - start_measures["""cpu"""]) / 2**2_0

       # GPU mem
       for i in range(torch.cuda.device_count() ):
              _UpperCAmelCase							=    (torch.cuda.memory_allocated(__snake_case ) - start_measures[str(__snake_case )]) / 2**2_0
              _UpperCAmelCase							=    (torch.cuda.max_memory_allocated(__snake_case ) - start_measures[str(__snake_case )]) / 2**2_0

       return measures





def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						str:
       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(__snake_case )]:.2f}MiB""" )
              _UpperCAmelCase							=    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""" )


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_      =     {
    'configuration_instructblip': [
        'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InstructBlipConfig',
        'InstructBlipQFormerConfig',
        'InstructBlipVisionConfig',
    ],
    'processing_instructblip': ['InstructBlipProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'InstructBlipQFormerModel',
			    'InstructBlipPreTrainedModel',
			    'InstructBlipForConditionalGeneration',
			    'InstructBlipVisionModel',
			]

if TYPE_CHECKING:
			from .configuration_instructblip import (
			    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    InstructBlipConfig,
			    InstructBlipQFormerConfig,
			    InstructBlipVisionConfig,
			)
			from .processing_instructblip import InstructBlipProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    InstructBlipForConditionalGeneration,
						    InstructBlipPreTrainedModel,
						    InstructBlipQFormerModel,
						    InstructBlipVisionModel,
						)

else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)