






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

from ..utils import logging


UpperCamelCase								=		logging.get_logger(__name__)






def    __magic_name__    (	SCREAMING_SNAKE_CASE=None     ,     SCREAMING_SNAKE_CASE=None				)     ->    str:
							return field(default_factory=lambda: default     ,     metadata=SCREAMING_SNAKE_CASE				)






@dataclass
class 				lowerCAmelCase_	:
				_UpperCamelCase					:       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"
				        )
				    }   ,		)

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

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

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


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


				@property
				def 						__a					(					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 						__a					(					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 ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = '''T5Config'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig



