





from dataclasses import dataclass, field
from typing import Optional

@dataclass
class 		snake_case_   :







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





@dataclass
class 		snake_case_   :







	'''simple docstring'''
	__UpperCamelCase   =		field(
	    default='''codeparrot/codeparrot'''     ,     metadata={'''help''': '''Model name or path of model to be evaluated.'''}						)
	__UpperCamelCase   =		field(
	    default='''codeparrot/codeparrot-clean-valid'''     ,     metadata={'''help''': '''Name or path of validation dataset.'''}						)
	__UpperCamelCase   =		field(default=2     ,     metadata={'''help''': '''Batch size used for evaluation.'''}						)
	__UpperCamelCase   =		field(
	    default=-1     ,     metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}						)
	__UpperCamelCase   =		field(default=10_24     ,     metadata={'''help''': '''Length of sequences to be evaluated.'''}						)
	__UpperCamelCase   =		field(default=1     ,     metadata={'''help''': '''Random seed used for evaluation.'''}						)





@dataclass
class 		snake_case_   :







	'''simple docstring'''
	__UpperCamelCase   =		field(
	    default='''codeparrot/codeparrot'''     ,     metadata={'''help''': '''Model name or path of model to be evaluated.'''}						)
	__UpperCamelCase   =		field(default=UpperCAmelCase_     ,     metadata={'''help''': '''Number of workers used for code evaluation.'''}						)
	__UpperCamelCase   =		field(
	    default=UpperCAmelCase_     ,     metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}     ,     )
	__UpperCamelCase   =		field(
	    default=UpperCAmelCase_     ,     metadata={'''help''': '''Sample from the language model\'s output distribution.'''}						)
	__UpperCamelCase   =		field(default=0.2     ,     metadata={'''help''': '''Sampling temperature used for generation.'''}						)
	__UpperCamelCase   =		field(default=2_56     ,     metadata={'''help''': '''Maximum number of newly generated tokens.'''}						)
	__UpperCamelCase   =		field(default=0     ,     metadata={'''help''': '''Top-k parameter used for generation.'''}						)
	__UpperCamelCase   =		field(default=0.9_5     ,     metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}						)
	__UpperCamelCase   =		field(default=10     ,     metadata={'''help''': '''Number of generations to run in parallel.'''}						)
	__UpperCamelCase   =		field(
	    default=2_00     ,     metadata={'''help''': '''Number of completions to generate for each sample.'''}						)
	__UpperCamelCase   =		field(default=1     ,     metadata={'''help''': '''Random seed used for evaluation.'''}						)
	__UpperCamelCase   =		field(
	    default='''eval_results.json'''     ,     metadata={'''help''': '''Random seed used for evaluation.'''}						)
	__UpperCamelCase   =		field(
	    default='''0'''     ,     metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}						)
	__UpperCamelCase   =		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 		snake_case_   :







	'''simple docstring'''
	__UpperCamelCase   =		field(
	    default=UpperCAmelCase_     ,     metadata={
	        '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
	    }     ,     )
	__UpperCamelCase   =		field(
	    default='''transformersbook/codeparrot'''     ,     metadata={'''help''': '''Folder or name of dataset to process.'''}						)
	__UpperCamelCase   =		field(
	    default='''codeparrot-clean'''     ,     metadata={'''help''': '''Folder to save processed processed dataset.'''}						)
	__UpperCamelCase   =		field(
	    default=10_00_00     ,     metadata={'''help''': '''Number of files to save per JSON output file.'''}						)
	__UpperCamelCase   =		field(default='''content'''     ,     metadata={'''help''': '''Column containing text data to process.'''}						)
	__UpperCamelCase   =		field(
	    default=10_00     ,     metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}						)
	__UpperCamelCase   =		field(
	    default=1_00     ,     metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}						)
	__UpperCamelCase   =		field(
	    default=0.2_5     ,     metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}						)
	__UpperCamelCase   =		field(
	    default=1.5     ,     metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}						)
	__UpperCamelCase   =		field(
	    default=0.7     ,     metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}						)
	__UpperCamelCase   =		field(
	    default='''codeparrot/codeparrot'''     ,     metadata={'''help''': '''Name or path to the tokenizer.'''}     ,     )
	__UpperCamelCase   =		field(
	    default=UpperCAmelCase_     ,     metadata={'''help''': '''If True, near-duplicate samples are removed.'''}						)
	__UpperCamelCase   =		field(
	    default=0.8_5     ,     metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}						)





@dataclass
class 		snake_case_   :







	'''simple docstring'''
	__UpperCamelCase   =		field(
	    default='''gpt2'''     ,     metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}						)
	__UpperCamelCase   =		field(
	    default='''transformersbook/codeparrot-train'''     ,     metadata={'''help''': '''Dataset to train tokenizer on.'''}						)
	__UpperCamelCase   =		field(default='''content'''     ,     metadata={'''help''': '''Column containing text data to process.'''}						)
	__UpperCamelCase   =		field(default=20_00_00     ,     metadata={'''help''': '''Number of examples to train tokenizer on.'''}						)
	__UpperCamelCase   =		field(
	    default=3_27_68     ,     metadata={'''help''': '''Number of examples to train the tokenizer on.'''}						)
	__UpperCamelCase   =		field(default='''codeparrot'''     ,     metadata={'''help''': '''Name of new tokenizer.'''}						)
	__UpperCamelCase   =		field(default=UpperCAmelCase_     ,     metadata={'''help''': '''Push saved tokenizer to the hub.'''}						)





@dataclass
class 		snake_case_   :







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





@dataclass
class 		snake_case_   :







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











class 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     Dict					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     Optional[Any]=None					,      __lowerCamelCase							:     Optional[Any]=None   )       -> Dict:

			'''simple docstring'''




			__lowercase											=     data
			__lowercase											=     previous
			__lowercase											=     next_node




	def __str__(						self							:     int   )       -> str:

			'''simple docstring'''




			return F"{self.data}"




	def    UpperCAmelCase			(						self							:     int   )       -> int:

			'''simple docstring'''




			return self.data




	def    UpperCAmelCase			(						self							:     Tuple   )       -> Optional[int]:

			'''simple docstring'''




			return self.next






	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> Any:

			'''simple docstring'''




			return self.previous





class 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     Tuple					,      __lowerCamelCase							:     Optional[Any]   )       -> str:

			'''simple docstring'''




			__lowercase											=     head




	def __iter__(						self							:     Union[str, Any]   )       -> List[Any]:

			'''simple docstring'''




			return self






	def    UpperCAmelCase			(						self							:     str   )       -> str:

			'''simple docstring'''




			if not self.current:
					raise StopIteration
			else:
					__lowercase											=     self.current.get_data()
					__lowercase											=     self.current.get_next()
					return value





class 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     List[str]   )       -> Any:

			'''simple docstring'''




			__lowercase											=     None  # First node in list
			__lowercase											=     None  # Last node in list




	def __str__(						self							:     int   )       -> Any:

			'''simple docstring'''




			__lowercase											=     self.head
			__lowercase											=     []
			while current is not None:
					nodes.append(current.get_data()   )
					__lowercase											=     current.get_next()
			return " ".join(str(__lowerCamelCase   ) for node in nodes   )




	def __contains__(						self							:     Tuple					,      __lowerCamelCase							:     int   )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     self.head
			while current:
					if current.get_data() == value:
							return True
					__lowercase											=     current.get_next()
			return False




	def __iter__(						self							:     Optional[int]   )       -> List[str]:

			'''simple docstring'''




			return LinkedListIterator(self.head   )




	def    UpperCAmelCase			(						self							:     Any   )       -> str:

			'''simple docstring'''




			if self.head:
					return self.head.get_data()
			return None




	def    UpperCAmelCase			(						self							:     str   )       -> Union[str, Any]:

			'''simple docstring'''




			if self.tail:
					return self.tail.get_data()
			return None




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     Node   )       -> None:

			'''simple docstring'''




			if self.head is None:
					__lowercase											=     node
					__lowercase											=     node
			else:
					self.insert_before_node(self.head					,      __lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Dict					,      __lowerCamelCase							:     Node   )       -> None:

			'''simple docstring'''




			if self.head is None:
					self.set_head(__lowerCamelCase   )
			else:
					self.insert_after_node(self.tail					,      __lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     Node(__lowerCamelCase   )
			if self.head is None:
					self.set_head(__lowerCamelCase   )
			else:
					self.set_tail(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     Node					,      __lowerCamelCase							:     Node   )       -> None:

			'''simple docstring'''




			__lowercase											=     node
			__lowercase											=     node.previous

			if node.get_previous() is None:
					__lowercase											=     node_to_insert
			else:
					__lowercase											=     node_to_insert

			__lowercase											=     node_to_insert




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     Node					,      __lowerCamelCase							:     Node   )       -> None:

			'''simple docstring'''




			__lowercase											=     node
			__lowercase											=     node.next

			if node.get_next() is None:
					__lowercase											=     node_to_insert
			else:
					__lowercase											=     node_to_insert

			__lowercase											=     node_to_insert




	def    UpperCAmelCase			(						self							:     List[str]					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     1
			__lowercase											=     Node(__lowerCamelCase   )
			__lowercase											=     self.head
			while node:
					if current_position == position:
							self.insert_before_node(__lowerCamelCase					,      __lowerCamelCase   )
							return
					current_position += 1
					__lowercase											=     node.next
			self.insert_after_node(self.tail					,      __lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     int   )       -> Node:

			'''simple docstring'''




			__lowercase											=     self.head
			while node:
					if node.get_data() == item:
							return node
					__lowercase											=     node.get_next()
			raise Exception('Node not found'   )




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     List[str]   )       -> Dict:

			'''simple docstring'''




			if (node := self.get_node(__lowerCamelCase   )) is not None:
					if node == self.head:
							__lowercase											=     self.head.get_next()

					if node == self.tail:
							__lowercase											=     self.tail.get_previous()

					self.remove_node_pointers(__lowerCamelCase   )




	@staticmethod
	def    UpperCAmelCase			(						__lowerCamelCase							:     Node   )       -> None:

			'''simple docstring'''




			if node.get_next():
					__lowercase											=     node.previous

			if node.get_previous():
					__lowercase											=     node.next

			__lowercase											=     None
			__lowercase											=     None






	def    UpperCAmelCase			(						self							:     str   )       -> List[str]:

			'''simple docstring'''




			return self.head is None





def  SCREAMING_SNAKE_CASE				(      )		->    None:
		pass


if __name__ == "__main__":
		import doctest

		doctest.testmod()




