


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=10000					,						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=750					,						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=_lowercase					,						metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''})
	_a  :  Optional[int]				   =							field(default=50000					,						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=1024					,						metadata={'''help''': '''Sequence lengths used for training.'''})
	_a  :  Optional[int]				   =							field(default=1					,						metadata={'''help''': '''Training seed.'''})
	_a  :  Optional[int]				   =							field(
	    default=1024					,						metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}					,						)
	_a  :  Optional[str]				   =							field(
	    default=_lowercase					,						metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''})
	_a  :  Optional[bool]				   =							field(default=_lowercase					,						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=1024					,						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=_lowercase					,						metadata={'''help''': '''Number of workers used for code evaluation.'''})
	_a  :  Optional[int]				   =							field(
	    default=_lowercase					,						metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}					,						)
	_a  :  Optional[bool]				   =							field(
	    default=_lowercase					,						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=256					,						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=200					,						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=_lowercase					,						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=100000					,						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=1000					,						metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''})
	_a  :  Optional[float]				   =							field(
	    default=100					,						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=_lowercase					,						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=200000					,						metadata={'''help''': '''Number of examples to train tokenizer on.'''})
	_a  :  Optional[int]				   =							field(
	    default=32768					,						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=_lowercase					,						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=_lowercase					,						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=_lowercase					,						metadata={'''help''': '''Push saved tokenizer to the hub.'''})



# using dfs for finding eulerian path traversal






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[Any]   					= (path or []) + [u]
		for v in graph[u]:
				if visited_edge[u][v] is False:
						lowerCAmelCase__					,     lowerCAmelCase__       :      List[Any]   					= True, True
						lowerCAmelCase__       :      Any   					= dfs(_a     ,       _a     ,       _a     ,       _a	)
		return path






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Union[str, Any]   					= 0
		lowerCAmelCase__       :      str   					= -1
		for i in range(_a	):
				if i not in graph.keys():
						continue
				if len(graph[i]	) % 2 == 1:
						odd_degree_nodes += 1
						lowerCAmelCase__       :      Tuple   					= i
		if odd_degree_nodes == 0:
				return 1, odd_node
		if odd_degree_nodes == 2:
				return 2, odd_node
		return 3, odd_node






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[Any]   					= [[False for _ in range(max_node + 1	)] for _ in range(max_node + 1	)]
		lowerCAmelCase__					,     lowerCAmelCase__       :      Optional[int]   					= check_circuit_or_path(_a     ,       _a	)
		if check == 3:
				print('''graph is not Eulerian'''	)
				print('''no path'''	)
				return
		lowerCAmelCase__       :      Optional[int]   					= 1
		if check == 2:
				lowerCAmelCase__       :      Any   					= odd_node
				print('''graph has a Euler path'''	)
		if check == 1:
				print('''graph has a Euler cycle'''	)
		lowerCAmelCase__       :      Optional[int]   					= dfs(_a     ,       _a     ,       _a	)
		print(_a	)






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
		lowerCAmelCase__       :      Tuple   					= {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
		lowerCAmelCase__       :      str   					= {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
		lowerCAmelCase__       :      List[str]   					= {1: [2, 3], 2: [1, 3], 3: [1, 2]}
		lowerCAmelCase__       :      List[Any]   					= {
		    1: [],
		    2: []
		    # all degree is zero
		}
		lowerCAmelCase__       :      Optional[Any]   					= 10
		check_euler(_a     ,       _a	)
		check_euler(_a     ,       _a	)
		check_euler(_a     ,       _a	)
		check_euler(_a     ,       _a	)
		check_euler(_a     ,       _a	)


if __name__ == "__main__":
		main()




