


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": 650, "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": 600, "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": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ])
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				str:
            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  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Tuple:
            _A      :       Dict				    = F"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
            # distributed data settings
            _A      :       Optional[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  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Optional[Any]:
            TrainingJobAnalytics(__lowerCamelCase).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv")


     @parameterized.expand([(2,)])
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Any:
            # create estimator
            _A      :       Union[str, Any]				    = self.create_estimator(__lowerCamelCase)

            # run training
            estimator.fit()

            # result dataframe
            _A      :       Optional[Any]				    = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

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








import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class       _UpperCAmelCase					(       A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       CTRLTokenizer
				lowercase__											=       False
				lowercase__											=       False




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowercase__    =			['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
								lowercase__    =			dict(zip(lowerCamelCase,  range(len(lowerCamelCase  )  )  )  )
								lowercase__    =			['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
								lowercase__    =			{'''unk_token''': '''<unk>'''}

								lowercase__    =			os.path.join(self.tmpdirname,  VOCAB_FILES_NAMES['''vocab_file''']  )
								lowercase__    =			os.path.join(self.tmpdirname,  VOCAB_FILES_NAMES['''merges_file''']  )
								with open(self.vocab_file,  '''w''',  encoding='''utf-8'''  ) as fp:
												fp.write(json.dumps(lowerCamelCase  ) + '''\n'''  )
								with open(self.merges_file,  '''w''',  encoding='''utf-8'''  ) as fp:
												fp.write('''\n'''.join(lowerCamelCase  )  )




				def 				lowercase__   (     self						:    Union[str, Any],  **lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								kwargs.update(self.special_tokens_map  )
								return CTRLTokenizer.from_pretrained(self.tmpdirname,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			'''adapt react readapt apt'''
								lowercase__    =			'''adapt react readapt apt'''
								return input_text, output_text




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			CTRLTokenizer(self.vocab_file,  self.merges_file,  **self.special_tokens_map  )
								lowercase__    =			'''adapt react readapt apt'''
								lowercase__    =			'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
								lowercase__    =			tokenizer.tokenize(lowerCamelCase  )
								self.assertListEqual(lowerCamelCase,  lowerCamelCase  )

								lowercase__    =			tokens + [tokenizer.unk_token]

								lowercase__    =			[0, 1, 2, 4, 5, 1, 0, 3, 6]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase  ),  lowerCamelCase  )




