from dataclasses import dataclass, field
from typing import Optional


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






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






@dataclass
class 						a__							:
			A__    :       Optional[str]        =      field(
			    default='codeparrot/codeparrot'				,						metadata={'help': 'Model name or path of model to be evaluated.'}		)
			A__    :       Optional[int]        =      field(default=__snake_case				,						metadata={'help': 'Number of workers used for code evaluation.'}		)
			A__    :       Optional[int]        =      field(
			    default=__snake_case				,						metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'}				,						)
			A__    :       Optional[bool]        =      field(
			    default=__snake_case				,						metadata={'help': 'Sample from the language model\'s output distribution.'}		)
			A__    :       Optional[float]        =      field(default=0.2				,						metadata={'help': 'Sampling temperature used for generation.'}		)
			A__    :       Optional[int]        =      field(default=2_56				,						metadata={'help': 'Maximum number of newly generated tokens.'}		)
			A__    :       Optional[int]        =      field(default=0				,						metadata={'help': 'Top-k parameter used for generation.'}		)
			A__    :       Optional[float]        =      field(default=0.95				,						metadata={'help': 'Top-p parameter used for nucleus sampling.'}		)
			A__    :       Optional[int]        =      field(default=10				,						metadata={'help': 'Number of generations to run in parallel.'}		)
			A__    :       Optional[int]        =      field(
			    default=2_00				,						metadata={'help': 'Number of completions to generate for each sample.'}		)
			A__    :       Optional[int]        =      field(default=1				,						metadata={'help': 'Random seed used for evaluation.'}		)
			A__    :       Optional[str]        =      field(
			    default='eval_results.json'				,						metadata={'help': 'Random seed used for evaluation.'}		)
			A__    :       Optional[str]        =      field(
			    default='0'				,						metadata={'help': 'Allow `code_eval` to execute Python code on machine'}		)
			A__    :       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__							:
			A__    :       Optional[int]        =      field(
			    default=__snake_case				,						metadata={
			        'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
			    }				,						)
			A__    :       Optional[str]        =      field(
			    default='transformersbook/codeparrot'				,						metadata={'help': 'Folder or name of dataset to process.'}		)
			A__    :       Optional[str]        =      field(
			    default='codeparrot-clean'				,						metadata={'help': 'Folder to save processed processed dataset.'}		)
			A__    :       Optional[int]        =      field(
			    default=10_00_00				,						metadata={'help': 'Number of files to save per JSON output file.'}		)
			A__    :       Optional[str]        =      field(default='content'				,						metadata={'help': 'Column containing text data to process.'}		)
			A__    :       Optional[float]        =      field(
			    default=10_00				,						metadata={'help': 'Maximum line length in file, otherwise file is filtered.'}		)
			A__    :       Optional[float]        =      field(
			    default=1_00				,						metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'}		)
			A__    :       Optional[float]        =      field(
			    default=0.25				,						metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'}		)
			A__    :       Optional[float]        =      field(
			    default=1.5				,						metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'}		)
			A__    :       Optional[float]        =      field(
			    default=0.7				,						metadata={'help': 'Probability for filtering config, test and uncommon files.'}		)
			A__    :       Optional[str]        =      field(
			    default='codeparrot/codeparrot'				,						metadata={'help': 'Name or path to the tokenizer.'}				,						)
			A__    :       Optional[bool]        =      field(
			    default=__snake_case				,						metadata={'help': 'If True, near-duplicate samples are removed.'}		)
			A__    :       Optional[float]        =      field(
			    default=0.85				,						metadata={'help': 'Jaccard threshold for near-duplicate samples.'}		)






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






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






@dataclass
class 						a__							:
			A__    :       Optional[str]        =      field(
			    default='gpt2-large'				,						metadata={'help': 'Configuration to use for model initialization.'}		)
			A__    :       Optional[str]        =      field(
			    default='codeparrot/codeparrot'				,						metadata={'help': 'Tokenizer attached to model.'}		)
			A__    :       Optional[str]        =      field(default='codeparrot'				,						metadata={'help': 'Name of the created model.'}		)
			A__    :       Optional[bool]        =      field(default=__snake_case				,						metadata={'help': 'Push saved tokenizer to the hub.'}		)





from __future__ import annotations

import numpy as np
def 							lowerCAmelCase(     __lowerCamelCase						):
					return np.maximum(0				,    __lowerCamelCase						)


if __name__ == "__main__":
	print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]




