


'''simple docstring'''







from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

A				:					Optional[int]    						=			TypeVar('''T''')






def 				lowerCAmelCase__	(    lowerCamelCase					:      int       ):
       return (position - 1) // 2






def 				lowerCAmelCase__	(    lowerCamelCase					:      int       ):
       return (2 * position) + 1






def 				lowerCAmelCase__	(    lowerCamelCase					:      int       ):
       return (2 * position) + 2


class 			__lowerCamelCase			(	Generic[T]	):
    """simple docstring"""


    def __init__(  self      :							str):
           _A					:     list[tuple[T, int]]     = []
           _A					:     dict[T, int]     = {}
           _A					:     int     = 0


    def __len__(  self      :							Optional[int]):
           return self.elements


    def __repr__(  self      :							str):
           return str(self.heap)


    def        A   (  self      :							Union[str, Any]):
           # Check if the priority queue is empty
           return self.elements == 0


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							T				,							SCREAMING_SNAKE_CASE      :							int):
           # Add an element with given priority to the queue
           self.heap.append((elem, weight))
           _A					:     Optional[Any]     = self.elements
           self.elements += 1
           self._bubble_up(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							str):
           # Remove and return the element with lowest weight (highest priority)
           if self.elements > 1:
                  self._swap_nodes(0				,							self.elements - 1)
           _A  ,			_A					:     str     = self.heap.pop()
           del self.position_map[elem]
           self.elements -= 1
           if self.elements > 0:
                  _A  ,			_A					:     Optional[Any]     = self.heap[0]
                  self._bubble_down(SCREAMING_SNAKE_CASE)
           return elem


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							T				,							SCREAMING_SNAKE_CASE      :							int):
           # Update the weight of the given key
           _A					:     Tuple     = self.position_map[elem]
           _A					:     int     = (elem, weight)
           if position > 0:
                  _A					:     List[Any]     = get_parent_position(SCREAMING_SNAKE_CASE)
                  _A  ,			_A					:     List[Any]     = self.heap[parent_position]
                  if parent_weight > weight:
                         self._bubble_up(SCREAMING_SNAKE_CASE)
                  else:
                         self._bubble_down(SCREAMING_SNAKE_CASE)
           else:
                  self._bubble_down(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							T):
           # Place a node at the proper position (upward movement) [to be used internally
           # only]
           _A					:     Any     = self.position_map[elem]
           if curr_pos == 0:
                  return None
           _A					:     Dict     = get_parent_position(SCREAMING_SNAKE_CASE)
           _A  ,			_A					:     Tuple     = self.heap[curr_pos]
           _A  ,			_A					:     Optional[Any]     = self.heap[parent_position]
           if parent_weight > weight:
                  self._swap_nodes(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                  return self._bubble_up(SCREAMING_SNAKE_CASE)
           return None


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							T):
           # Place a node at the proper position (downward movement) [to be used
           # internally only]
           _A					:     str     = self.position_map[elem]
           _A  ,			_A					:     int     = self.heap[curr_pos]
           _A					:     Optional[int]     = get_child_left_position(SCREAMING_SNAKE_CASE)
           _A					:     int     = get_child_right_position(SCREAMING_SNAKE_CASE)
           if child_left_position < self.elements and child_right_position < self.elements:
                  _A  ,			_A					:     Tuple     = self.heap[child_left_position]
                  _A  ,			_A					:     str     = self.heap[child_right_position]
                  if child_right_weight < child_left_weight and child_right_weight < weight:
                         self._swap_nodes(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                         return self._bubble_down(SCREAMING_SNAKE_CASE)
           if child_left_position < self.elements:
                  _A  ,			_A					:     str     = self.heap[child_left_position]
                  if child_left_weight < weight:
                         self._swap_nodes(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                         return self._bubble_down(SCREAMING_SNAKE_CASE)
           else:
                  return None
           if child_right_position < self.elements:
                  _A  ,			_A					:     Dict     = self.heap[child_right_position]
                  if child_right_weight < weight:
                         self._swap_nodes(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                         return self._bubble_down(SCREAMING_SNAKE_CASE)
           return None


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int):
           # Swap the nodes at the given positions
           _A					:     List[str]     = self.heap[nodea_pos][0]
           _A					:     List[Any]     = self.heap[nodea_pos][0]
           _A  ,			_A					:     Optional[int]     = (
               self.heap[nodea_pos],
               self.heap[nodea_pos],
           )
           _A					:     int     = nodea_pos
           _A					:     List[str]     = nodea_pos




class 			__lowerCamelCase			(	Generic[T]	):
    """simple docstring"""


    def __init__(  self      :							List[str]):
           _A					:     dict[T, dict[T, int]]     = {}
           _A					:     int     = 0


    def __repr__(  self      :							Union[str, Any]):
           return str(self.connections)


    def __len__(  self      :							List[str]):
           return self.nodes


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							T):
           # Add a node in the graph if it is not in the graph
           if node not in self.connections:
                  _A					:     List[Any]     = {}
                  self.nodes += 1


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							T				,							SCREAMING_SNAKE_CASE      :							T				,							SCREAMING_SNAKE_CASE      :							int):
           # Add an edge between 2 nodes in the graph
           self.add_node(SCREAMING_SNAKE_CASE)
           self.add_node(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = weight
           _A					:     str     = weight






def 				lowerCAmelCase__	(    lowerCamelCase					:      GraphUndirectedWeighted[T]  ,):
       _A					:     dict[T, int]     = {node: maxsize for node in graph.connections}
       _A					:     dict[T, T | None]     = {node: None for node in graph.connections}

       _A					:     MinPriorityQueue[T]     = MinPriorityQueue()
       for node, weight in dist.items():
              priority_queue.push(lowerCamelCase  ,lowerCamelCase       )

       if priority_queue.is_empty():
              return dist, parent

       # initialization
       _A					:     Any     = priority_queue.extract_min()
       _A					:     Union[str, Any]     = 0
       for neighbour in graph.connections[node]:
              if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                     _A					:     Union[str, Any]     = dist[node] + graph.connections[node][neighbour]
                     priority_queue.update_key(lowerCamelCase  ,dist[neighbour]       )
                     _A					:     Optional[int]     = node

    # running prim's algorithm
       while not priority_queue.is_empty():
              _A					:     List[str]     = priority_queue.extract_min()
              for neighbour in graph.connections[node]:
                     if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                            _A					:     Optional[int]     = dist[node] + graph.connections[node][neighbour]
                            priority_queue.update_key(lowerCamelCase  ,dist[neighbour]       )
                            _A					:     List[str]     = node
       return dist, parent










'''simple docstring'''







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_0000    , 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=750    , metadata={"help": "Number of warmup steps in the learning rate schedule."}	)
    a													=   field(
        default=16    , metadata={"help": "Number of gradient accumulation steps."}	)
    a													=   field(
        default=a_    , metadata={"help": "Use gradient checkpointing to reduce memory footprint."}	)
    a													=   field(default=5_0000    , 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=1024    , metadata={"help": "Sequence lengths used for training."}	)
    a													=   field(default=1    , metadata={"help": "Training seed."}	)
    a													=   field(
        default=1024    , metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}    , )
    a													=   field(
        default=a_    , metadata={"help": "States path if the training should continue from a checkpoint folder."}	)
    a													=   field(default=a_    , 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=1024    , 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=a_    , metadata={"help": "Number of workers used for code evaluation."}	)
    a													=   field(
        default=a_    , metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}    , )
    a													=   field(
        default=a_    , 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=256    , 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=10    , metadata={"help": "Number of generations to run in parallel."}	)
    a													=   field(
        default=200    , 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=a_    , 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=10_0000    , 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=1000    , metadata={"help": "Maximum line length in file, otherwise file is filtered."}	)
    a													=   field(
        default=100    , 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=a_    , 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=20_0000    , metadata={"help": "Number of examples to train tokenizer on."}	)
    a													=   field(
        default=3_2768    , metadata={"help": "Number of examples to train the tokenizer on."}	)
    a													=   field(default="codeparrot"    , metadata={"help": "Name of new tokenizer."}	)
    a													=   field(default=a_    , 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=a_    , 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=a_    , metadata={"help": "Push saved tokenizer to the hub."}	)






