




from __future__ import annotations

from typing import Any





class      snake_case	:


   '''simple docstring'''







   def __init__(       self  :						Optional[Any]    ,     lowerCamelCase_  :						int      )       ->None:
     '''simple docstring'''







     UpperCAmelCase__         = num_of_nodes
     UpperCAmelCase__         = []
     UpperCAmelCase__         = {}




   def 						UpperCAmelCase							(       self  :						Dict    ,     lowerCamelCase_  :						int    ,     lowerCamelCase_  :						int    ,     lowerCamelCase_  :						int      )       ->None:
     '''simple docstring'''







     self.m_edges.append([u_node, v_node, weight]      )




   def 						UpperCAmelCase							(       self  :						List[str]    ,     lowerCamelCase_  :						int      )       ->int:
     '''simple docstring'''







     if self.m_component[u_node] == u_node:
       return u_node
     return self.find_component(self.m_component[u_node]      )




   def 						UpperCAmelCase							(       self  :						Optional[int]    ,     lowerCamelCase_  :						int      )       ->None:
     '''simple docstring'''







     if self.m_component[u_node] != u_node:
       for k in self.m_component:
         UpperCAmelCase__         = self.find_component(lowerCamelCase_      )




   def 						UpperCAmelCase							(       self  :						Union[str, Any]    ,     lowerCamelCase_  :						list[int]    ,     lowerCamelCase_  :						int    ,     lowerCamelCase_  :						int      )       ->None:
     '''simple docstring'''







     if component_size[u_node] <= component_size[v_node]:
       UpperCAmelCase__         = v_node
       component_size[v_node] += component_size[u_node]
       self.set_component(lowerCamelCase_      )

     elif component_size[u_node] >= component_size[v_node]:
       UpperCAmelCase__         = self.find_component(lowerCamelCase_      )
       component_size[u_node] += component_size[v_node]
       self.set_component(lowerCamelCase_      )







   def 						UpperCAmelCase							(       self  :						Tuple      )       ->None:
     '''simple docstring'''







     UpperCAmelCase__         = []
     UpperCAmelCase__         = 0

     UpperCAmelCase__         = [-1] * self.m_num_of_nodes

     # A list of components (initialized to all of the nodes)
     for node in range(self.m_num_of_nodes      ):
       self.m_component.update({node: node}      )
       component_size.append(1      )

     UpperCAmelCase__         = self.m_num_of_nodes

     while num_of_components > 1:
       for edge in self.m_edges:
         UpperCAmelCase__         = edge

         UpperCAmelCase__         = self.m_component[u]
         UpperCAmelCase__         = self.m_component[v]

         if u_component != v_component:

           for component in (u_component, v_component):
             if (
                 minimum_weight_edge[component] == -1
                 or minimum_weight_edge[component][2] > w
             ):
               UpperCAmelCase__         = [u, v, w]

       for edge in minimum_weight_edge:
         if isinstance(lowerCamelCase_    ,     lowerCamelCase_      ):
           UpperCAmelCase__         = edge

           UpperCAmelCase__         = self.m_component[u]
           UpperCAmelCase__         = self.m_component[v]

           if u_component != v_component:
             mst_weight += w
             self.union(lowerCamelCase_    ,     lowerCamelCase_    ,     lowerCamelCase_      )
             print(f'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''      )
             num_of_components -= 1

       UpperCAmelCase__         = [-1] * self.m_num_of_nodes
     print(f'''The total weight of the minimal spanning tree is: {mst_weight}'''      )



def     lowercase		(  ):
  pass



if __name__ == "__main__":
   import doctest

   doctest.testmod()






from dataclasses import dataclass, field
from typing import Optional




@dataclass
class   __lowercase						:
	_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=10000   , 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=__snake_case   , metadata={"help": "Use gradient checkpointing to reduce memory footprint."}       )
	_A       =   field(default=50000   , 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=__snake_case   , metadata={"help": "States path if the training should continue from a checkpoint folder."}       )
	_A       =   field(default=__snake_case   , metadata={"help": "If True the data is pretokenized."}       )




@dataclass
class   __lowercase						:
	_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   __lowercase						:
	_A       =   field(
	    default="codeparrot/codeparrot"   , metadata={"help": "Model name or path of model to be evaluated."}       )
	_A       =   field(default=__snake_case   , metadata={"help": "Number of workers used for code evaluation."}       )
	_A       =   field(
	    default=__snake_case   , metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}   , )
	_A       =   field(
	    default=__snake_case   , 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   __lowercase						:
	_A       =   field(
	    default=__snake_case   , 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=100000   , 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=__snake_case   , metadata={"help": "If True, near-duplicate samples are removed."}       )
	_A       =   field(
	    default=0.85   , metadata={"help": "Jaccard threshold for near-duplicate samples."}       )




@dataclass
class   __lowercase						:
	_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=200000   , metadata={"help": "Number of examples to train tokenizer on."}       )
	_A       =   field(
	    default=32768   , metadata={"help": "Number of examples to train the tokenizer on."}       )
	_A       =   field(default="codeparrot"   , metadata={"help": "Name of new tokenizer."}       )
	_A       =   field(default=__snake_case   , metadata={"help": "Push saved tokenizer to the hub."}       )




@dataclass
class   __lowercase						:
	_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=__snake_case   , metadata={"help": "Number of workers used for code evaluation."}       )




@dataclass
class   __lowercase						:
	_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=__snake_case   , metadata={"help": "Push saved tokenizer to the hub."}       )




