
"""simple docstring"""






from dataclasses import dataclass, field
from typing import Optional




@dataclass
class 			_SCREAMING_SNAKE_CASE					:
			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=1_0000	, 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=_UpperCAmelCase	, metadata={"help": "Use gradient checkpointing to reduce memory footprint."}							)
			UpperCAmelCase_  :Optional[int]				 =						field(default=5_0000	, 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=_UpperCAmelCase	, metadata={"help": "States path if the training should continue from a checkpoint folder."}							)
			UpperCAmelCase_  :Optional[bool]				 =						field(default=_UpperCAmelCase	, metadata={"help": "If True the data is pretokenized."}							)




@dataclass
class 			_SCREAMING_SNAKE_CASE					:
			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 			_SCREAMING_SNAKE_CASE					:
			UpperCAmelCase_  :Optional[str]				 =						field(
			    default="codeparrot/codeparrot"	, metadata={"help": "Model name or path of model to be evaluated."}							)
			UpperCAmelCase_  :Optional[int]				 =						field(default=_UpperCAmelCase	, metadata={"help": "Number of workers used for code evaluation."}							)
			UpperCAmelCase_  :Optional[int]				 =						field(
			    default=_UpperCAmelCase	, metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}	, )
			UpperCAmelCase_  :Optional[bool]				 =						field(
			    default=_UpperCAmelCase	, 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=0.9_5	, 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 			_SCREAMING_SNAKE_CASE					:
			UpperCAmelCase_  :Optional[int]				 =						field(
			    default=_UpperCAmelCase	, 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=10_0000	, 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=0.2_5	, 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=_UpperCAmelCase	, metadata={"help": "If True, near-duplicate samples are removed."}							)
			UpperCAmelCase_  :Optional[float]				 =						field(
			    default=0.8_5	, metadata={"help": "Jaccard threshold for near-duplicate samples."}							)




@dataclass
class 			_SCREAMING_SNAKE_CASE					:
			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=20_0000	, metadata={"help": "Number of examples to train tokenizer on."}							)
			UpperCAmelCase_  :Optional[int]				 =						field(
			    default=3_2768	, 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=_UpperCAmelCase	, metadata={"help": "Push saved tokenizer to the hub."}							)




@dataclass
class 			_SCREAMING_SNAKE_CASE					:
			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=_UpperCAmelCase	, metadata={"help": "Number of workers used for code evaluation."}							)




@dataclass
class 			_SCREAMING_SNAKE_CASE					:
			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=_UpperCAmelCase	, metadata={"help": "Push saved tokenizer to the hub."}							)










'''simple docstring'''



from math import isqrt



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int							)     ->		bool:
     return all(number % divisor != 0 for divisor in range(2      ,    isqrt(UpperCAmelCase_							) + 1							)							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int = 10**6							)     ->		int:
     __lowerCamelCase   :	Optional[Any]								=		0
     __lowerCamelCase   :	Optional[int]								=		1
     __lowerCamelCase   :	List[str]								=		7
     while prime_candidate < max_prime:
          primes_count += is_prime(UpperCAmelCase_							)

          cube_index += 1
          prime_candidate += 6 * cube_index

     return primes_count


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






