






import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


SCREAMING_SNAKE_CASE__		:							int 				=				logging.get_logger(__name__)





def  _lowerCamelCase						(						__lowerCamelCase=None ,							__lowerCamelCase=None       ) ->			List[str]:

     '''simple docstring'''
     return field(default_factory=lambda: default ,							metadata=__lowerCamelCase       )







@dataclass
class 				UpperCAmelCase_  :
     __lowerCamelCase      				=  list_field(
         default=[]							,      metadata={
             'help': (
                 'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
                 ' of all available models'
             )
         }							,      )

     __lowerCamelCase      				=  list_field(
         default=[8]							,      metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'}		)

     __lowerCamelCase      				=  list_field(
         default=[8, 32, 128, 512]							,      metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'}							,      )

     __lowerCamelCase      				=  field(
         default=__lowerCamelCase							,      metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'}							,      )
     __lowerCamelCase      				=  field(
         default=__lowerCamelCase							,      metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'}							,      )
     __lowerCamelCase      				=  field(
         default=__lowerCamelCase							,      metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'}		)
     __lowerCamelCase      				=  field(default=__lowerCamelCase							,      metadata={'help': 'Use FP16 to accelerate inference.'}		)
     __lowerCamelCase      				=  field(default=__lowerCamelCase							,      metadata={'help': 'Benchmark training of model'}		)
     __lowerCamelCase      				=  field(default=__lowerCamelCase							,      metadata={'help': 'Verbose memory tracing'}		)
     __lowerCamelCase      				=  field(
         default=__lowerCamelCase							,      metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'}							,      )
     __lowerCamelCase      				=  field(
         default=__lowerCamelCase							,      metadata={
             'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
         }							,      )
     __lowerCamelCase      				=  field(default=__lowerCamelCase							,      metadata={'help': 'Trace memory line by line'}		)
     __lowerCamelCase      				=  field(default=__lowerCamelCase							,      metadata={'help': 'Save result to a CSV file'}		)
     __lowerCamelCase      				=  field(default=__lowerCamelCase							,      metadata={'help': 'Save all print statements in a log file'}		)
     __lowerCamelCase      				=  field(default=__lowerCamelCase							,      metadata={'help': 'Whether to print environment information'}		)
     __lowerCamelCase      				=  field(
         default=__lowerCamelCase							,      metadata={
             'help': (
                 'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
                 ' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
                 ' for debugging / testing and on TPU.'
             )
         }							,      )
     __lowerCamelCase      				=  field(
         default=f"inference_time_{round(time()		)}.csv"							,      metadata={'help': 'CSV filename used if saving time results to csv.'}							,      )
     __lowerCamelCase      				=  field(
         default=f"inference_memory_{round(time()		)}.csv"							,      metadata={'help': 'CSV filename used if saving memory results to csv.'}							,      )
     __lowerCamelCase      				=  field(
         default=f"train_time_{round(time()		)}.csv"							,      metadata={'help': 'CSV filename used if saving time results to csv for training.'}							,      )
     __lowerCamelCase      				=  field(
         default=f"train_memory_{round(time()		)}.csv"							,      metadata={'help': 'CSV filename used if saving memory results to csv for training.'}							,      )
     __lowerCamelCase      				=  field(
         default=f"env_info_{round(time()		)}.csv"							,      metadata={'help': 'CSV filename used if saving environment information.'}							,      )
     __lowerCamelCase      				=  field(
         default=f"log_{round(time()		)}.csv"							,      metadata={'help': 'Log filename used if print statements are saved in log.'}							,      )
     __lowerCamelCase      				=  field(default=3							,      metadata={'help': 'Times an experiment will be run.'}		)
     __lowerCamelCase      				=  field(
         default=__lowerCamelCase							,      metadata={
             'help': (
                 'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
                 ' model weights.'
             )
         }							,      )




     def   __UpperCAmelCase  (				self		):
          warnings.warn(
              f"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
              """ are deprecated in general and it is advised to use external Benchmarking libraries """
              """ to benchmark Transformer models."""	,						_lowerCAmelCase	,						)




     def   __UpperCAmelCase  (				self		):
          return json.dumps(dataclasses.asdict(self		)	,						indent=2		)




     @property
     def   __UpperCAmelCase  (				self		):
          if len(self.models		) <= 0:
               raise ValueError(
                   """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
                   """ bert-base-cased` or `args.models = ['bert-base-cased']."""		)
          return self.models




     @property
     def   __UpperCAmelCase  (				self		):
          if not self.multi_process:
               return False
          elif self.is_tpu:
               logger.info("""Multiprocessing is currently not possible on TPU."""		)
               return False
          else:
               return True









"""simple docstring"""




from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


A_				:      Union[str, Any]           =			{
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		int       		=				'ernie_m'
			lowerCamelCase__ :		Dict[str, str]       		=				{"dropout": "classifier_dropout", "num_classes": "num_labels"}

			def __init__(       self					:    List[str]     ,					__UpperCAmelCase					:    int = 2_5_0_0_0_2     ,					__UpperCAmelCase					:    int = 7_6_8     ,					__UpperCAmelCase					:    int = 1_2     ,					__UpperCAmelCase					:    int = 1_2     ,					__UpperCAmelCase					:    int = 3_0_7_2     ,					__UpperCAmelCase					:    str = "gelu"     ,					__UpperCAmelCase					:    float = 0.1     ,					__UpperCAmelCase					:    float = 0.1     ,					__UpperCAmelCase					:    int = 5_1_4     ,					__UpperCAmelCase					:    float = 0.02     ,					__UpperCAmelCase					:    int = 1     ,					__UpperCAmelCase					:    float = 1e-05     ,					__UpperCAmelCase					:    Union[str, Any]=None     ,					__UpperCAmelCase					:    List[Any]=False     ,					__UpperCAmelCase					:    Union[str, Any]=0.0     ,					**__UpperCAmelCase					:    Any     ,					) ->     Optional[Any]:
						super().__init__(pad_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   vocab_size
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   layer_norm_eps
						SCREAMING_SNAKE_CASE__										=   classifier_dropout
						SCREAMING_SNAKE_CASE__										=   is_decoder
						SCREAMING_SNAKE_CASE__										=   act_dropout


