
'''simple docstring'''

import gc
import threading
import time

import psutil
import torch


class 			_UpperCAmelCase			:

     """simple docstring"""

     def __init__(   self			):

       '''simple docstring'''

       a_   :						str   =				psutil.Process()
       a_   :						List[Any]   =				False


     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

       a_   :						Any   =				-1

       while True:
         a_   :						Optional[int]   =				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			):

       '''simple docstring'''

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






     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

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







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






def 						_snake_case    (   ):
  """simple docstring"""







  a_   :						Union[str, Any]   =				{"""time""": time.time()}

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

  # CPU mem
  a_   :						str   =				psutil.Process().memory_info().rss
  cpu_peak_tracker.start()

  # GPU mem
  for i in range(torch.cuda.device_count() ):
    a_   :						Any   =				torch.cuda.memory_allocated(__A )
  torch.cuda.reset_peak_memory_stats()

  return measures






def 						_snake_case    (   A_							:     Any ):
  """simple docstring"""







  a_   :						Tuple   =				{"""time""": time.time() - start_measures["""time"""]}

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

  # CPU mem
  a_   :						Optional[int]   =				(psutil.Process().memory_info().rss - start_measures["""cpu"""]) / 2**20
  a_   :						Union[str, Any]   =				(cpu_peak_tracker.stop() - start_measures["""cpu"""]) / 2**20

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

  return measures






def 						_snake_case    (   A_							:     Union[str, Any]  ,		A_							:     Any ):
  """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''' )
    a_   :						Union[str, Any]   =				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_tokenizers_available, is_torch_available


_lowerCamelCase      :					Tuple					    =      {
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					List[str]					    =      ['''SqueezeBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Tuple					    =      [
       '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SqueezeBertForMaskedLM''',
       '''SqueezeBertForMultipleChoice''',
       '''SqueezeBertForQuestionAnswering''',
       '''SqueezeBertForSequenceClassification''',
       '''SqueezeBertForTokenClassification''',
       '''SqueezeBertModel''',
       '''SqueezeBertModule''',
       '''SqueezeBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_squeezebert import (
       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SqueezeBertConfig,
       SqueezeBertOnnxConfig,
   )
   from .tokenization_squeezebert import SqueezeBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_squeezebert import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
          SqueezeBertModule,
          SqueezeBertPreTrainedModel,
      )

else:
   import sys

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


