


import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad

class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   str													=					10







					def 			snake_case					(							self :     Any					):
									lowercase__					:   Optional[Any]													=					[1, 2, 3, 4]
									lowercase__					:   int													=					[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
									self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_				,       self.block_size				,       0					)				,       SCREAMING_SNAKE_CASE_					)







					def 			snake_case					(							self :     Tuple					):
									lowercase__					:   Tuple													=					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
									lowercase__					:   int													=					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
									self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_				,       self.block_size				,       0					)				,       SCREAMING_SNAKE_CASE_					)







					def 			snake_case					(							self :     Dict					):
									lowercase__					:   Optional[Any]													=					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
									lowercase__					:   List[Any]													=					[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
									self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_				,       self.block_size				,       0					)				,       SCREAMING_SNAKE_CASE_					)







					def 			snake_case					(							self :     List[Any]					):
									lowercase__					:   Tuple													=					'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
									lowercase__					:   List[Any]													=					process_story(SCREAMING_SNAKE_CASE_					)
									self.assertEqual(SCREAMING_SNAKE_CASE_				,       []					)







					def 			snake_case					(							self :     str					):
									lowercase__					:   str													=					''
									lowercase__					:   Tuple													=					process_story(SCREAMING_SNAKE_CASE_					)
									self.assertEqual(SCREAMING_SNAKE_CASE_				,       []					)
									self.assertEqual(SCREAMING_SNAKE_CASE_				,       []					)







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Optional[Any]													=					(
									    'It was the year of Our Lord one thousand seven hundred and '
									    'seventy-five\n\nSpiritual revelations were conceded to England '
									    'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
									)
									lowercase__					:   List[Any]													=					process_story(SCREAMING_SNAKE_CASE_					)

									lowercase__					:   str													=					[
									    'It was the year of Our Lord one thousand seven hundred and seventy-five.',
									    'Spiritual revelations were conceded to England at that favoured period, as at this.',
									]
									self.assertEqual(SCREAMING_SNAKE_CASE_				,       SCREAMING_SNAKE_CASE_					)

									lowercase__					:   Dict													=					['It was the best of times.']
									self.assertEqual(SCREAMING_SNAKE_CASE_				,       SCREAMING_SNAKE_CASE_					)







					def 			snake_case					(							self :     Tuple					):
									lowercase__					:   Optional[int]													=					torch.tensor([1, 2, 3, 4]					)
									lowercase__					:   Optional[int]													=					torch.tensor([1, 1, 1, 1]					)
									np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_				,       0					).numpy()				,       expected.numpy()					)







					def 			snake_case					(							self :     Any					):
									lowercase__					:   Optional[Any]													=					torch.tensor([1, 2, 3, 4, 23, 23, 23]					)
									lowercase__					:   List[Any]													=					torch.tensor([1, 1, 1, 1, 0, 0, 0]					)
									np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_				,       23					).numpy()				,       expected.numpy()					)







					def 			snake_case					(							self :     Any					):
									lowercase__					:   Optional[int]													=					torch.tensor([8, 2, 3, 4, 1, 1, 1]					)
									lowercase__					:   List[Any]													=					torch.tensor([1, 1, 1, 1, 0, 0, 0]					)
									np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_				,       1					).numpy()				,       expected.numpy()					)







					def 			snake_case					(							self :     str					):
									lowercase__					:   Tuple													=					101
									lowercase__					:   Optional[int]													=					torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]					)
									lowercase__					:   Tuple													=					torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]					)

									lowercase__					:   Tuple													=					compute_token_type_ids(SCREAMING_SNAKE_CASE_				,       SCREAMING_SNAKE_CASE_					)
									np.testing.assert_array_equal(SCREAMING_SNAKE_CASE_				,       SCREAMING_SNAKE_CASE_					)









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 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     Union[str, Any]					):
									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 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     str=1					):
									# creates estimator
									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 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     str					):
									TrainingJobAnalytics(SCREAMING_SNAKE_CASE					).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""					)







					def 			snake_case					(							self :     str					):
									# create estimator
									lowercase__					:   Optional[int]													=					self.create_estimator()

									# run training
									estimator.fit()

									# result dataframe
									lowercase__					:   Union[str, Any]													=					TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

									# extract kpis
									lowercase__					:   str													=					list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]					)
									lowercase__					:   Tuple													=					list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]					)
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									lowercase__					:   Any													=					(
									    Session().describe_training_job(estimator.latest_training_job.name					).get("TrainingTimeInSeconds"				,       999_999					)
									)

									# 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					)




