





"""simple docstring"""



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

import pytest
from parameterized import parameterized, 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.p3.16xlarge""",
            """results""": {"""train_runtime""": 6_50, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_ddp.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 6_00, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf_dist.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 6_00, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ]	)
class 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 def    UpperCamelCase__(       self    ):



     '''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=__lowerCamelCase   ,		)
     assert hasattr(self   ,		'''env'''    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Dict							=	F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
     # distributed data settings
     __A       :		List[Any]							=	{'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None

     # 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=__lowerCamelCase   ,		instance_count=__lowerCamelCase   ,		instance_type=self.instance_type   ,		debugger_hook_config=__lowerCamelCase   ,		hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}   ,		metric_definitions=self.env.metric_definitions   ,		distribution=__lowerCamelCase   ,		py_version='''py36'''   ,		)

 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




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







 @parameterized.expand([(2,)]    )
 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	self.create_estimator(__lowerCamelCase    )

     # run training
     estimator.fit()

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

     # extract kpis
     __A       :		List[str]							=	list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']    )
     __A       :		Any							=	list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']    )
     # get train time from SageMaker job, this includes starting, preprocessing, stopping
     __A       :		List[Any]							=	(
         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}   ,		__lowerCamelCase    )











"""simple docstring"""



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Any							=	AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''   ,		return_dict=__lowerCamelCase    ).to(__lowerCamelCase    )
     __A       :		Optional[Any]							=	AutoTokenizer.from_pretrained('''google/mt5-small'''    )

     __A       :		Optional[int]							=	tokenizer('''Hello there'''   ,		return_tensors='''pt'''    ).input_ids
     __A       :		str							=	tokenizer('''Hi I am'''   ,		return_tensors='''pt'''    ).input_ids

     __A       :		Any							=	model(input_ids.to(__lowerCamelCase    )   ,		labels=labels.to(__lowerCamelCase    )    ).loss
     __A       :		Tuple							=	-(labels.shape[-1] * loss.item())

     __A       :		int							=	-8_4.9_1_2_7
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE    ) < 1e-4    )




