


"""simple docstring"""






import gc
import threading
import time

import psutil
import torch





class      lowerCamelCase__   :







					"""simple docstring"""


					def __init__(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					psutil.Process()
												__UpperCAmelCase   :						Union[str, Any]      							=					False






					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					-1

												while True:
																			__UpperCAmelCase   :						Tuple      							=					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]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Dict      							=					True
												__UpperCAmelCase   :						Optional[int]      							=					threading.Thread(target=self.peak_monitor  )
												__UpperCAmelCase   :						List[Any]      							=					True
												self.thread.start()






					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






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






UpperCAmelCase    :     Union[str, Any]   						=		PeakCPUMemory()
def      lowerCamelCase       (     )		->       Union[str, Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Tuple      							=					{"time": time.time()}

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

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

							# GPU mem
							for i in range(torch.cuda.device_count()							):
														__UpperCAmelCase   :						Dict      							=					torch.cuda.memory_allocated(_snake_case							)
							torch.cuda.reset_peak_memory_stats()

							return measures
def      lowerCamelCase       (     _UpperCamelCase		:      str							)		->       Optional[Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						int      							=					{"time": time.time() - start_measures["time"]}

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

							# CPU mem
							__UpperCAmelCase   :						Optional[Any]      							=					(psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**2_0
							__UpperCAmelCase   :						str      							=					(cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**2_0

							# GPU mem
							for i in range(torch.cuda.device_count()							):
														__UpperCAmelCase   :						Optional[int]      							=					(torch.cuda.memory_allocated(_snake_case							) - start_measures[str(_snake_case							)]) / 2**2_0
														__UpperCAmelCase   :						Optional[Any]      							=					(torch.cuda.max_memory_allocated(_snake_case							) - start_measures[str(_snake_case							)]) / 2**2_0

							return measures
def      lowerCamelCase       (     _UpperCamelCase		:      str       ,			_UpperCamelCase		:      Dict							)		->       str:

							'''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(_snake_case							)]:.2f}MiB'''							)
														__UpperCAmelCase   :						List[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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case :		Dict							      =  logging.get_logger(__name__)

snake_case :		List[Any]							      =  {
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    'convbert'







			def __init__(				self						,   _a=30_522						,   _a=768						,   _a=12						,   _a=12						,   _a=3_072						,   _a="gelu"						,   _a=0.1						,   _a=0.1						,   _a=512						,   _a=2						,   _a=0.02						,   _a=1e-12						,   _a=1						,   _a=0						,   _a=2						,   _a=768						,   _a=2						,   _a=9						,   _a=1						,   _a=None						,   **_a						,   ):
				super().__init__(
				    pad_token_id=_a						,   bos_token_id=_a						,   eos_token_id=_a						,   **_a						,   )

				__magic_name__		:		Tuple	   =     vocab_size
				__magic_name__		:		List[Any]	   =     hidden_size
				__magic_name__		:		Union[str, Any]	   =     num_hidden_layers
				__magic_name__		:		List[Any]	   =     num_attention_heads
				__magic_name__		:		str	   =     intermediate_size
				__magic_name__		:		Any	   =     hidden_act
				__magic_name__		:		List[Any]	   =     hidden_dropout_prob
				__magic_name__		:		Optional[int]	   =     attention_probs_dropout_prob
				__magic_name__		:		Tuple	   =     max_position_embeddings
				__magic_name__		:		str	   =     type_vocab_size
				__magic_name__		:		List[str]	   =     initializer_range
				__magic_name__		:		Tuple	   =     layer_norm_eps
				__magic_name__		:		List[Any]	   =     embedding_size
				__magic_name__		:		List[Any]	   =     head_ratio
				__magic_name__		:		str	   =     conv_kernel_size
				__magic_name__		:		Dict	   =     num_groups
				__magic_name__		:		str	   =     classifier_dropout







class       _snake_case					(     snake_case				):







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				if self.task == "multiple-choice":
					__magic_name__		:		Dict	   =     {0: "batch", 1: "choice", 2: "sequence"}
				else:
					__magic_name__		:		Dict	   =     {0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]		)
