

from dataclasses import dataclass, field
from typing import Optional

@dataclass
class 				UpperCAmelCase_		:
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="codeparrot/codeparrot"				,       metadata={"help": "Model name or path of model to be trained."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="./"				,       metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="codeparrot/codeparrot-clean-train"				,       metadata={"help": "Name or path of training dataset."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="codeparrot/codeparrot-clean-valid"				,       metadata={"help": "Name or path of validation dataset."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(default=2				,       metadata={"help": "Batch size for training."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(default=2				,       metadata={"help": "Batch size for evaluation."}   )
							UpperCAmelCase__       :						Optional[float]	    =    field(default=0.1				,       metadata={"help": "Value of weight decay."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=10000				,       metadata={"help": "Size of buffer used to shuffle streaming dataset."}   )
							UpperCAmelCase__       :						Optional[float]	    =    field(default=2E-4				,       metadata={"help": "Learning rate fo training."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(default="cosine"				,       metadata={"help": "Learning rate."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=750				,       metadata={"help": "Number of warmup steps in the learning rate schedule."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=16				,       metadata={"help": "Number of gradient accumulation steps."}   )
							UpperCAmelCase__       :						Optional[bool]	    =    field(
							    default=__lowercase				,       metadata={"help": "Use gradient checkpointing to reduce memory footprint."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(default=50000				,       metadata={"help": "Maximum number of training steps."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=-1				,       metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(default=1024				,       metadata={"help": "Sequence lengths used for training."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(default=1				,       metadata={"help": "Training seed."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=1024				,       metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}				,       )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default=__lowercase				,       metadata={"help": "States path if the training should continue from a checkpoint folder."}   )
							UpperCAmelCase__       :						Optional[bool]	    =    field(default=__lowercase				,       metadata={"help": "If True the data is pretokenized."}   )



@dataclass
class 				UpperCAmelCase_		:
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="codeparrot/codeparrot"				,       metadata={"help": "Model name or path of model to be evaluated."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="codeparrot/codeparrot-clean-valid"				,       metadata={"help": "Name or path of validation dataset."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(default=2				,       metadata={"help": "Batch size used for evaluation."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=-1				,       metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(default=1024				,       metadata={"help": "Length of sequences to be evaluated."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(default=1				,       metadata={"help": "Random seed used for evaluation."}   )



@dataclass
class 				UpperCAmelCase_		:
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="codeparrot/codeparrot"				,       metadata={"help": "Model name or path of model to be evaluated."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(default=__lowercase				,       metadata={"help": "Number of workers used for code evaluation."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=__lowercase				,       metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}				,       )
							UpperCAmelCase__       :						Optional[bool]	    =    field(
							    default=__lowercase				,       metadata={"help": "Sample from the language model's output distribution."}   )
							UpperCAmelCase__       :						Optional[float]	    =    field(default=0.2				,       metadata={"help": "Sampling temperature used for generation."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(default=256				,       metadata={"help": "Maximum number of newly generated tokens."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(default=0				,       metadata={"help": "Top-k parameter used for generation."}   )
							UpperCAmelCase__       :						Optional[float]	    =    field(default=0.95				,       metadata={"help": "Top-p parameter used for nucleus sampling."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(default=10				,       metadata={"help": "Number of generations to run in parallel."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=200				,       metadata={"help": "Number of completions to generate for each sample."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(default=1				,       metadata={"help": "Random seed used for evaluation."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="eval_results.json"				,       metadata={"help": "Random seed used for evaluation."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="0"				,       metadata={"help": "Allow `code_eval` to execute Python code on machine"}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=-1				,       metadata={
							        "help": (
							            "Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"
							            " number corresponds to which GPU device id to run on."
							        )
							    }				,       )



@dataclass
class 				UpperCAmelCase_		:
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=__lowercase				,       metadata={
							        "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
							    }				,       )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="transformersbook/codeparrot"				,       metadata={"help": "Folder or name of dataset to process."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="codeparrot-clean"				,       metadata={"help": "Folder to save processed processed dataset."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=100000				,       metadata={"help": "Number of files to save per JSON output file."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(default="content"				,       metadata={"help": "Column containing text data to process."}   )
							UpperCAmelCase__       :						Optional[float]	    =    field(
							    default=1000				,       metadata={"help": "Maximum line length in file, otherwise file is filtered."}   )
							UpperCAmelCase__       :						Optional[float]	    =    field(
							    default=100				,       metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}   )
							UpperCAmelCase__       :						Optional[float]	    =    field(
							    default=0.25				,       metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}   )
							UpperCAmelCase__       :						Optional[float]	    =    field(
							    default=1.5				,       metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}   )
							UpperCAmelCase__       :						Optional[float]	    =    field(
							    default=0.7				,       metadata={"help": "Probability for filtering config, test and uncommon files."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="codeparrot/codeparrot"				,       metadata={"help": "Name or path to the tokenizer."}				,       )
							UpperCAmelCase__       :						Optional[bool]	    =    field(
							    default=__lowercase				,       metadata={"help": "If True, near-duplicate samples are removed."}   )
							UpperCAmelCase__       :						Optional[float]	    =    field(
							    default=0.85				,       metadata={"help": "Jaccard threshold for near-duplicate samples."}   )



@dataclass
class 				UpperCAmelCase_		:
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="gpt2"				,       metadata={"help": "Base tokenizer to build new tokenizer from."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="transformersbook/codeparrot-train"				,       metadata={"help": "Dataset to train tokenizer on."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(default="content"				,       metadata={"help": "Column containing text data to process."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(default=200000				,       metadata={"help": "Number of examples to train tokenizer on."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(
							    default=32768				,       metadata={"help": "Number of examples to train the tokenizer on."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(default="codeparrot"				,       metadata={"help": "Name of new tokenizer."}   )
							UpperCAmelCase__       :						Optional[bool]	    =    field(default=__lowercase				,       metadata={"help": "Push saved tokenizer to the hub."}   )



@dataclass
class 				UpperCAmelCase_		:
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="codeparrot/codeparrot"				,       metadata={"help": "Name or path to the tokenizer."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="codeparrot/codeparrot-clean-train"				,       metadata={"help": "Name or path to the dataset to pretokenize."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="tokenized-codeparrot-train"				,       metadata={"help": "Repo name of the pretokenized data."}   )
							UpperCAmelCase__       :						Optional[int]	    =    field(default=__lowercase				,       metadata={"help": "Number of workers used for code evaluation."}   )



@dataclass
class 				UpperCAmelCase_		:
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="gpt2-large"				,       metadata={"help": "Configuration to use for model initialization."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(
							    default="codeparrot/codeparrot"				,       metadata={"help": "Tokenizer attached to model."}   )
							UpperCAmelCase__       :						Optional[str]	    =    field(default="codeparrot"				,       metadata={"help": "Name of the created model."}   )
							UpperCAmelCase__       :						Optional[bool]	    =    field(default=__lowercase				,       metadata={"help": "Push saved tokenizer to the hub."}   )






from math import ceil



def    __UpperCAmelCase (	__a      :			int = 1_001	)			->     int:

					"""simple docstring"""

					_a	:    List[Any]         =		1

					for i in range(1		,int(ceil(n / 2.0	)	)	):
										_a	:    Optional[Any]         =		2 * i + 1
										_a	:    Optional[int]         =		2 * i
										_a	:    str         =		total + 4 * odd**2 - 6 * even

					return total


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						print(solution())
			else:
						try:
									a__				=		int(sys.argv[1])
									print(solution(n))
						except ValueError:
									print('''Invalid entry - please enter a number''')



