


"""simple docstring"""






from dataclasses import dataclass, field
from typing import Optional




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




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




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




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




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




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


from math import sqrt
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int = 1000000       )					->				int:
	__A :			int		    =			0
	__A :			int		    =			0
	__A :			int

	while num_cuboids <= limit:
		max_cuboid_size += 1
		for sum_shortest_sides in range(2			,2 * max_cuboid_size + 1       ):
			if sqrt(sum_shortest_sides**2 + max_cuboid_size**2       ).is_integer():
				num_cuboids += (
				    min(a__			,sum_shortest_sides // 2       )
				    - max(1			,sum_shortest_sides - max_cuboid_size       )
				    + 1
				)

	return max_cuboid_size


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





