
from dataclasses import dataclass, field
from typing import Optional




@dataclass
class  lowerCamelCase__							:




	'''simple docstring'''




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




@dataclass
class  lowerCamelCase__							:




	'''simple docstring'''




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




@dataclass
class  lowerCamelCase__							:




	'''simple docstring'''




	_A								=     field(
	    default='codeparrot/codeparrot'     ,						metadata={'help': 'Model name or path of model to be evaluated.'})
	_A								=     field(default=__lowercase     ,						metadata={'help': 'Number of workers used for code evaluation.'})
	_A								=     field(
	    default=__lowercase     ,						metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'}     ,						)
	_A								=     field(
	    default=__lowercase     ,						metadata={'help': 'Sample from the language model\'s output distribution.'})
	_A								=     field(default=0.2     ,						metadata={'help': 'Sampling temperature used for generation.'})
	_A								=     field(default=2_5_6     ,						metadata={'help': 'Maximum number of newly generated tokens.'})
	_A								=     field(default=0     ,						metadata={'help': 'Top-k parameter used for generation.'})
	_A								=     field(default=0.95     ,						metadata={'help': 'Top-p parameter used for nucleus sampling.'})
	_A								=     field(default=1_0     ,						metadata={'help': 'Number of generations to run in parallel.'})
	_A								=     field(
	    default=2_0_0     ,						metadata={'help': 'Number of completions to generate for each sample.'})
	_A								=     field(default=1     ,						metadata={'help': 'Random seed used for evaluation.'})
	_A								=     field(
	    default='eval_results.json'     ,						metadata={'help': 'Random seed used for evaluation.'})
	_A								=     field(
	    default='0'     ,						metadata={'help': 'Allow `code_eval` to execute Python code on machine'})
	_A								=     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  lowerCamelCase__							:




	'''simple docstring'''




	_A								=     field(
	    default=__lowercase     ,						metadata={
	        'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
	    }     ,						)
	_A								=     field(
	    default='transformersbook/codeparrot'     ,						metadata={'help': 'Folder or name of dataset to process.'})
	_A								=     field(
	    default='codeparrot-clean'     ,						metadata={'help': 'Folder to save processed processed dataset.'})
	_A								=     field(
	    default=1_0_0_0_0_0     ,						metadata={'help': 'Number of files to save per JSON output file.'})
	_A								=     field(default='content'     ,						metadata={'help': 'Column containing text data to process.'})
	_A								=     field(
	    default=1_0_0_0     ,						metadata={'help': 'Maximum line length in file, otherwise file is filtered.'})
	_A								=     field(
	    default=1_0_0     ,						metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'})
	_A								=     field(
	    default=0.25     ,						metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'})
	_A								=     field(
	    default=1.5     ,						metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'})
	_A								=     field(
	    default=0.7     ,						metadata={'help': 'Probability for filtering config, test and uncommon files.'})
	_A								=     field(
	    default='codeparrot/codeparrot'     ,						metadata={'help': 'Name or path to the tokenizer.'}     ,						)
	_A								=     field(
	    default=__lowercase     ,						metadata={'help': 'If True, near-duplicate samples are removed.'})
	_A								=     field(
	    default=0.85     ,						metadata={'help': 'Jaccard threshold for near-duplicate samples.'})




@dataclass
class  lowerCamelCase__							:




	'''simple docstring'''




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




@dataclass
class  lowerCamelCase__							:




	'''simple docstring'''




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




@dataclass
class  lowerCamelCase__							:




	'''simple docstring'''




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





def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       int	,							lowercase	:       int     ):


      '''simple docstring'''



      return int((input_a, input_a).count(1     ) != 0     )

def 			_SCREAMING_SNAKE_CASE			(							):


      '''simple docstring'''



      assert or_gate(0	,							0     ) == 0
      assert or_gate(0	,							1     ) == 1
      assert or_gate(1	,							0     ) == 1
      assert or_gate(1	,							1     ) == 1


if __name__ == "__main__":
 print(or_gate(0, 1))
 print(or_gate(1, 0))
 print(or_gate(0, 0))
 print(or_gate(1, 1))



