'''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""": 650, """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""": 600, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]     )
class 	__A				(			unittest.TestCase     ):



    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=__a       ,      )
         assert hasattr(self       ,      "env"			)



    def  _lowercase						(self   :   int       ,      __a   :   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=__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  _lowercase						(self   :   int       ,      __a   :   int			):
         TrainingJobAnalytics(__a			).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""			)






    def  _lowercase						(self   :   Any			):
         # create estimator
         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"       ,      999999			)
         )

         # 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			)





'''simple docstring'''





import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
    import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin




@require_torch
class 	__A				(			unittest.TestCase		,     UpperCamelCase__     ):



    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       load_tool("text-to-speech"			)
         self.tool.setup()



    def  _lowercase						(self   :   Union[str, Any]			):
         # SpeechT5 isn't deterministic
         torch.manual_seed(0			)
         UpperCAmelCase_	   =       self.tool("hey"			)
         UpperCAmelCase_	   =       result.to_raw()
         self.assertTrue(
             torch.allclose(
                 resulting_tensor[:3]       ,      torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]			)       ,      )			)






    def  _lowercase						(self   :   List[str]			):
         # SpeechT5 isn't deterministic
         torch.manual_seed(0			)
         UpperCAmelCase_	   =       self.tool("hey"			)
         UpperCAmelCase_	   =       result.to_raw()
         self.assertTrue(
             torch.allclose(
                 resulting_tensor[:3]       ,      torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]			)       ,      )			)




