



import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


UpperCAmelCase__       =	"\\n@inproceedings{lin-2004-rouge,\n    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n    author = \"Lin, Chin-Yew\",\n    booktitle = \"Text Summarization Branches Out\",\n    month = jul,\n    year = \"2004\",\n    address = \"Barcelona, Spain\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W04-1013\",\n    pages = \"74--81\",\n}\n"

UpperCAmelCase__       =	"\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

UpperCAmelCase__       =	"\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"







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



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   int:



						"""simple docstring"""






						return datasets.MetricInfo(
						    description=_DESCRIPTION			,		citation=_CITATION			,		inputs_description=_KWARGS_DESCRIPTION			,		features=datasets.Features(
						        {
						            'predictions': datasets.Value('string'			,		id='sequence'),
						            'references': datasets.Value('string'			,		id='sequence'),
						        })			,		codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge']			,		reference_urls=[
						        'https://en.wikipedia.org/wiki/ROUGE_(metric)',
						        'https://github.com/google-research/google-research/tree/master/rouge',
						    ]			,		)



			def    _lowerCamelCase	(						self       :  Dict			,		A       :  Tuple			,		A       :  Any			,		A       :  List[str]=None			,		A       :  Union[str, Any]=True			,		A       :  str=False)       ->   List[str]:



						"""simple docstring"""






						if rouge_types is None:
									_UpperCAmelCase			=       ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

						_UpperCAmelCase			=       rouge_scorer.RougeScorer(rouge_types=A			,		use_stemmer=A)
						if use_aggregator:
									_UpperCAmelCase			=       scoring.BootstrapAggregator()
						else:
									_UpperCAmelCase			=       []

						for ref, pred in zip(A			,		A):
									_UpperCAmelCase			=       scorer.score(A			,		A)
									if use_aggregator:
												aggregator.add_scores(A)
									else:
												scores.append(A)

						if use_aggregator:
									_UpperCAmelCase			=       aggregator.aggregate()
						else:
									_UpperCAmelCase			=       {}
									for key in scores[0]:
												_UpperCAmelCase			=       [score[key] for score in scores]

						return result





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       __lowerCAmelCase   (      unittest.TestCase						):



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Any:



						"""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=A			,		)
						assert hasattr(self			,		'env')



			def    _lowerCamelCase	(						self       :  Any			,		A       :  Tuple=1)       ->   List[str]:



						"""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=A			,		instance_type=self.instance_type			,		debugger_hook_config=A			,		hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}			,		metric_definitions=self.env.metric_definitions			,		py_version='py36'			,		)



			def    _lowerCamelCase	(						self       :  Dict			,		A       :  int)       ->   str:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.create_estimator()

						# run training
						estimator.fit()

						# result dataframe
						_UpperCAmelCase			=       TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

						# extract kpis
						_UpperCAmelCase			=       list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'])
						_UpperCAmelCase			=       list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'])
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						_UpperCAmelCase			=       (
						    Session().describe_training_job(estimator.latest_training_job.name).get('TrainingTimeInSeconds'			,		99_99_99)
						)

						# 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}			,		A)
