

"""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_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]   )
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			Optional[int]	):
    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=UpperCAmelCase__,					)
    assert hasattr(self,					"env"	)


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			List[Any]	):
    # configuration for running training on smdistributed Model Parallel
    __lowercase								=						{
        "enabled": True,
        "processes_per_host": 8,
    }
    __lowercase								=						{
        "enabled": True,
        "parameters": {
            "microbatches": 4,
            "placement_strategy": "spread",
            "pipeline": "interleaved",
            "optimize": "speed",
            "partitions": 4,
            "ddp": True,
        },
    }

    __lowercase								=						{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

    __lowercase								=						"trainer" if self.script == "run_glue.py" else "smtrainer"
    # 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}-{instance_count}-smp-{name_extension}""",					instance_count=UpperCAmelCase__,					instance_type=self.instance_type,					debugger_hook_config=UpperCAmelCase__,					hyperparameters={
            **self.env.hyperparameters,
            "model_name_or_path": self.model_name_or_path,
            "max_steps": 5_0_0,
        },					metric_definitions=self.env.metric_definitions,					distribution=UpperCAmelCase__,					py_version="py36",					)


 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			int	):
    TrainingJobAnalytics(UpperCAmelCase__	).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""	)



 @parameterized.expand([(1,)]	)
 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			Union[str, Any]	):
    # create estimator
    __lowercase								=						self.create_estimator(UpperCAmelCase__	)

    # run training
    estimator.fit()

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

    # extract kpis
    __lowercase								=						list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]	)
    __lowercase								=						list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]	)
    # get train time from SageMaker job, this includes starting, preprocessing, stopping
    __lowercase								=						(
        Session().describe_training_job(estimator.latest_training_job.name	).get("TrainingTimeInSeconds",					9_9_9_9_9_9	)
    )

    # 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},					UpperCAmelCase__	)







import unittest

from transformers import DonutProcessor


snake_case_			  = '''naver-clova-ix/donut-base'''


class       SCREAMING_SNAKE_CASE__					(unittest.TestCase     ):





    def       snake_case_				(      self):
         lowercase__    :   Dict      =       DonutProcessor.from_pretrained(a)





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       {
             'name': 'John Doe',
             'age': '99',
             'city': 'Atlanta',
             'state': 'GA',
             'zip': '30301',
             'phone': '123-4567',
             'nicknames': [{'nickname': 'Johnny'}, {'nickname': 'JD'}],
         }

         lowercase__    :   Tuple      =       (
             '<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'
             '<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'
             '<s_nicknames><s_nickname>Johnny</s_nickname>'
             '<sep/><s_nickname>JD</s_nickname></s_nicknames>'
         )
         lowercase__    :   str      =       self.processor.tokenajson(a)

         self.assertDictEqual(a						,   a)

