
"""simple docstring"""



from dataclasses import dataclass, field
from typing import Optional
@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



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






@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="codeparrot/codeparrot"      ,				metadata={"help": "Model name or path of model to be evaluated."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="codeparrot/codeparrot-clean-valid"      ,				metadata={"help": "Name or path of validation dataset."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=2      ,				metadata={"help": "Batch size used for evaluation."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=-1      ,				metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=1_0_2_4      ,				metadata={"help": "Length of sequences to be evaluated."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=1      ,				metadata={"help": "Random seed used for evaluation."}							)






@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="codeparrot/codeparrot"      ,				metadata={"help": "Model name or path of model to be evaluated."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=UpperCAmelCase      ,				metadata={"help": "Number of workers used for code evaluation."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=UpperCAmelCase      ,				metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}      ,				)
				SCREAMING_SNAKE_CASE_:							Optional[bool]							=					field(
				    default=UpperCAmelCase      ,				metadata={"help": "Sample from the language model's output distribution."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(default=0.2      ,				metadata={"help": "Sampling temperature used for generation."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=2_5_6      ,				metadata={"help": "Maximum number of newly generated tokens."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=0      ,				metadata={"help": "Top-k parameter used for generation."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(default=0.9_5      ,				metadata={"help": "Top-p parameter used for nucleus sampling."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=1_0      ,				metadata={"help": "Number of generations to run in parallel."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=2_0_0      ,				metadata={"help": "Number of completions to generate for each sample."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=1      ,				metadata={"help": "Random seed used for evaluation."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="eval_results.json"      ,				metadata={"help": "Random seed used for evaluation."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="0"      ,				metadata={"help": "Allow `code_eval` to execute Python code on machine"}							)
				SCREAMING_SNAKE_CASE_:							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  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=UpperCAmelCase      ,				metadata={
				        "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
				    }      ,				)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="transformersbook/codeparrot"      ,				metadata={"help": "Folder or name of dataset to process."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="codeparrot-clean"      ,				metadata={"help": "Folder to save processed processed dataset."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=1_0_0_0_0_0      ,				metadata={"help": "Number of files to save per JSON output file."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(default="content"      ,				metadata={"help": "Column containing text data to process."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(
				    default=1_0_0_0      ,				metadata={"help": "Maximum line length in file, otherwise file is filtered."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(
				    default=1_0_0      ,				metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(
				    default=0.2_5      ,				metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(
				    default=1.5      ,				metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(
				    default=0.7      ,				metadata={"help": "Probability for filtering config, test and uncommon files."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="codeparrot/codeparrot"      ,				metadata={"help": "Name or path to the tokenizer."}      ,				)
				SCREAMING_SNAKE_CASE_:							Optional[bool]							=					field(
				    default=UpperCAmelCase      ,				metadata={"help": "If True, near-duplicate samples are removed."}							)
				SCREAMING_SNAKE_CASE_:							Optional[float]							=					field(
				    default=0.8_5      ,				metadata={"help": "Jaccard threshold for near-duplicate samples."}							)






@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="gpt2"      ,				metadata={"help": "Base tokenizer to build new tokenizer from."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default="transformersbook/codeparrot-train"      ,				metadata={"help": "Dataset to train tokenizer on."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(default="content"      ,				metadata={"help": "Column containing text data to process."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(default=2_0_0_0_0_0      ,				metadata={"help": "Number of examples to train tokenizer on."}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=3_2_7_6_8      ,				metadata={"help": "Number of examples to train the tokenizer on."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(default="codeparrot"      ,				metadata={"help": "Name of new tokenizer."}							)
				SCREAMING_SNAKE_CASE_:							Optional[bool]							=					field(default=UpperCAmelCase      ,				metadata={"help": "Push saved tokenizer to the hub."}							)






@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



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






@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



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








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[int(SCREAMING_SNAKE_CASE						) for i in ip_va_address.split('.'						) if i.isdigit()]
							return len(SCREAMING_SNAKE_CASE						) == 4 and all(0 <= int(SCREAMING_SNAKE_CASE						) <= 254 for octet in octets						)


if __name__ == "__main__":
	_snake_case          =						input().strip()
	_snake_case          =						'''valid''' if is_ip_va_address_valid(ip) else '''invalid'''
	print(f'{ip} is a {valid_or_invalid} IP v4 address.')






