import gc
import threading
import time

import psutil
import torch





class UpperCAmelCase						:


				'''simple docstring'''






				def __init__(						self ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				psutil.Process()
						A_		:    Optional[Any]         =				False
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				-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_		:    Dict         =				True
						A_		:    Tuple         =				threading.Thread(target=self.peak_monitor )
						A_		:    Union[str, Any]         =				True
						self.thread.start()







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    int         =				False
						self.thread.join()
						return self.cpu_memory_peak

_UpperCAmelCase            =		PeakCPUMemory()




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Any         =				{'time': time.time()}

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

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

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

		return measures




def     UpperCamelCase						(	__lowercase						:			Optional[int]     ):
		'''simple docstring'''




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

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

		# CPU mem
		A_		:    Tuple         =				(psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
		A_		:    Optional[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(__lowercase     ) - start_measures[str(__lowercase     )]) / 2**20
				A_		:    Union[str, Any]         =				(torch.cuda.max_memory_allocated(__lowercase     ) - start_measures[str(__lowercase     )]) / 2**20

		return measures




def     UpperCamelCase						(	__lowercase						:			Union[str, Any]						,__lowercase						:			int     ):
		'''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(__lowercase     )]:.2f}MiB'''     )
				A_		:    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'''     )

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{
    """SCUT-DLVCLab/lilt-roberta-en-base""": (
        """https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
    ),
}





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = '''lilt'''
				def __init__(						self		,     lowercase=3_0_5_2_2		,     lowercase=7_6_8		,     lowercase=1_2		,     lowercase=1_2		,     lowercase=3_0_7_2		,     lowercase="gelu"		,     lowercase=0.1		,     lowercase=0.1		,     lowercase=5_1_2		,     lowercase=2		,     lowercase=0.02		,     lowercase=1E-12		,     lowercase=0		,     lowercase="absolute"		,     lowercase=None		,     lowercase=4		,     lowercase=1_0_2_4		,     **lowercase		,     ):



						"""simple docstring"""





						super().__init__(pad_token_id=lowercase		,     **lowercase )

						A_		:    str         =				vocab_size
						A_		:    Tuple         =				hidden_size
						A_		:    List[Any]         =				num_hidden_layers
						A_		:    Any         =				num_attention_heads
						A_		:    Union[str, Any]         =				hidden_act
						A_		:    int         =				intermediate_size
						A_		:    int         =				hidden_dropout_prob
						A_		:    Union[str, Any]         =				attention_probs_dropout_prob
						A_		:    List[str]         =				max_position_embeddings
						A_		:    List[str]         =				type_vocab_size
						A_		:    Any         =				initializer_range
						A_		:    str         =				layer_norm_eps
						A_		:    Optional[int]         =				position_embedding_type
						A_		:    Union[str, Any]         =				classifier_dropout
						A_		:    Optional[Any]         =				channel_shrink_ratio
						A_		:    int         =				max_ad_position_embeddings






