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


def 				lowerCamelCase__				(	A       :      int=None				,       A       :      List[Any]=None					):




						'''simple docstring'''






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






@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				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"
				        )
				    }						,    )

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

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

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




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

										"""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.'''				,   lowerCAmelCase				,   )




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

										"""simple docstring"""




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




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

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



_lowercase      :      Any            =					range(2, 20 + 1)
_lowercase      :      str            =					[10**k for k in range(ks[-1] + 1)]
_lowercase      :      dict[int, dict[int, list[list[int]]]]            =					{}


def 				lowerCamelCase__				(	A       :      int				,       A       :      str				,       A       :      List[Any]				,       A       :      Union[str, Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     sum(a_i[j] for j in range(A				,       len(A					)					)					)
						UpperCAmelCase					    =     sum(a_i[j] * base[j] for j in range(min(len(A					)				,       A					)					)					)

						UpperCAmelCase    ,    UpperCAmelCase					    =     0, 0
						UpperCAmelCase					    =     n - i

						UpperCAmelCase					    =     memo.get(A					)

						if sub_memo is not None:
												UpperCAmelCase					    =     sub_memo.get(A					)

												if jumps is not None and len(A					) > 0:
																		# find and make the largest jump without going over
																		UpperCAmelCase					    =     -1
																		for _k in range(len(A					) - 1				,       -1				,       -1					):
																								if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																														UpperCAmelCase					    =     _k
																														break

																		if max_jump >= 0:
																								UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     jumps[max_jump]
																								# since the difference between jumps is cached, add c
																								UpperCAmelCase					    =     diff + c
																								for j in range(min(A				,       len(A					)					)					):
																														UpperCAmelCase    ,    UpperCAmelCase					    =     divmod(A				,       10					)
																								if new_c > 0:
																														add(A				,       A				,       A					)

												else:
																		UpperCAmelCase					    =     []
						else:
												UpperCAmelCase					    =     {c: []}
												UpperCAmelCase					    =     sub_memo

						if dn >= max_dn or c + diff >= base[k]:
												return diff, dn

						if k > ks[0]:
												while True:
																		# keep doing smaller jumps
																		UpperCAmelCase    ,    UpperCAmelCase					    =     next_term(A				,       k - 1				,       i + dn				,       A					)
																		diff += _diff
																		dn += terms_jumped

																		if dn >= max_dn or c + diff >= base[k]:
																								break
						else:
												# would be too small a jump, just compute sequential terms instead
												UpperCAmelCase    ,    UpperCAmelCase					    =     compute(A				,       A				,       i + dn				,       A					)
												diff += _diff
												dn += terms_jumped

						UpperCAmelCase					    =     sub_memo[c]

						# keep jumps sorted by # of terms skipped
						UpperCAmelCase					    =     0
						while j < len(A					):
												if jumps[j][1] > dn:
																		break
												j += 1

						# cache the jump for this value digitsum(b) and c
						sub_memo[c].insert(A				,       (diff, dn, k)					)
						return (diff, dn)


def 				lowerCamelCase__				(	A       :      Dict				,       A       :      Optional[int]				,       A       :      List[Any]				,       A       :      int					):




						'''simple docstring'''






						if i >= n:
												return 0, i
						if k > len(A					):
												a_i.extend([0 for _ in range(k - len(A					)					)]					)

						# note: a_i -> b * 10^k + c
						# ds_b -> digitsum(b)
						# ds_c -> digitsum(c)
						UpperCAmelCase					    =     i
						UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     0, 0, 0
						for j in range(len(A					)					):
												if j >= k:
																		ds_b += a_i[j]
												else:
																		ds_c += a_i[j]

						while i < n:
												i += 1
												UpperCAmelCase					    =     ds_c + ds_b
												diff += addend
												UpperCAmelCase					    =     0
												for j in range(A					):
																		UpperCAmelCase					    =     a_i[j] + addend
																		UpperCAmelCase    ,    UpperCAmelCase					    =     divmod(A				,       10					)

																		ds_c += a_i[j]

												if addend > 0:
																		break

						if addend > 0:
												add(A				,       A				,       A					)
						return diff, i - start_i


def 				lowerCamelCase__				(	A       :      List[str]				,       A       :      Optional[int]				,       A       :      Optional[Any]					):




						'''simple docstring'''






						for j in range(A				,       len(A					)					):
												UpperCAmelCase					    =     digits[j] + addend
												if s >= 10:
																		UpperCAmelCase    ,    UpperCAmelCase					    =     divmod(A				,       10					)
																		UpperCAmelCase					    =     addend // 10 + quotient
												else:
																		UpperCAmelCase					    =     s
																		UpperCAmelCase					    =     addend // 10

												if addend == 0:
																		break

						while addend > 0:
												UpperCAmelCase    ,    UpperCAmelCase					    =     divmod(A				,       10					)
												digits.append(A					)


def 				lowerCamelCase__				(	A       :      int = 10**15					):




						'''simple docstring'''






						UpperCAmelCase					    =     [1]
						UpperCAmelCase					    =     1
						UpperCAmelCase					    =     0
						while True:
												UpperCAmelCase    ,    UpperCAmelCase					    =     next_term(A				,       20				,       i + dn				,       A					)
												dn += terms_jumped
												if dn == n - i:
																		break

						UpperCAmelCase					    =     0
						for j in range(len(A					)					):
												a_n += digits[j] * 10**j
						return a_n


if __name__ == "__main__":
	print(F"""{solution() = }""")





