






import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


__snake_case     :     int       =  "\\n@article{eddine2021frugalscore,\n  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},\n  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},\n  journal={arXiv preprint arXiv:2110.08559},\n  year={2021}\n}\n"

__snake_case     :     Optional[Any]       =  "\\nFrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.\n"


__snake_case     :     Optional[Any]       =  "\nCalculates how good are predictions given some references, using certain scores.\nArgs:\n    predictions (list of str): list of predictions to score. Each predictions\n        should be a string.\n    references (list of str): list of reference for each prediction. Each\n        reference should be a string.\n    batch_size (int): the batch size for predictions.\n    max_length (int): maximum sequence length.\n    device (str): either gpu or cpu\nReturns:\n    scores (list of int): list of scores.\nExamples:\n    >>> frugalscore = datasets.load_metric(\"frugalscore\")\n    >>> results = frugalscore.compute(predictions=['hello there', 'huggingface'], references=['hello world', 'hugging face'])\n    >>> print([round(s, 3) for s in results[\"scores\"]])\n    [0.631, 0.645]\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,       _KWARGS_DESCRIPTION)
class      __SCREAMING_SNAKE_CASE       ( datasets.Metric):






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return datasets.MetricInfo(
												    description=_DESCRIPTION  ,    citation=_CITATION  ,    inputs_description=_KWARGS_DESCRIPTION  ,    features=datasets.Features(
												        {
												            'predictions': datasets.Value('string' ),
												            'references': datasets.Value('string' ),
												        } )  ,    homepage='https://github.com/moussaKam/FrugalScore'  ,    )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												if self.config_name == "default":
																		lowerCAmelCase__         =    'moussaKam/frugalscore_tiny_bert-base_bert-score'
												else:
																		lowerCAmelCase__         =    self.config_name
												lowerCAmelCase__         =    AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_ )
												lowerCAmelCase__         =    AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase=32  ,    _UpperCamelCase=1_28  ,    _UpperCamelCase=None  ,    ):






												"""simple docstring"""
												assert len(SCREAMING_SNAKE_CASE_ ) == len(
												    SCREAMING_SNAKE_CASE_ ), "predictions and references should have the same number of sentences."
												if device is not None:
																		assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
												else:
																		lowerCAmelCase__         =    'gpu' if torch.cuda.is_available() else 'cpu'
												lowerCAmelCase__         =    TrainingArguments(
												    'trainer'  ,    fpaa=(device == 'gpu')  ,    per_device_eval_batch_size=SCREAMING_SNAKE_CASE_  ,    report_to='all'  ,    no_cuda=(device == 'cpu')  ,    log_level='warning'  ,    )
												lowerCAmelCase__         =    {'sentence1': predictions, 'sentence2': references}
												lowerCAmelCase__         =    datasets.Dataset.from_dict(SCREAMING_SNAKE_CASE_ )

												def tokenize_function(_UpperCamelCase ):
																		return self.tokenizer(
																		    data['sentence1']  ,    data['sentence2']  ,    max_length=SCREAMING_SNAKE_CASE_  ,    truncation=SCREAMING_SNAKE_CASE_  ,    padding=SCREAMING_SNAKE_CASE_ )

												lowerCAmelCase__         =    raw_datasets.map(SCREAMING_SNAKE_CASE_  ,    batched=SCREAMING_SNAKE_CASE_ )
												tokenized_datasets.remove_columns(['sentence1', 'sentence2'] )
												lowerCAmelCase__         =    Trainer(self.model  ,    SCREAMING_SNAKE_CASE_  ,    tokenizer=self.tokenizer )
												lowerCAmelCase__         =    trainer.predict(SCREAMING_SNAKE_CASE_ )
												return {"scores": list(predictions.predictions.squeeze(-1 ) )}







from __future__ import annotations







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_ = 0    ):
            __snake_case						       =   value
            __snake_case						       =   None
            __snake_case						       =   None





def        __lowercase(							__snake_case				:		Node | None    ,__snake_case				:		Node | None					)       ->   Node | None:
       if treea is None:
              return treea
       if treea is None:
              return treea

       __snake_case						       =   treea.value + treea.value
       __snake_case						       =   merge_two_binary_trees(treea.left    ,treea.left					)
       __snake_case						       =   merge_two_binary_trees(treea.right    ,treea.right					)
       return treea
def        __lowercase(							__snake_case				:		Node | None					)       ->   None:
       if root:
              print(root.value					)
              print_preorder(root.left					)
              print_preorder(root.right					)


if __name__ == "__main__":
 lowerCamelCase_				:							int       =    Node(1)
 lowerCamelCase_				:							Tuple       =    Node(2)
 lowerCamelCase_				:							Tuple       =    Node(3)
 lowerCamelCase_				:							int       =    Node(4)

 lowerCamelCase_				:							Tuple       =    Node(2)
 lowerCamelCase_				:							List[Any]       =    Node(4)
 lowerCamelCase_				:							List[Any]       =    Node(6)
 lowerCamelCase_				:							Optional[Any]       =    Node(9)
 lowerCamelCase_				:							List[str]       =    Node(5)

 print("Tree1 is: ")
 print_preorder(treea)
 print("Tree2 is: ")
 print_preorder(treea)
 lowerCamelCase_				:							int       =    merge_two_binary_trees(treea, treea)
 print("Merged Tree is: ")
 print_preorder(merged_tree)
