

"""simple docstring"""




import gc
import threading
import time

import psutil
import torch


class 		_A       :





      def __init__(  self		)   ->					List[Any]:
         lowerCamelCase__						=			psutil.Process()
         lowerCamelCase__						=			False





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			-1

         while True:
            lowerCamelCase__						=			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		)   ->					Union[str, Any]:
         lowerCamelCase__						=			True
         lowerCamelCase__						=			threading.Thread(target=self.peak_monitor		)
         lowerCamelCase__						=			True
         self.thread.start()





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         lowerCamelCase__						=			False
         self.thread.join()
         return self.cpu_memory_peak






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



def 	UpperCAmelCase__   (				)      ->      List[str]:




   """simple docstring"""
   # Time
   lowerCamelCase__						=			{"time": time.time()}

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

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

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

   return measures



def 	UpperCAmelCase__   (				A__   )      ->      List[Any]:




   """simple docstring"""
   # Time
   lowerCamelCase__						=			{"time": time.time() - start_measures["time"]}

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

   # CPU mem
   lowerCamelCase__						=			(psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
   lowerCamelCase__						=			(cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

   # GPU mem
   for i in range(torch.cuda.device_count()   ):
      lowerCamelCase__						=			(torch.cuda.memory_allocated(A__   ) - start_measures[str(A__   )]) / 2**20
      lowerCamelCase__						=			(torch.cuda.max_memory_allocated(A__   ) - start_measures[str(A__   )]) / 2**20

   return measures



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      Dict:




   """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(A__   )]:.2f}MiB'   )
      lowerCamelCase__						=			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


SCREAMING_SNAKE_CASE_     :			List[Any]	  =			{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			[
         '''RagModel''',
         '''RagPreTrainedModel''',
         '''RagSequenceForGeneration''',
         '''RagTokenForGeneration''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			str	  =			[
         '''TFRagModel''',
         '''TFRagPreTrainedModel''',
         '''TFRagSequenceForGeneration''',
         '''TFRagTokenForGeneration''',
     ]


if TYPE_CHECKING:
     from .configuration_rag import RagConfig
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rag import (
              TFRagModel,
              TFRagPreTrainedModel,
              TFRagSequenceForGeneration,
              TFRagTokenForGeneration,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_     :			List[str]	  =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




