
"""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 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





		def 	snake_case_     (     self):
				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 	snake_case_     (     self							,  lowerCAmelCase__=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=lowerCAmelCase__							,  instance_type=self.instance_type							,  debugger_hook_config=lowerCAmelCase__							,  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							,  lowerCAmelCase__):
				TrainingJobAnalytics(lowerCAmelCase__).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv")




		def 	snake_case_     (     self):
				# create estimator
				__SCREAMING_SNAKE_CASE            = self.create_estimator()

				# run training
				estimator.fit()

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

				# extract kpis
				__SCREAMING_SNAKE_CASE            = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
				__SCREAMING_SNAKE_CASE            = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""])
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				__SCREAMING_SNAKE_CASE            = (
				    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__)





'''simple docstring'''





from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest




@require_tokenizers
class        __A      ( UpperCamelCase__	):
    a__       :						Optional[Any]          =  DistilBertTokenizer
    a__       :						Any          =  DistilBertTokenizerFast
    a__       :						str          =  True



    @slow
    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   DistilBertTokenizer.from_pretrained("distilbert-base-uncased" )

         UpperCAmelCase_           =   tokenizer.encode("sequence builders"   ,			add_special_tokens=__a )
         UpperCAmelCase_           =   tokenizer.encode("multi-sequence build"   ,			add_special_tokens=__a )

         UpperCAmelCase_           =   tokenizer.build_inputs_with_special_tokens(__a )
         UpperCAmelCase_           =   tokenizer.build_inputs_with_special_tokens(__a   ,			__a )

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]




