



"""simple docstring"""





import gc
import threading
import time

import psutil
import torch
class    lowercase__     :







							'''simple docstring'''


							def __init__(							self    :						Any					)				->		List[Any]:


													'''simple docstring'''




													UpperCAmelCase_    				=							psutil.Process()
													UpperCAmelCase_    				=							False



							def   lowercase__       (							self    :						Any					)				->		List[Any]:


													'''simple docstring'''




													UpperCAmelCase_    				=							-1

													while True:
																			UpperCAmelCase_    				=							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   lowercase__       (							self    :						List[str]					)				->		List[str]:


													'''simple docstring'''




													UpperCAmelCase_    				=							True
													UpperCAmelCase_    				=							threading.Thread(target=self.peak_monitor					)
													UpperCAmelCase_    				=							True
													self.thread.start()







							def   lowercase__       (							self    :						int					)				->		Dict:


													'''simple docstring'''




													UpperCAmelCase_    				=							False
													self.thread.join()
													return self.cpu_memory_peak



lowerCamelCase      =		PeakCPUMemory()
def      a__			(   ):
						# Time
						UpperCAmelCase_    				=							{"time": time.time()}

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

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

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

						return measures
def      a__			(   lowerCAmelCase__   ):
						# Time
						UpperCAmelCase_    				=							{"time": time.time() - start_measures["time"]}

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

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

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

						return measures


def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
						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(__SCREAMING_SNAKE_CASE   )]:.2f}MiB"""   )
												UpperCAmelCase_    				=							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 collections import OrderedDict
from typing import Mapping

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


lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    """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    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''convbert'''



       def __init__(							self    :						Any	,       _UpperCAmelCase    :						Optional[int]=30522	,       _UpperCAmelCase    :						Tuple=768	,       _UpperCAmelCase    :						Optional[int]=12	,       _UpperCAmelCase    :						Dict=12	,       _UpperCAmelCase    :						Any=3072	,       _UpperCAmelCase    :						Dict="gelu"	,       _UpperCAmelCase    :						Optional[int]=0.1	,       _UpperCAmelCase    :						str=0.1	,       _UpperCAmelCase    :						Tuple=512	,       _UpperCAmelCase    :						Optional[Any]=2	,       _UpperCAmelCase    :						Union[str, Any]=0.02	,       _UpperCAmelCase    :						Dict=1e-12	,       _UpperCAmelCase    :						Optional[int]=1	,       _UpperCAmelCase    :						int=0	,       _UpperCAmelCase    :						Optional[Any]=2	,       _UpperCAmelCase    :						List[Any]=768	,       _UpperCAmelCase    :						Optional[int]=2	,       _UpperCAmelCase    :						str=9	,       _UpperCAmelCase    :						List[Any]=1	,       _UpperCAmelCase    :						Optional[Any]=None	,       **_UpperCAmelCase    :						str	,       )				->		List[Any]:


             '''simple docstring'''




             super().__init__(
                 pad_token_id=_UpperCAmelCase	,       bos_token_id=_UpperCAmelCase	,       eos_token_id=_UpperCAmelCase	,       **_UpperCAmelCase	,       )

             UpperCAmelCase_    				=							vocab_size
             UpperCAmelCase_    				=							hidden_size
             UpperCAmelCase_    				=							num_hidden_layers
             UpperCAmelCase_    				=							num_attention_heads
             UpperCAmelCase_    				=							intermediate_size
             UpperCAmelCase_    				=							hidden_act
             UpperCAmelCase_    				=							hidden_dropout_prob
             UpperCAmelCase_    				=							attention_probs_dropout_prob
             UpperCAmelCase_    				=							max_position_embeddings
             UpperCAmelCase_    				=							type_vocab_size
             UpperCAmelCase_    				=							initializer_range
             UpperCAmelCase_    				=							layer_norm_eps
             UpperCAmelCase_    				=							embedding_size
             UpperCAmelCase_    				=							head_ratio
             UpperCAmelCase_    				=							conv_kernel_size
             UpperCAmelCase_    				=							num_groups
             UpperCAmelCase_    				=							classifier_dropout



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       @property
       def   lowercase__       (							self    :						Dict					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




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




