






'''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': 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 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      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 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  F'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
								# distributed data settings
								lowerCamelCase__         =  {"""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       ):
								TrainingJobAnalytics(_lowerCAmelCase       ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''       )






		@parameterized.expand([(2,)]       )
		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								# create estimator
								lowerCamelCase__         =  self.create_estimator(_lowerCAmelCase       )

								# run training
								estimator.fit()

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

								# extract kpis
								lowerCamelCase__         =  list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]       )
								lowerCamelCase__         =  list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]       )
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								lowerCamelCase__         =  (
								    Session().describe_training_job(estimator.latest_training_job.name       ).get("""TrainingTimeInSeconds"""   ,99_99_99       )
								)

								# 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 GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase    :      List[Any]  			=		get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')




@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__    (a   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					GPTSwaTokenizer
		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					True
		_UpperCamelCase				  =					False






		def 			UpperCamelCase_					(      self       ):
								super().setUp()

								# We have a SentencePiece fixture for testing
								lowerCamelCase__         =  GPTSwaTokenizer(_lowerCAmelCase   ,eos_token="""<unk>"""   ,bos_token="""<unk>"""   ,pad_token="""<unk>"""       )

								tokenizer.save_pretrained(self.tmpdirname       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  """This is a test"""
								lowerCamelCase__         =  """This is a test"""
								return input_text, output_text






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """<s>"""
								lowerCamelCase__         =  1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCAmelCase       )   ,_lowerCAmelCase       )
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCAmelCase       )   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  list(self.get_tokenizer().get_vocab().keys()       )

								self.assertEqual(vocab_keys[0]   ,"""<unk>"""       )
								self.assertEqual(vocab_keys[1]   ,"""<s>"""       )
								self.assertEqual(vocab_keys[-1]   ,"""j"""       )
								self.assertEqual(len(_lowerCAmelCase       )   ,20_00       )






		def 			UpperCamelCase_					(      self       ):
								self.assertEqual(self.get_tokenizer().vocab_size   ,20_00       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  GPTSwaTokenizer(_lowerCAmelCase       )

								lowerCamelCase__         =  tokenizer.tokenize("""This is a test"""       )
								self.assertListEqual(_lowerCAmelCase   ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]       )

								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase       )   ,[4_65, 2_87, 2_65, 6_31, 8_42]       )

								lowerCamelCase__         =  tokenizer.tokenize("""I was born in 92000, and this is falsé."""       )
								# fmt: off
								self.assertListEqual(
								    _lowerCAmelCase   ,["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]   ,)
								# fmt: on

								lowerCamelCase__         =  tokenizer.convert_tokens_to_ids(_lowerCAmelCase       )
								self.assertListEqual(
								    _lowerCAmelCase   ,[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60]   ,)

								lowerCamelCase__         =  tokenizer.convert_ids_to_tokens(_lowerCAmelCase       )
								# fmt: off
								self.assertListEqual(
								    _lowerCAmelCase   ,["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]       )
								# fmt: on






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  GPTSwaTokenizer(_lowerCAmelCase       )
								lowerCamelCase__         =  ["""This is a test""", """I was born in 92000, and this is falsé."""]
								lowerCamelCase__         =  [
								    [4_65, 2_87, 2_65, 6_31, 8_42],
								    [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
								]

								# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
								for text, expected_ids in zip(_lowerCAmelCase   ,_lowerCAmelCase       ):
														self.assertListEqual(tokenizer.encode_fast(_lowerCAmelCase       )   ,_lowerCAmelCase       )

								# Test that decode_fast returns the input text
								for text, token_ids in zip(_lowerCAmelCase   ,_lowerCAmelCase       ):
														self.assertEqual(tokenizer.decode_fast(_lowerCAmelCase       )   ,_lowerCAmelCase       )






		@slow
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  [
								    """<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')""",
								    """Hey there, how are you doing this fine day?""",
								    """This is a text with a trailing spaces followed by a dot     .""",
								    """Häj sväjs lillebrör! =)""",
								    """Det är inget fel på Mr. Cool""",
								]

								# fmt: off
								lowerCamelCase__         =  {"""input_ids""": [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
								# fmt: on
								self.tokenizer_integration_test_util(
								    expected_encoding=_lowerCAmelCase   ,model_name="""AI-Sweden/gpt-sw3-126m"""   ,sequences=_lowerCAmelCase   ,)

