




from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase__			:       List[str]         =							logging.get_logger(__name__)

lowerCamelCase__			:       Optional[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 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   "convbert"



 def __init__(							self  :		List[str]    ,    _lowerCAmelCase  :		Optional[Any]=30_522    ,    _lowerCAmelCase  :		Any=768    ,    _lowerCAmelCase  :		int=12    ,    _lowerCAmelCase  :		str=12    ,    _lowerCAmelCase  :		List[str]=3_072    ,    _lowerCAmelCase  :		Dict="gelu"    ,    _lowerCAmelCase  :		List[str]=0.1    ,    _lowerCAmelCase  :		Tuple=0.1    ,    _lowerCAmelCase  :		List[str]=512    ,    _lowerCAmelCase  :		Dict=2    ,    _lowerCAmelCase  :		Union[str, Any]=0.02    ,    _lowerCAmelCase  :		List[Any]=1E-12    ,    _lowerCAmelCase  :		Union[str, Any]=1    ,    _lowerCAmelCase  :		List[str]=0    ,    _lowerCAmelCase  :		List[str]=2    ,    _lowerCAmelCase  :		str=768    ,    _lowerCAmelCase  :		Dict=2    ,    _lowerCAmelCase  :		Dict=9    ,    _lowerCAmelCase  :		int=1    ,    _lowerCAmelCase  :		List[Any]=None    ,    **_lowerCAmelCase  :		Dict    ,    ):
       super().__init__(
           pad_token_id=_lowerCAmelCase    ,    bos_token_id=_lowerCAmelCase    ,    eos_token_id=_lowerCAmelCase    ,    **_lowerCAmelCase    ,    )

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





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 @property
 def 				lowerCAmelCase_  (							self  :		int						):
       if self.task == "multiple-choice":
             SCREAMING_SNAKE_CASE_ :    Union[str, Any]     						=      {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
             SCREAMING_SNAKE_CASE_ :    Tuple     						=      {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('token_type_ids', dynamic_axis),
           ]						)





import gc
import threading
import time

import psutil
import torch





class 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      psutil.Process()
       SCREAMING_SNAKE_CASE_     						=      False



 def 				lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_     						=      -1

       while True:
             SCREAMING_SNAKE_CASE_     						=      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  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      True
       SCREAMING_SNAKE_CASE_     						=      threading.Thread(target=self.peak_monitor						)
       SCREAMING_SNAKE_CASE_     						=      True
       self.thread.start()




 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      False
       self.thread.join()
       return self.cpu_memory_peak


lowerCamelCase__			:       List[str]         =							PeakCPUMemory()

def  UpperCAmelCase_							(				) ->			Tuple:
      # Time
      SCREAMING_SNAKE_CASE_     						=      {'time': time.time()}

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

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

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

      return measures

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]							) ->			Optional[Any]:
      # Time
      SCREAMING_SNAKE_CASE_     						=      {'time': time.time() - start_measures['time']}

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

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

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

      return measures





def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]      ,      __UpperCAmelCase						:     Any							) ->			Optional[Any]:
      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(__UpperCAmelCase							)]:.2f}MiB"							)
            SCREAMING_SNAKE_CASE_     						=      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"							)