



'''simple docstring'''





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

from ..utils import logging


lowercase_	 =						logging.get_logger(__name__)




def lowerCAmelCase						(__A=None						,			__A=None):



					"""simple docstring"""



					return field(default_factory=lambda: default						,			metadata=SCREAMING_SNAKE_CASE_)






@dataclass
class __A    :



	'''simple docstring'''







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

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

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



	def       a__						(self )					->  Dict:


						"""simple docstring"""



						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.'''				,     __lowercase				,     )



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


						"""simple docstring"""



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



	@property
	def       a__						(self )					->  Optional[Any]:


						"""simple docstring"""



						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       a__						(self )					->  List[Any]:


						"""simple docstring"""



						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'''





def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						set()
     # To detect a back edge, keep track of vertices currently in the recursion stack
     _a											=						set()
     return any(
         node not in visited and depth_first_search(__A						,			__A						,			__A						,			__A)
         for node in graph)




def lowerCAmelCase						(__A						,			__A						,			__A						,			__A):



     """simple docstring"""



     visited.add(__A)
     rec_stk.add(__A)

     for node in graph[vertex]:
          if node not in visited:
               if depth_first_search(__A						,			__A						,			__A						,			__A):
                    return True
          elif node in rec_stk:
               return True

    # The node needs to be removed from recursion stack before function ends
     rec_stk.remove(__A)
     return False


if __name__ == "__main__":
  from doctest import testmod

  testmod()



