


"""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


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE=None							,    SCREAMING_SNAKE_CASE=None )  ->     Tuple:







							"""simple docstring"""


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







@dataclass
class 							__magic_name__     :
					_SCREAMING_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'
					        )
					    }      ,		)

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

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

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





					def 							lowerCAmelCase      (				self   :							Dict    ):
												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 							lowerCAmelCase      (				self   :							Any    ):
												return json.dumps(dataclasses.asdict(self    )							,						indent=2    )





					@property
					def 							lowerCAmelCase      (				self   :							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   :							List[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

from dataclasses import dataclass







@dataclass
class 							__magic_name__     :
					_SCREAMING_SNAKE_CASE       :					float
					_SCREAMING_SNAKE_CASE       :					TreeNode | None     							=   None
					_SCREAMING_SNAKE_CASE       :					TreeNode | None     							=   None




def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     bool:







							"""simple docstring"""


							def is_valid_tree(SCREAMING_SNAKE_CASE ) -> bool:
														if node is None:
																					return True

														if not isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
																					return False

														try:
																					float(node.data )
														except (TypeError, ValueError):
																					return False

														return is_valid_tree(node.left ) and is_valid_tree(node.right )

							if not is_valid_tree(SCREAMING_SNAKE_CASE ):
														raise ValueError(
														    "Each node should be type of TreeNode and data should be float." )

							def is_binary_search_tree_recursive_check(
							    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) -> bool:

														if node is None:
																					return True

														return (
														    left_bound < node.data < right_bound
														    and is_binary_search_tree_recursive_check(node.left							,    SCREAMING_SNAKE_CASE							,    node.data )
														    and is_binary_search_tree_recursive_check(
														        node.right							,    node.data							,    SCREAMING_SNAKE_CASE )
														)

							return is_binary_search_tree_recursive_check(SCREAMING_SNAKE_CASE							,    -float("inf" )							,    float("inf" ) )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
