
"""simple docstring"""

from dataclasses import dataclass, field
from typing import Optional






@dataclass
class 	_lowercase       :
	lowercase_				=     field(
	    default='codeparrot/codeparrot'    ,	metadata={'help': 'Model name or path of model to be trained.'}		)
	lowercase_				=     field(
	    default='./'    ,	metadata={'help': 'Save dir where model repo is cloned and models updates are saved to.'}		)
	lowercase_				=     field(
	    default='codeparrot/codeparrot-clean-train'    ,	metadata={'help': 'Name or path of training dataset.'}		)
	lowercase_				=     field(
	    default='codeparrot/codeparrot-clean-valid'    ,	metadata={'help': 'Name or path of validation dataset.'}		)
	lowercase_				=     field(default=2    ,	metadata={'help': 'Batch size for training.'}		)
	lowercase_				=     field(default=2    ,	metadata={'help': 'Batch size for evaluation.'}		)
	lowercase_				=     field(default=0.1    ,	metadata={'help': 'Value of weight decay.'}		)
	lowercase_				=     field(
	    default=1_0_0_0_0    ,	metadata={'help': 'Size of buffer used to shuffle streaming dataset.'}		)
	lowercase_				=     field(default=2e-4    ,	metadata={'help': 'Learning rate fo training.'}		)
	lowercase_				=     field(default='cosine'    ,	metadata={'help': 'Learning rate.'}		)
	lowercase_				=     field(
	    default=7_5_0    ,	metadata={'help': 'Number of warmup steps in the learning rate schedule.'}		)
	lowercase_				=     field(
	    default=1_6    ,	metadata={'help': 'Number of gradient accumulation steps.'}		)
	lowercase_				=     field(
	    default=__UpperCAmelCase    ,	metadata={'help': 'Use gradient checkpointing to reduce memory footprint.'}		)
	lowercase_				=     field(default=5_0_0_0_0    ,	metadata={'help': 'Maximum number of training steps.'}		)
	lowercase_				=     field(
	    default=-1    ,	metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}		)
	lowercase_				=     field(default=1_0_2_4    ,	metadata={'help': 'Sequence lengths used for training.'}		)
	lowercase_				=     field(default=1    ,	metadata={'help': 'Training seed.'}		)
	lowercase_				=     field(
	    default=1_0_2_4    ,	metadata={'help': 'Interval to save checkpoints. Measured as number of forward passes not training steps.'}    ,	)
	lowercase_				=     field(
	    default=__UpperCAmelCase    ,	metadata={'help': 'States path if the training should continue from a checkpoint folder.'}		)
	lowercase_				=     field(default=__UpperCAmelCase    ,	metadata={'help': 'If True the data is pretokenized.'}		)






@dataclass
class 	_lowercase       :
	lowercase_				=     field(
	    default='codeparrot/codeparrot'    ,	metadata={'help': 'Model name or path of model to be evaluated.'}		)
	lowercase_				=     field(
	    default='codeparrot/codeparrot-clean-valid'    ,	metadata={'help': 'Name or path of validation dataset.'}		)
	lowercase_				=     field(default=2    ,	metadata={'help': 'Batch size used for evaluation.'}		)
	lowercase_				=     field(
	    default=-1    ,	metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}		)
	lowercase_				=     field(default=1_0_2_4    ,	metadata={'help': 'Length of sequences to be evaluated.'}		)
	lowercase_				=     field(default=1    ,	metadata={'help': 'Random seed used for evaluation.'}		)






@dataclass
class 	_lowercase       :
	lowercase_				=     field(
	    default='codeparrot/codeparrot'    ,	metadata={'help': 'Model name or path of model to be evaluated.'}		)
	lowercase_				=     field(default=__UpperCAmelCase    ,	metadata={'help': 'Number of workers used for code evaluation.'}		)
	lowercase_				=     field(
	    default=__UpperCAmelCase    ,	metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'}    ,	)
	lowercase_				=     field(
	    default=__UpperCAmelCase    ,	metadata={'help': 'Sample from the language model\'s output distribution.'}		)
	lowercase_				=     field(default=0.2    ,	metadata={'help': 'Sampling temperature used for generation.'}		)
	lowercase_				=     field(default=2_5_6    ,	metadata={'help': 'Maximum number of newly generated tokens.'}		)
	lowercase_				=     field(default=0    ,	metadata={'help': 'Top-k parameter used for generation.'}		)
	lowercase_				=     field(default=0.95    ,	metadata={'help': 'Top-p parameter used for nucleus sampling.'}		)
	lowercase_				=     field(default=1_0    ,	metadata={'help': 'Number of generations to run in parallel.'}		)
	lowercase_				=     field(
	    default=2_0_0    ,	metadata={'help': 'Number of completions to generate for each sample.'}		)
	lowercase_				=     field(default=1    ,	metadata={'help': 'Random seed used for evaluation.'}		)
	lowercase_				=     field(
	    default='eval_results.json'    ,	metadata={'help': 'Random seed used for evaluation.'}		)
	lowercase_				=     field(
	    default='0'    ,	metadata={'help': 'Allow `code_eval` to execute Python code on machine'}		)
	lowercase_				=     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 	_lowercase       :
	lowercase_				=     field(
	    default=__UpperCAmelCase    ,	metadata={
	        'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
	    }    ,	)
	lowercase_				=     field(
	    default='transformersbook/codeparrot'    ,	metadata={'help': 'Folder or name of dataset to process.'}		)
	lowercase_				=     field(
	    default='codeparrot-clean'    ,	metadata={'help': 'Folder to save processed processed dataset.'}		)
	lowercase_				=     field(
	    default=1_0_0_0_0_0    ,	metadata={'help': 'Number of files to save per JSON output file.'}		)
	lowercase_				=     field(default='content'    ,	metadata={'help': 'Column containing text data to process.'}		)
	lowercase_				=     field(
	    default=1_0_0_0    ,	metadata={'help': 'Maximum line length in file, otherwise file is filtered.'}		)
	lowercase_				=     field(
	    default=1_0_0    ,	metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'}		)
	lowercase_				=     field(
	    default=0.25    ,	metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'}		)
	lowercase_				=     field(
	    default=1.5    ,	metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'}		)
	lowercase_				=     field(
	    default=0.7    ,	metadata={'help': 'Probability for filtering config, test and uncommon files.'}		)
	lowercase_				=     field(
	    default='codeparrot/codeparrot'    ,	metadata={'help': 'Name or path to the tokenizer.'}    ,	)
	lowercase_				=     field(
	    default=__UpperCAmelCase    ,	metadata={'help': 'If True, near-duplicate samples are removed.'}		)
	lowercase_				=     field(
	    default=0.85    ,	metadata={'help': 'Jaccard threshold for near-duplicate samples.'}		)






@dataclass
class 	_lowercase       :
	lowercase_				=     field(
	    default='gpt2'    ,	metadata={'help': 'Base tokenizer to build new tokenizer from.'}		)
	lowercase_				=     field(
	    default='transformersbook/codeparrot-train'    ,	metadata={'help': 'Dataset to train tokenizer on.'}		)
	lowercase_				=     field(default='content'    ,	metadata={'help': 'Column containing text data to process.'}		)
	lowercase_				=     field(default=2_0_0_0_0_0    ,	metadata={'help': 'Number of examples to train tokenizer on.'}		)
	lowercase_				=     field(
	    default=3_2_7_6_8    ,	metadata={'help': 'Number of examples to train the tokenizer on.'}		)
	lowercase_				=     field(default='codeparrot'    ,	metadata={'help': 'Name of new tokenizer.'}		)
	lowercase_				=     field(default=__UpperCAmelCase    ,	metadata={'help': 'Push saved tokenizer to the hub.'}		)






@dataclass
class 	_lowercase       :
	lowercase_				=     field(
	    default='codeparrot/codeparrot'    ,	metadata={'help': 'Name or path to the tokenizer.'}		)
	lowercase_				=     field(
	    default='codeparrot/codeparrot-clean-train'    ,	metadata={'help': 'Name or path to the dataset to pretokenize.'}		)
	lowercase_				=     field(
	    default='tokenized-codeparrot-train'    ,	metadata={'help': 'Repo name of the pretokenized data.'}		)
	lowercase_				=     field(default=__UpperCAmelCase    ,	metadata={'help': 'Number of workers used for code evaluation.'}		)






@dataclass
class 	_lowercase       :
	lowercase_				=     field(
	    default='gpt2-large'    ,	metadata={'help': 'Configuration to use for model initialization.'}		)
	lowercase_				=     field(
	    default='codeparrot/codeparrot'    ,	metadata={'help': 'Tokenizer attached to model.'}		)
	lowercase_				=     field(default='codeparrot'    ,	metadata={'help': 'Name of the created model.'}		)
	lowercase_				=     field(default=__UpperCAmelCase    ,	metadata={'help': 'Push saved tokenizer to the hub.'}		)








"""simple docstring"""

def 		UpperCAmelCase (		):





		'''simple docstring'''


		return 1
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return 0 if x < 0 else two_pence(x - 2      ) + one_pence()
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return 0 if x < 0 else five_pence(x - 5      ) + two_pence(a_      )
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return 0 if x < 0 else ten_pence(x - 10      ) + five_pence(a_      )
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return 0 if x < 0 else twenty_pence(x - 20      ) + ten_pence(a_      )
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return 0 if x < 0 else fifty_pence(x - 50      ) + twenty_pence(a_      )
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return 0 if x < 0 else one_pound(x - 100      ) + fifty_pence(a_      )
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return 0 if x < 0 else two_pound(x - 200      ) + one_pound(a_      )
def 		UpperCAmelCase (		a_ = 200      ):





		'''simple docstring'''


		return two_pound(a_      )


if __name__ == "__main__":
					print(solution(int(input().strip())))






