


"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE = 50_000_000			)      ->			int:
							snake_case_          =						set()
							snake_case_          =						int((limit - 24) ** (1 / 2)			)

							snake_case_          =						set(range(3			,	prime_square_limit + 1			,	2			)			)
							primes.add(2			)
							for p in range(3			,	prime_square_limit + 1			,	2			):
														if p not in primes:
																					continue
														primes.difference_update(set(range(p * p			,	prime_square_limit + 1			,	_SCREAMING_SNAKE_CASE			)			)			)

							for primea in primes:
														snake_case_          =						primea * primea
														for primea in primes:
																					snake_case_          =						primea * primea * primea
																					if square + cube >= limit - 16:
																												break
																					for primea in primes:
																												snake_case_          =						primea * primea * primea * primea
																												snake_case_          =						square + cube + tetr
																												if total >= limit:
																																			break
																												ret.add(_SCREAMING_SNAKE_CASE			)

							return len(_SCREAMING_SNAKE_CASE			)


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







"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional





@dataclass
class        __A       :





							'''simple docstring'''

							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot"""  ,						metadata={"""help""": """Model name or path of model to be trained."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""./"""  ,						metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot-clean-train"""  ,						metadata={"""help""": """Name or path of training dataset."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot-clean-valid"""  ,						metadata={"""help""": """Name or path of validation dataset."""})
							__lowercase:						Optional[int]							 =      field(default=2  ,						metadata={"""help""": """Batch size for training."""})
							__lowercase:						Optional[int]							 =      field(default=2  ,						metadata={"""help""": """Batch size for evaluation."""})
							__lowercase:						Optional[float]							 =      field(default=0.1  ,						metadata={"""help""": """Value of weight decay."""})
							__lowercase:						Optional[int]							 =      field(
							    default=1_00_00  ,						metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""})
							__lowercase:						Optional[float]							 =      field(default=2E-4  ,						metadata={"""help""": """Learning rate fo training."""})
							__lowercase:						Optional[str]							 =      field(default="""cosine"""  ,						metadata={"""help""": """Learning rate."""})
							__lowercase:						Optional[int]							 =      field(
							    default=7_50  ,						metadata={"""help""": """Number of warmup steps in the learning rate schedule."""})
							__lowercase:						Optional[int]							 =      field(
							    default=16  ,						metadata={"""help""": """Number of gradient accumulation steps."""})
							__lowercase:						Optional[bool]							 =      field(
							    default=snake_case__  ,						metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""})
							__lowercase:						Optional[int]							 =      field(default=5_00_00  ,						metadata={"""help""": """Maximum number of training steps."""})
							__lowercase:						Optional[int]							 =      field(
							    default=-1  ,						metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""})
							__lowercase:						Optional[int]							 =      field(default=10_24  ,						metadata={"""help""": """Sequence lengths used for training."""})
							__lowercase:						Optional[int]							 =      field(default=1  ,						metadata={"""help""": """Training seed."""})
							__lowercase:						Optional[int]							 =      field(
							    default=10_24  ,						metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}  ,						)
							__lowercase:						Optional[str]							 =      field(
							    default=snake_case__  ,						metadata={"""help""": """States path if the training should continue from a checkpoint folder."""})
							__lowercase:						Optional[bool]							 =      field(default=snake_case__  ,						metadata={"""help""": """If True the data is pretokenized."""})





@dataclass
class        __A       :





							'''simple docstring'''

							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot"""  ,						metadata={"""help""": """Model name or path of model to be evaluated."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot-clean-valid"""  ,						metadata={"""help""": """Name or path of validation dataset."""})
							__lowercase:						Optional[int]							 =      field(default=2  ,						metadata={"""help""": """Batch size used for evaluation."""})
							__lowercase:						Optional[int]							 =      field(
							    default=-1  ,						metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""})
							__lowercase:						Optional[int]							 =      field(default=10_24  ,						metadata={"""help""": """Length of sequences to be evaluated."""})
							__lowercase:						Optional[int]							 =      field(default=1  ,						metadata={"""help""": """Random seed used for evaluation."""})





@dataclass
class        __A       :





							'''simple docstring'''

							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot"""  ,						metadata={"""help""": """Model name or path of model to be evaluated."""})
							__lowercase:						Optional[int]							 =      field(default=snake_case__  ,						metadata={"""help""": """Number of workers used for code evaluation."""})
							__lowercase:						Optional[int]							 =      field(
							    default=snake_case__  ,						metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}  ,						)
							__lowercase:						Optional[bool]							 =      field(
							    default=snake_case__  ,						metadata={"""help""": """Sample from the language model's output distribution."""})
							__lowercase:						Optional[float]							 =      field(default=0.2  ,						metadata={"""help""": """Sampling temperature used for generation."""})
							__lowercase:						Optional[int]							 =      field(default=2_56  ,						metadata={"""help""": """Maximum number of newly generated tokens."""})
							__lowercase:						Optional[int]							 =      field(default=0  ,						metadata={"""help""": """Top-k parameter used for generation."""})
							__lowercase:						Optional[float]							 =      field(default=0.9_5  ,						metadata={"""help""": """Top-p parameter used for nucleus sampling."""})
							__lowercase:						Optional[int]							 =      field(default=10  ,						metadata={"""help""": """Number of generations to run in parallel."""})
							__lowercase:						Optional[int]							 =      field(
							    default=2_00  ,						metadata={"""help""": """Number of completions to generate for each sample."""})
							__lowercase:						Optional[int]							 =      field(default=1  ,						metadata={"""help""": """Random seed used for evaluation."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""eval_results.json"""  ,						metadata={"""help""": """Random seed used for evaluation."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""0"""  ,						metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""})
							__lowercase:						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        __A       :





							'''simple docstring'''

							__lowercase:						Optional[int]							 =      field(
							    default=snake_case__  ,						metadata={
							        """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
							    }  ,						)
							__lowercase:						Optional[str]							 =      field(
							    default="""transformersbook/codeparrot"""  ,						metadata={"""help""": """Folder or name of dataset to process."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot-clean"""  ,						metadata={"""help""": """Folder to save processed processed dataset."""})
							__lowercase:						Optional[int]							 =      field(
							    default=10_00_00  ,						metadata={"""help""": """Number of files to save per JSON output file."""})
							__lowercase:						Optional[str]							 =      field(default="""content"""  ,						metadata={"""help""": """Column containing text data to process."""})
							__lowercase:						Optional[float]							 =      field(
							    default=10_00  ,						metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""})
							__lowercase:						Optional[float]							 =      field(
							    default=1_00  ,						metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""})
							__lowercase:						Optional[float]							 =      field(
							    default=0.2_5  ,						metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""})
							__lowercase:						Optional[float]							 =      field(
							    default=1.5  ,						metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""})
							__lowercase:						Optional[float]							 =      field(
							    default=0.7  ,						metadata={"""help""": """Probability for filtering config, test and uncommon files."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot"""  ,						metadata={"""help""": """Name or path to the tokenizer."""}  ,						)
							__lowercase:						Optional[bool]							 =      field(
							    default=snake_case__  ,						metadata={"""help""": """If True, near-duplicate samples are removed."""})
							__lowercase:						Optional[float]							 =      field(
							    default=0.8_5  ,						metadata={"""help""": """Jaccard threshold for near-duplicate samples."""})





@dataclass
class        __A       :





							'''simple docstring'''

							__lowercase:						Optional[str]							 =      field(
							    default="""gpt2"""  ,						metadata={"""help""": """Base tokenizer to build new tokenizer from."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""transformersbook/codeparrot-train"""  ,						metadata={"""help""": """Dataset to train tokenizer on."""})
							__lowercase:						Optional[str]							 =      field(default="""content"""  ,						metadata={"""help""": """Column containing text data to process."""})
							__lowercase:						Optional[int]							 =      field(default=20_00_00  ,						metadata={"""help""": """Number of examples to train tokenizer on."""})
							__lowercase:						Optional[int]							 =      field(
							    default=3_27_68  ,						metadata={"""help""": """Number of examples to train the tokenizer on."""})
							__lowercase:						Optional[str]							 =      field(default="""codeparrot"""  ,						metadata={"""help""": """Name of new tokenizer."""})
							__lowercase:						Optional[bool]							 =      field(default=snake_case__  ,						metadata={"""help""": """Push saved tokenizer to the hub."""})





@dataclass
class        __A       :





							'''simple docstring'''

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





@dataclass
class        __A       :





							'''simple docstring'''

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



