

import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


lowercase_       		=  "\\n@inproceedings{snover-etal-2006-study,\n    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n    author = \"Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John\",\n    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",\n    month = aug # \" 8-12\",\n    year = \"2006\",\n    address = \"Cambridge, Massachusetts, USA\",\n    publisher = \"Association for Machine Translation in the Americas\",\n    url = \"https://aclanthology.org/2006.amta-papers.25\",\n    pages = \"223--231\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

lowercase_       		=  "\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

lowercase_       		=  "\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"


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







    """simple docstring"""





    def 						snake_case__     (      self :       Optional[int]		)->    List[str]:





        '''simple docstring'''







        if version.parse(scb.__version__		) < version.parse('1.4.12'		):
            raise ImportWarning(
                'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
                'You can install it with `pip install "sacrebleu>=1.4.12"`.'		)
        return datasets.MetricInfo(
            description=_DESCRIPTION,citation=_CITATION,homepage='http://www.cs.umd.edu/~snover/tercom/',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/mjpost/sacreBLEU#ter'],reference_urls=[
                'https://github.com/jhclark/tercom',
            ],)


    def 						snake_case__     (      self :       int,lowercase_ :       Any,lowercase_ :       Optional[Any],lowercase_ :       bool = False,lowercase_ :       bool = False,lowercase_ :       bool = False,lowercase_ :       bool = False,)->    Optional[Any]:





        '''simple docstring'''







        A__          =   len(references[0]		)
        if any(len(lowercase_		) != references_per_prediction for refs in references		):
            raise ValueError('Sacrebleu requires the same number of references for each prediction'		)
        A__          =   [[refs[i] for refs in references] for i in range(lowercase_		)]

        A__          =   TER(
            normalized=lowercase_,no_punct=lowercase_,asian_support=lowercase_,case_sensitive=lowercase_,)
        A__          =   sb_ter.corpus_score(lowercase_,lowercase_		)

        return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}






"""simple docstring"""






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
	from sagemaker import Session, TrainingJobAnalytics
	from sagemaker.huggingface import HuggingFace



@pytest.mark.skipif(
    literal_eval(os.getenv('TEST_SAGEMAKER'  ,		'False'							)							) is not True  ,		reason='Skipping test because should only be run when releasing minor transformers version'  ,		)
@pytest.mark.usefixtures('sm_env'							)
@parameterized_class(
    [
        {
            'framework': 'pytorch',
            'script': 'run_glue.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.g4dn.xlarge',
            'results': {'train_runtime': 6_5_0, 'eval_accuracy': 0.6, 'eval_loss': 0.9},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.g4dn.xlarge',
            'results': {'train_runtime': 6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]							)
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Tuple:

				"""simple docstring"""

				if self.framework == "pytorch":
							subprocess.run(
							    F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()       ,			encoding="utf-8"       ,			check=_SCREAMING_SNAKE_CASE       ,			)
				assert hasattr(self       ,			"env")

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		Any=1)				->       Dict:

				"""simple docstring"""

				return HuggingFace(
				    entry_point=self.script       ,			source_dir=self.env.test_path       ,			role=self.env.role       ,			image_uri=self.env.image_uri       ,			base_job_name=F"""{self.env.base_job_name}-single"""       ,			instance_count=_SCREAMING_SNAKE_CASE       ,			instance_type=self.instance_type       ,			debugger_hook_config=_SCREAMING_SNAKE_CASE       ,			hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}       ,			metric_definitions=self.env.metric_definitions       ,			py_version="py36"       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		str       ,			_SCREAMING_SNAKE_CASE:		List[Any])				->       Optional[Any]:

				"""simple docstring"""

				TrainingJobAnalytics(_SCREAMING_SNAKE_CASE).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")







	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.create_estimator()

				# run training
				estimator.fit()

				# result dataframe
				__lowerCAmelCase      :  Tuple												=       TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

				# extract kpis
				__lowerCAmelCase      :  Optional[Any]												=       list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
				__lowerCAmelCase      :  Union[str, Any]												=       list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				__lowerCAmelCase      :  Tuple												=       (
				    Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds"       ,			99_9999)
				)

				# assert kpis
				assert train_runtime <= self.results["train_runtime"]
				assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy)
				assert all(t <= self.results["eval_loss"] for t in eval_loss)

				# dump tests result into json file to share in PR
				with open(F"""{estimator.latest_training_job.name}.json"""       ,			"w") as outfile:
							json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}       ,			_SCREAMING_SNAKE_CASE)