




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

from ..utils import logging


lowerCamelCase__					:  int      							=				logging.get_logger(__name__)

def    UpperCAmelCase_				( __UpperCAmelCase		:      int=None     ,     __UpperCAmelCase		:      Tuple=None				)   ->					str:
      return field(default_factory=lambda: default     ,     metadata=__UpperCAmelCase				)





@dataclass
class 			lowerCamelCase_				:


 '''simple docstring'''







 lowercase_							       =       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"
         )
     }			,  )

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

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

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



 def  lowerCAmelCase_    (				self    :						List[str]		):
       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  lowerCAmelCase_    (				self    :						str		):
       return json.dumps(dataclasses.asdict(self		)							,							indent=2		)



 @property
 def  lowerCAmelCase_    (				self    :						Dict		):
       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  lowerCAmelCase_    (				self    :						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 __future__ import annotations



def 							_lowerCamelCase			(			__a,				__a = None     ):
		SCREAMING_SNAKE_CASE_    			=			word_bank or []
		# create a table
		SCREAMING_SNAKE_CASE_    			=			len(__a     ) + 1

		SCREAMING_SNAKE_CASE_    			=			[]
		for _ in range(__a     ):
				table.append([]     )
		# seed value
		SCREAMING_SNAKE_CASE_    			=			[[]]  # because empty string has empty combination

		# iterate through the indices
		for i in range(__a     ):
				# condition
				if table[i] != []:
						for word in word_bank:
								# slice condition
								if target[i : i + len(__a     )] == word:
										SCREAMING_SNAKE_CASE_    			=			[
										    [word, *way] for way in table[i]
										]
										# adds the word to every combination the current position holds
										# now,push that combination to the table[i+len(word)]
										table[i + len(__a     )] += new_combinations

    # combinations are in reverse order so reverse for better output
		for combination in table[len(__a     )]:
				combination.reverse()

		return table[len(__a     )]


if __name__ == "__main__":
						print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
						print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
						print(
						    all_construct(
						        'hexagonosaurus',
						        ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
						    )
						)