




"""simple docstring"""






import gc
import threading
import time

import psutil
import torch






class lowerCAmelCase__	:






			'''simple docstring'''



			def __init__(  self  ):
							_lowerCamelCase     :			Tuple    =    psutil.Process()
							_lowerCamelCase     :			Optional[int]    =    False
			def 					A_    (  self  ):
							_lowerCamelCase     :			Tuple    =    -1

							while True:
											_lowerCamelCase     :			List[Any]    =    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 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    True
							_lowerCamelCase     :			Union[str, Any]    =    threading.Thread(target=self.peak_monitor  )
							_lowerCamelCase     :			Optional[Any]    =    True
							self.thread.start()






			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    False
							self.thread.join()
							return self.cpu_memory_peak

lowercase__     						=						PeakCPUMemory()







def      _snake_case			(				):
				# Time
				_lowerCamelCase     :			Dict    =    {'time': time.time()}

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

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

				# GPU mem
				for i in range(torch.cuda.device_count()		):
								_lowerCamelCase     :			List[Any]    =    torch.cuda.memory_allocated(lowercase__		)
				torch.cuda.reset_peak_memory_stats()

				return measures







def      _snake_case			(				lowercase__		):
				# Time
				_lowerCamelCase     :			Any    =    {'time': time.time() - start_measures['time']}

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

				# CPU mem
				_lowerCamelCase     :			Dict    =    (psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
				_lowerCamelCase     :			str    =    (cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

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

				return measures







def      _snake_case			(				lowercase__							,    lowercase__		):
				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(lowercase__		)]:.2f}MiB'''		)
								_lowerCamelCase     :			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'''		)







"""simple docstring"""






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


UpperCAmelCase_   : str   			= {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : Dict   			= ["""GPTNeoXTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : List[str]   			= [
      """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTNeoXForCausalLM""",
      """GPTNeoXForQuestionAnswering""",
      """GPTNeoXForSequenceClassification""",
      """GPTNeoXForTokenClassification""",
      """GPTNeoXLayer""",
      """GPTNeoXModel""",
      """GPTNeoXPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neox import (
        GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoXForCausalLM,
        GPTNeoXForQuestionAnswering,
        GPTNeoXForSequenceClassification,
        GPTNeoXForTokenClassification,
        GPTNeoXLayer,
        GPTNeoXModel,
        GPTNeoXPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_   : Tuple   			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


