"""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   					=    logging.get_logger(__name__)




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase=None  ,					__UpperCAmelCase=None       )							->   List[str]:
				return field(default_factory=lambda: default  ,					metadata=__UpperCAmelCase       )




@dataclass
class lowerCamelCase		:






					'''simple docstring'''


					a   	=		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"
					        )
					    }    ,    )

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

					a   	=		list_field(
					    default=[8, 3_2, 1_2_8, 5_1_2]    ,    metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}    ,    )

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



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Any:
									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."   , A__   , )



					def 			lowerCAmelCase_					(						self			:				int		)							->       Tuple:
									return json.dumps(dataclasses.asdict(self		)   , indent=2		)



					@property
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       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 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Optional[int]:
									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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   List[str]:
				if not head:
								return True
				# split the list to two parts
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				head.next, head
				while fast and fast.next:
								SCREAMING_SNAKE_CASE__		  =				fast.next.next
								SCREAMING_SNAKE_CASE__		  =				slow.next
				SCREAMING_SNAKE_CASE__		  =				slow.next
				SCREAMING_SNAKE_CASE__		  =				None  # Don't forget here! But forget still works!
				# reverse the second part
				SCREAMING_SNAKE_CASE__		  =				None
				while second:
								SCREAMING_SNAKE_CASE__		  =				second.next
								SCREAMING_SNAKE_CASE__		  =				node
								SCREAMING_SNAKE_CASE__		  =				second
								SCREAMING_SNAKE_CASE__		  =				nxt
				# compare two parts
				# second part has the same or one less node
				while node:
								if node.val != head.val:
												return False
								SCREAMING_SNAKE_CASE__		  =				node.next
								SCREAMING_SNAKE_CASE__		  =				head.next
				return True




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   str:
				if not head or not head.next:
								return True

				# 1. Get the midpoint (slow)
				SCREAMING_SNAKE_CASE__		  =				SCREAMING_SNAKE_CASE__		  =				SCREAMING_SNAKE_CASE__		  =				head
				while fast and fast.next:
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				fast.next.next, slow.next

				# 2. Push the second half into the stack
				SCREAMING_SNAKE_CASE__		  =				[slow.val]
				while slow.next:
								SCREAMING_SNAKE_CASE__		  =				slow.next
								stack.append(slow.val       )

				# 3. Comparison
				while stack:
								if stack.pop() != cur.val:
												return False
								SCREAMING_SNAKE_CASE__		  =				cur.next

				return True




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   str:
				if not head or not head.next:
								return True
				SCREAMING_SNAKE_CASE__		  =				{}
				SCREAMING_SNAKE_CASE__		  =				0
				while head:
								if head.val in d:
												d[head.val].append(__UpperCAmelCase       )
								else:
												SCREAMING_SNAKE_CASE__		  =				[pos]
								SCREAMING_SNAKE_CASE__		  =				head.next
								pos += 1
				SCREAMING_SNAKE_CASE__		  =				pos - 1
				SCREAMING_SNAKE_CASE__		  =				0
				for v in d.values():
								if len(__UpperCAmelCase       ) % 2 != 0:
												middle += 1
								else:
												SCREAMING_SNAKE_CASE__		  =				0
												for i in range(0  ,					len(__UpperCAmelCase       )       ):
																if v[i] + v[len(__UpperCAmelCase       ) - 1 - step] != checksum:
																				return False
																step += 1
								if middle > 1:
												return False
				return True




