


'''simple docstring'''


from dataclasses import dataclass, field
from typing import Optional





@dataclass
class     lowercase__  :





							'''simple docstring'''


							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_0000     ,     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=_snake_case     ,     metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}					)
							A_							:      Optional[int]      	=  field(default=5_0000     ,     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=_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     lowercase__  :





							'''simple docstring'''


							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     lowercase__  :





							'''simple docstring'''


							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=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     lowercase__  :





							'''simple docstring'''


							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_0000     ,     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=_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     lowercase__  :





							'''simple docstring'''


							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_0000     ,     metadata={"""help""": """Number of examples to train tokenizer on."""}					)
							A_							:      Optional[int]      	=  field(
							    default=3_2768     ,     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     lowercase__  :





							'''simple docstring'''


							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     lowercase__  :





							'''simple docstring'''


							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."""}					)








'''simple docstring'''


class     lowercase__  :





							'''simple docstring'''


							def __init__(    self    ,					__snake_case = ""    ,					__snake_case = False  ):
											# Mapping from the first character of the prefix of the node
											_SCREAMING_SNAKE_CASE :       dict[str, RadixNode]     		=      {}

											# A node will be a leaf if the tree contains its word
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      is_leaf

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      prefix






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      0
											for q, w in zip(self.prefix    ,					__snake_case  ):
															if q != w:
																			break

															x += 1

											return self.prefix[:x], self.prefix[x:], word[x:]






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											for word in words:
															self.insert(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											# Case 1: If the word is the prefix of the node
											# Solution: We set the current node as leaf
											if self.prefix == word:
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      True

											# Case 2: The node has no edges that have a prefix to the word
											# Solution: We create an edge from the current node to a new one
											# containing the word
											elif word[0] not in self.nodes:
															_SCREAMING_SNAKE_CASE :       List[str]     		=      RadixNode(prefix=__snake_case    ,					is_leaf=__snake_case  )

											else:
															_SCREAMING_SNAKE_CASE :       int     		=      self.nodes[word[0]]
															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Optional[Any]     		=      incoming_node.match(
															    __snake_case  )

															# Case 3: The node prefix is equal to the matching
															# Solution: We insert remaining word on the next node
															if remaining_prefix == "":
																			self.nodes[matching_string[0]].insert(__snake_case  )

															# Case 4: The word is greater equal to the matching
															# Solution: Create a node in between both nodes, change
															# prefixes and add the new node for the remaining word
															else:
																			_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      remaining_prefix

																			_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.nodes[matching_string[0]]
																			_SCREAMING_SNAKE_CASE :       List[Any]     		=      RadixNode(__snake_case    ,					__snake_case  )
																			_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      aux_node

																			if remaining_word == "":
																							_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      True
																			else:
																							self.nodes[matching_string[0]].insert(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.nodes.get(word[0]    ,					__snake_case  )
											if not incoming_node:
															return False
											else:
															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Tuple     		=      incoming_node.match(
															    __snake_case  )
															# If there is remaining prefix, the word can't be on the tree
															if remaining_prefix != "":
																			return False
															# This applies when the word and the prefix are equal
															elif remaining_word == "":
																			return incoming_node.is_leaf
															# We have word remaining so we check the next node
															else:
																			return incoming_node.find(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.nodes.get(word[0]    ,					__snake_case  )
											if not incoming_node:
															return False
											else:
															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[Any]     		=      incoming_node.match(
															    __snake_case  )
															# If there is remaining prefix, the word can't be on the tree
															if remaining_prefix != "":
																			return False
															# We have word remaining so we check the next node
															elif remaining_word != "":
																			return incoming_node.delete(__snake_case  )
															else:
																			# If it is not a leaf, we don't have to delete
																			if not incoming_node.is_leaf:
																							return False
																			else:
																							# We delete the nodes if no edges go from it
																							if len(incoming_node.nodes  ) == 0:
																											del self.nodes[word[0]]
																											# We merge the current node with its only child
																											if len(self.nodes  ) == 1 and not self.is_leaf:
																															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      list(self.nodes.values()  )[0]
																															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      merging_node.is_leaf
																															self.prefix += merging_node.prefix
																															_SCREAMING_SNAKE_CASE :       Tuple     		=      merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																							elif len(incoming_node.nodes  ) > 1:
																											_SCREAMING_SNAKE_CASE :       List[str]     		=      False
																							# If there is 1 edge, we merge it with its child
																							else:
																											_SCREAMING_SNAKE_CASE :       int     		=      list(incoming_node.nodes.values()  )[0]
																											_SCREAMING_SNAKE_CASE :       Tuple     		=      merging_node.is_leaf
																											incoming_node.prefix += merging_node.prefix
																											_SCREAMING_SNAKE_CASE :       str     		=      merging_node.nodes

																							return True






							def UpperCAmelCase_	(    self    ,					__snake_case = 0  ):
											if self.prefix != "":
															print("""-""" * height    ,					self.prefix    ,					"""  (leaf)""" if self.is_leaf else """"""  )

											for value in self.nodes.values():
															value.print_tree(height + 1  )







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      """banana bananas bandana band apple all beast""".split()
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      RadixNode()
				root.insert_many(SCREAMING_SNAKE_CASE__     )

				assert all(root.find(SCREAMING_SNAKE_CASE__     ) for word in words     )
				assert not root.find("""bandanas"""     )
				assert not root.find("""apps"""     )
				root.delete("""all"""     )
				assert not root.find("""all"""     )
				root.delete("""banana"""     )
				assert not root.find("""banana"""     )
				assert root.find("""bananas"""     )

				return True







def 			snake_case_   (  ):







				"""simple docstring"""





				assert test_trie()







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Tuple     		=      RadixNode()
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      """banana bananas bandanas bandana band apple all beast""".split()
				root.insert_many(SCREAMING_SNAKE_CASE__     )

				print("""Words:"""							,      SCREAMING_SNAKE_CASE__     )
				print("""Tree:"""     )
				root.print_tree()


if __name__ == "__main__":
				main()




