






import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets


@contextmanager
def     _a					(       )	->				Optional[int]:
						"""simple docstring"""







						def filter_log(UpperCamelCase_   :			List[Any]				):
												return False if "This IS expected if you are initializing" in record.msg else True

						lowerCAmelCase__	 =  datasets.utils.logging.get_logger("transformers.modeling_utils"				)
						logger.addFilter(UpperCamelCase_				)
						try:
												yield
						finally:
												logger.removeFilter(UpperCamelCase_				)


a_							=      '''\
@inproceedings{bert-score,
  title={BERTScore: Evaluating Text Generation with BERT},
  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=SkeHuCVFDr}
}
'''

a_							=      '''\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.
'''

a_							=      '''
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. \'en\').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bertscore = datasets.load_metric("bertscore")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")
    >>> print([round(v, 2) for v in results["f1"]])
    [1.0, 1.0]
'''





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

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







							'''simple docstring'''





							return datasets.MetricInfo(
							    description=_DESCRIPTION						,      citation=_CITATION						,      homepage="https://github.com/Tiiiger/bert_score"						,      inputs_description=_KWARGS_DESCRIPTION						,      features=datasets.Features(
							        {
							            "predictions": datasets.Value("string"						,      id="sequence"    ),
							            "references": datasets.Sequence(datasets.Value("string"						,      id="sequence"    )						,      id="references"    ),
							        }    )						,      codebase_urls=["https://github.com/Tiiiger/bert_score"]						,      reference_urls=[
							        "https://github.com/Tiiiger/bert_score",
							        "https://arxiv.org/abs/1904.09675",
							    ]						,      )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase=None						,      __UpperCAmelCase=None						,      __UpperCAmelCase=None						,      __UpperCAmelCase=False						,      __UpperCAmelCase=False						,      __UpperCAmelCase=None						,      __UpperCAmelCase=64						,      __UpperCAmelCase=4						,      __UpperCAmelCase=False						,      __UpperCAmelCase=False						,      __UpperCAmelCase=None						,      __UpperCAmelCase=False						,      )->   Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  bert_score.utils.get_hash
							lowerCAmelCase__	 =  bert_score.BERTScorer

							if version.parse(bert_score.__version__    ) >= version.parse("0.3.10"    ):
													lowerCAmelCase__	 =  functools.partial(__UpperCAmelCase						,      use_fast_tokenizer=__UpperCAmelCase    )
													lowerCAmelCase__	 =  functools.partial(__UpperCAmelCase						,      use_fast_tokenizer=__UpperCAmelCase    )
							elif use_fast_tokenizer:
													raise ImportWarning(
													    "To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"
													    "You can install it with `pip install \"bert-score>=0.3.10\"`."    )

							if model_type is None:
													assert lang is not None, "either lang or model_type should be specified"
													lowerCAmelCase__	 =  bert_score.utils.langamodel[lang.lower()]

							if num_layers is None:
													lowerCAmelCase__	 =  bert_score.utils.modelalayers[model_type]

							lowerCAmelCase__	 =  get_hash(
							    model=__UpperCAmelCase						,      num_layers=__UpperCAmelCase						,      idf=__UpperCAmelCase						,      rescale_with_baseline=__UpperCAmelCase						,      use_custom_baseline=baseline_path is not None						,      )

							with filter_logging_context():
													if not hasattr(self						,      "cached_bertscorer"    ) or self.cached_bertscorer.hash != hashcode:
																			lowerCAmelCase__	 =  scorer(
																			    model_type=__UpperCAmelCase						,      num_layers=__UpperCAmelCase						,      batch_size=__UpperCAmelCase						,      nthreads=__UpperCAmelCase						,      all_layers=__UpperCAmelCase						,      idf=__UpperCAmelCase						,      device=__UpperCAmelCase						,      lang=__UpperCAmelCase						,      rescale_with_baseline=__UpperCAmelCase						,      baseline_path=__UpperCAmelCase						,      )

							((lowerCAmelCase__) ,							(lowerCAmelCase__) ,							(lowerCAmelCase__))	 =  self.cached_bertscorer.score(
							    cands=__UpperCAmelCase						,      refs=__UpperCAmelCase						,      verbose=__UpperCAmelCase						,      batch_size=__UpperCAmelCase						,      )
							lowerCAmelCase__	 =  {
							    "precision": P.tolist(),
							    "recall": R.tolist(),
							    "f1": F.tolist(),
							    "hashcode": hashcode,
							}
							return output_dict

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=None						,      __UpperCAmelCase=None						,      **__UpperCAmelCase    )->   Any:







							'''simple docstring'''





							if references is not None:
													lowerCAmelCase__	 =  [[ref] if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ) else ref for ref in references]
							super().add_batch(predictions=__UpperCAmelCase						,      references=__UpperCAmelCase						,      **__UpperCAmelCase    )


	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=None						,      __UpperCAmelCase=None						,      **__UpperCAmelCase    )->   Optional[int]:







							'''simple docstring'''





							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
													lowerCAmelCase__	 =  [reference]
							super().add(prediction=__UpperCAmelCase						,      reference=__UpperCAmelCase						,      **__UpperCAmelCase    )














from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
a_							=      {
    '''1''': ['''2''', '''3''', '''4''', '''5'''],
    '''2''': ['''1''', '''3''', '''4''', '''5'''],
    '''3''': ['''1''', '''2''', '''4''', '''5''', '''10'''],
    '''4''': ['''1''', '''2''', '''3''', '''5''', '''6'''],
    '''5''': ['''1''', '''2''', '''3''', '''4''', '''7'''],
    '''6''': ['''7''', '''8''', '''9''', '''10''', '''4'''],
    '''7''': ['''6''', '''8''', '''9''', '''10''', '''5'''],
    '''8''': ['''6''', '''7''', '''9''', '''10'''],
    '''9''': ['''6''', '''7''', '''8''', '''10'''],
    '''10''': ['''6''', '''7''', '''8''', '''9''', '''3'''],
}


def     _a					(       UpperCamelCase_   :			dict[str, list[str]]				)	->				set[tuple[str, str]]:
						"""simple docstring"""







						lowerCAmelCase__	 =  {node: {node} for node in graph}

						lowerCAmelCase__	 =  {node: graph[node][:] for node in graph}

						while len(UpperCamelCase_				) > 2:
												# Choose a random edge.
												lowerCAmelCase__	 =  random.choice(list(graph_copy.keys()				)				)
												lowerCAmelCase__	 =  random.choice(graph_copy[u]				)

												# Contract edge (u, v) to new node uv
												lowerCAmelCase__	 =  u + v
												lowerCAmelCase__	 =  list(set(graph_copy[u] + graph_copy[v]				)				)
												uv_neighbors.remove(UpperCamelCase_				)
												uv_neighbors.remove(UpperCamelCase_				)
												lowerCAmelCase__	 =  uv_neighbors
												for neighbor in uv_neighbors:
																		graph_copy[neighbor].append(UpperCamelCase_				)

												lowerCAmelCase__	 =  set(contracted_nodes[u].union(contracted_nodes[v]				)				)

												# Remove nodes u and v.
												del graph_copy[u]
												del graph_copy[v]
												for neighbor in uv_neighbors:
																		if u in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(UpperCamelCase_				)
																		if v in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(UpperCamelCase_				)

    # Find cutset.
						lowerCAmelCase__	 =  [contracted_nodes[node] for node in graph_copy]
						return {
						    (node, neighbor)
						    for node in groups[0]
						    for neighbor in graph[node]
						    if neighbor in groups[1]
						}


if __name__ == "__main__":
	print(partition_graph(TEST_GRAPH))






