


"""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


a__    :							Union[str, Any]            =					logging.get_logger(__name__)




def        UpperCAmelCase__	(lowerCAmelCase_=None  ,						lowerCAmelCase_=None       ):




					'''simple docstring'''


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




@dataclass
class 			UpperCamelCase_     :

	"""simple docstring"""





	snake_case__    :  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"
	        )
	    }      ,  )

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

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

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




	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     Tuple:
						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."      ,      UpperCAmelCase__      ,      )




	def   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     List[str]:
						return json.dumps(dataclasses.asdict(self       )      ,      indent=2       )




	@property
	def   UpperCAmelCase_				(			self      :    List[str]       )	->     List[str]:




						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      :    Union[str, Any]       )	->     Union[str, Any]:




						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 ...configuration_utils import PretrainedConfig
from ...utils import logging


a__    :							int            =					logging.get_logger(__name__)

a__    :							Tuple            =					{'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Dict    =      "ctrl"
	snake_case__    :  int    =      ["past_key_values"]
	snake_case__    :  Tuple    =      {
	    "max_position_embeddings": "n_positions",
	    "hidden_size": "n_embd",
	    "num_attention_heads": "n_head",
	    "num_hidden_layers": "n_layer",
	}




	def __init__(			self      :    List[Any]      ,      UpperCAmelCase__      :    Any=2_4_6_5_3_4      ,      UpperCAmelCase__      :    List[Any]=2_5_6      ,      UpperCAmelCase__      :    Optional[int]=1_2_8_0      ,      UpperCAmelCase__      :    Optional[Any]=8_1_9_2      ,      UpperCAmelCase__      :    int=4_8      ,      UpperCAmelCase__      :    Optional[int]=1_6      ,      UpperCAmelCase__      :    Tuple=0.1      ,      UpperCAmelCase__      :    Optional[Any]=0.1      ,      UpperCAmelCase__      :    Any=1E-6      ,      UpperCAmelCase__      :    List[str]=0.02      ,      UpperCAmelCase__      :    Union[str, Any]=True      ,      **UpperCAmelCase__      :    Tuple      ,      )	->     str:
						__SCREAMING_SNAKE_CASE						       =    vocab_size
						__SCREAMING_SNAKE_CASE						       =    n_positions
						__SCREAMING_SNAKE_CASE						       =    n_embd
						__SCREAMING_SNAKE_CASE						       =    n_layer
						__SCREAMING_SNAKE_CASE						       =    n_head
						__SCREAMING_SNAKE_CASE						       =    dff
						__SCREAMING_SNAKE_CASE						       =    resid_pdrop
						__SCREAMING_SNAKE_CASE						       =    embd_pdrop
						__SCREAMING_SNAKE_CASE						       =    layer_norm_epsilon
						__SCREAMING_SNAKE_CASE						       =    initializer_range

						__SCREAMING_SNAKE_CASE						       =    use_cache

						super().__init__(**UpperCAmelCase__       )
