


'''simple docstring'''



import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__A		=get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
		from transformers.models.plbart.modeling_plbart import shift_tokens_right

__A		=5_00_03
__A		=5_00_02


@require_sentencepiece
@require_tokenizers
class 						_snake_case    (   a__				,      unittest.TestCase		):
					lowerCAmelCase					:Union[str, Any]            =    PLBartTokenizer
					lowerCAmelCase					:Union[str, Any]            =    None
					lowerCAmelCase					:Optional[int]            =    False




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

								# We have a SentencePiece fixture for testing
								UpperCAmelCase__     :    Union[str, Any]         =   PLBartTokenizer(__lowerCamelCase							,							language_codes="""base"""							,							keep_accents=__lowerCamelCase)
								tokenizer.save_pretrained(self.tmpdirname)




					def  snake_case__  (					self):
								UpperCAmelCase__     :    List[Any]         =   PLBartTokenizer(__lowerCamelCase							,							language_codes="""base"""							,							keep_accents=__lowerCamelCase)

								UpperCAmelCase__     :    List[Any]         =   tokenizer.tokenize("""This is a test""")
								self.assertListEqual(__lowerCamelCase							,							["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(__lowerCamelCase)							,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]							,							)

								UpperCAmelCase__     :    Dict         =   tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
								self.assertListEqual(
								    __lowerCamelCase							,							[
								        SPIECE_UNDERLINE + """I""",
								        SPIECE_UNDERLINE + """was""",
								        SPIECE_UNDERLINE + """b""",
								        """or""",
								        """n""",
								        SPIECE_UNDERLINE + """in""",
								        SPIECE_UNDERLINE + """""",
								        """9""",
								        """2""",
								        """0""",
								        """0""",
								        """0""",
								        """,""",
								        SPIECE_UNDERLINE + """and""",
								        SPIECE_UNDERLINE + """this""",
								        SPIECE_UNDERLINE + """is""",
								        SPIECE_UNDERLINE + """f""",
								        """al""",
								        """s""",
								        """é""",
								        """.""",
								    ]							,							)
								UpperCAmelCase__     :    Optional[int]         =   tokenizer.convert_tokens_to_ids(__lowerCamelCase)
								self.assertListEqual(
								    __lowerCamelCase							,							[
								        value + tokenizer.fairseq_offset
								        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
								    ]							,							)

								UpperCAmelCase__     :    Union[str, Any]         =   tokenizer.convert_ids_to_tokens(__lowerCamelCase)
								self.assertListEqual(
								    __lowerCamelCase							,							[
								        SPIECE_UNDERLINE + """I""",
								        SPIECE_UNDERLINE + """was""",
								        SPIECE_UNDERLINE + """b""",
								        """or""",
								        """n""",
								        SPIECE_UNDERLINE + """in""",
								        SPIECE_UNDERLINE + """""",
								        """<unk>""",
								        """2""",
								        """0""",
								        """0""",
								        """0""",
								        """,""",
								        SPIECE_UNDERLINE + """and""",
								        SPIECE_UNDERLINE + """this""",
								        SPIECE_UNDERLINE + """is""",
								        SPIECE_UNDERLINE + """f""",
								        """al""",
								        """s""",
								        """<unk>""",
								        """.""",
								    ]							,							)

								UpperCAmelCase__     :    Tuple         =   tokenizer.vocab_size
								UpperCAmelCase__     :    int         =   [tokenizer.convert_ids_to_tokens(__lowerCamelCase) for x in range(end - 4							,							__lowerCamelCase)]

								self.assertListEqual(__lowerCamelCase							,							["""__java__""", """__python__""", """__en_XX__""", """<mask>"""])

								UpperCAmelCase__     :    Tuple         =   "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
								UpperCAmelCase__     :    Dict         =   tokenizer(__lowerCamelCase).input_ids
								self.assertEqual(
								    tokenizer.decode(__lowerCamelCase							,							skip_special_tokens=__lowerCamelCase							,							clean_up_tokenization_spaces=__lowerCamelCase)							,							__lowerCamelCase							,							)




					def  snake_case__  (					self):
								UpperCAmelCase__     :    str         =   PLBartTokenizer(__lowerCamelCase							,							language_codes="""multi"""							,							keep_accents=__lowerCamelCase)

								UpperCAmelCase__     :    Optional[Any]         =   tokenizer.tokenize("""This is a test""")
								self.assertListEqual(__lowerCamelCase							,							["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(__lowerCamelCase)							,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]							,							)

								UpperCAmelCase__     :    List[str]         =   tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
								self.assertListEqual(
								    __lowerCamelCase							,							[
								        SPIECE_UNDERLINE + """I""",
								        SPIECE_UNDERLINE + """was""",
								        SPIECE_UNDERLINE + """b""",
								        """or""",
								        """n""",
								        SPIECE_UNDERLINE + """in""",
								        SPIECE_UNDERLINE + """""",
								        """9""",
								        """2""",
								        """0""",
								        """0""",
								        """0""",
								        """,""",
								        SPIECE_UNDERLINE + """and""",
								        SPIECE_UNDERLINE + """this""",
								        SPIECE_UNDERLINE + """is""",
								        SPIECE_UNDERLINE + """f""",
								        """al""",
								        """s""",
								        """é""",
								        """.""",
								    ]							,							)
								UpperCAmelCase__     :    List[Any]         =   tokenizer.convert_tokens_to_ids(__lowerCamelCase)
								self.assertListEqual(
								    __lowerCamelCase							,							[
								        value + tokenizer.fairseq_offset
								        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
								    ]							,							)

								UpperCAmelCase__     :    str         =   tokenizer.convert_ids_to_tokens(__lowerCamelCase)
								self.assertListEqual(
								    __lowerCamelCase							,							[
								        SPIECE_UNDERLINE + """I""",
								        SPIECE_UNDERLINE + """was""",
								        SPIECE_UNDERLINE + """b""",
								        """or""",
								        """n""",
								        SPIECE_UNDERLINE + """in""",
								        SPIECE_UNDERLINE + """""",
								        """<unk>""",
								        """2""",
								        """0""",
								        """0""",
								        """0""",
								        """,""",
								        SPIECE_UNDERLINE + """and""",
								        SPIECE_UNDERLINE + """this""",
								        SPIECE_UNDERLINE + """is""",
								        SPIECE_UNDERLINE + """f""",
								        """al""",
								        """s""",
								        """<unk>""",
								        """.""",
								    ]							,							)
								UpperCAmelCase__     :    Dict         =   tokenizer.vocab_size
								UpperCAmelCase__     :    List[Any]         =   [tokenizer.convert_ids_to_tokens(__lowerCamelCase) for x in range(end - 7							,							__lowerCamelCase)]

								self.assertListEqual(
								    __lowerCamelCase							,							["""__java__""", """__python__""", """__en_XX__""", """__javascript__""", """__php__""", """__ruby__""", """__go__"""])
								UpperCAmelCase__     :    Tuple         =   "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
								UpperCAmelCase__     :    Any         =   tokenizer(__lowerCamelCase).input_ids
								self.assertEqual(
								    tokenizer.decode(__lowerCamelCase							,							skip_special_tokens=__lowerCamelCase							,							clean_up_tokenization_spaces=__lowerCamelCase)							,							__lowerCamelCase							,							)


@require_torch
@require_sentencepiece
@require_tokenizers
class 						_snake_case    (   unittest.TestCase		):
					lowerCAmelCase					:List[Any]            =    '''uclanlp/plbart-python-en_XX'''
					lowerCAmelCase					:Any            =    [
					    '''def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])''',
					    '''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''',
					]
					lowerCAmelCase					:Union[str, Any]            =    [
					    '''Returns the maximum value of a b c.''',
					    '''Sums the values of a b c.''',
					]
					lowerCAmelCase					:Any            =    [
					    134,
					    5_452,
					    33_460,
					    33_441,
					    33_463,
					    33_465,
					    33_463,
					    33_449,
					    988,
					    20,
					    33_456,
					    19,
					    33_456,
					    771,
					    39,
					    4_258,
					    889,
					    3_318,
					    33_441,
					    33_463,
					    33_465,
					    33_463,
					    33_449,
					    2_471,
					    2,
					    PYTHON_CODE,
					]




					@classmethod
					def  snake_case__  (					cls):
								UpperCAmelCase__     :    PLBartTokenizer         =   PLBartTokenizer.from_pretrained(
								    cls.checkpoint_name							,							language_codes="""base"""							,							src_lang="""python"""							,							tgt_lang="""en_XX""")
								UpperCAmelCase__     :    str         =   1
								return cls




					def  snake_case__  (					self):
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__java__"""]							,							5_0001)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__python__"""]							,							5_0002)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__en_XX__"""]							,							5_0003)




					def  snake_case__  (					self):
								UpperCAmelCase__     :    Optional[Any]         =   self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
								self.assertListEqual(self.expected_src_tokens							,							__lowerCamelCase)




					def  snake_case__  (					self):
								self.assertIn(__lowerCamelCase							,							self.tokenizer.all_special_ids)
								UpperCAmelCase__     :    int         =   [EN_CODE, 9037, 3_3442, 57, 752, 153, 14, 56, 18, 9, 2]
								UpperCAmelCase__     :    Dict         =   self.tokenizer.decode(__lowerCamelCase							,							skip_special_tokens=__lowerCamelCase)
								UpperCAmelCase__     :    Optional[Any]         =   self.tokenizer.decode(generated_ids[1:]							,							skip_special_tokens=__lowerCamelCase)
								self.assertEqual(__lowerCamelCase							,							__lowerCamelCase)
								self.assertNotIn(self.tokenizer.eos_token							,							__lowerCamelCase)




					def  snake_case__  (					self):
								UpperCAmelCase__     :    Optional[int]         =   ["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 20]
								self.assertIsInstance(src_text[0]							,							__lowerCamelCase)
								UpperCAmelCase__     :    str         =   10
								UpperCAmelCase__     :    str         =   self.tokenizer(__lowerCamelCase							,							max_length=__lowerCamelCase							,							truncation=__lowerCamelCase).input_ids[0]
								self.assertEqual(ids[-2]							,							2)
								self.assertEqual(ids[-1]							,							__lowerCamelCase)
								self.assertEqual(len(__lowerCamelCase)							,							__lowerCamelCase)




					def  snake_case__  (					self):
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """__java__"""])							,							[5_0004, 5_0001])




					def  snake_case__  (					self):
								UpperCAmelCase__     :    Any         =   tempfile.mkdtemp()
								UpperCAmelCase__     :    Dict         =   self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(__lowerCamelCase)
								UpperCAmelCase__     :    int         =   PLBartTokenizer.from_pretrained(__lowerCamelCase)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,							__lowerCamelCase)




					@require_torch
					def  snake_case__  (					self):
								UpperCAmelCase__     :    Optional[int]         =   self.tokenizer(self.src_text							,							text_target=self.tgt_text							,							padding=__lowerCamelCase							,							return_tensors="""pt""")
								UpperCAmelCase__     :    Union[str, Any]         =   shift_tokens_right(batch["""labels"""]							,							self.tokenizer.pad_token_id)

								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								self.assertEqual(batch.input_ids[1][-2:].tolist()							,							[2, PYTHON_CODE])
								self.assertEqual(batch.decoder_input_ids[1][0]							,							__lowerCamelCase)
								self.assertEqual(batch.decoder_input_ids[1][-1]							,							2)
								self.assertEqual(batch.labels[1][-2:].tolist()							,							[2, EN_CODE])




					@require_torch
					def  snake_case__  (					self):
								UpperCAmelCase__     :    List[Any]         =   self.tokenizer(
								    self.src_text							,							text_target=self.tgt_text							,							padding=__lowerCamelCase							,							truncation=__lowerCamelCase							,							max_length=len(self.expected_src_tokens)							,							return_tensors="""pt"""							,							)
								UpperCAmelCase__     :    Tuple         =   shift_tokens_right(batch["""labels"""]							,							self.tokenizer.pad_token_id)

								self.assertIsInstance(__lowerCamelCase							,							__lowerCamelCase)

								self.assertEqual((2, 26)							,							batch.input_ids.shape)
								self.assertEqual((2, 26)							,							batch.attention_mask.shape)
								UpperCAmelCase__     :    Tuple         =   batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens							,							__lowerCamelCase)
								self.assertEqual(2							,							batch.decoder_input_ids[0, -1])  # EOS
								# Test that special tokens are reset
								self.assertEqual(self.tokenizer.prefix_tokens							,							[])
								self.assertEqual(self.tokenizer.suffix_tokens							,							[self.tokenizer.eos_token_id, PYTHON_CODE])




					def  snake_case__  (					self):
								UpperCAmelCase__     :    List[Any]         =   self.tokenizer(self.src_text							,							padding=__lowerCamelCase							,							truncation=__lowerCamelCase							,							max_length=3							,							return_tensors="""pt""")
								UpperCAmelCase__     :    Dict         =   self.tokenizer(
								    text_target=self.tgt_text							,							padding=__lowerCamelCase							,							truncation=__lowerCamelCase							,							max_length=10							,							return_tensors="""pt""")
								UpperCAmelCase__     :    Any         =   targets["input_ids"]
								UpperCAmelCase__     :    Union[str, Any]         =   shift_tokens_right(__lowerCamelCase							,							self.tokenizer.pad_token_id)

								self.assertEqual(batch.input_ids.shape[1]							,							3)
								self.assertEqual(batch.decoder_input_ids.shape[1]							,							10)




					@require_torch
					def  snake_case__  (					self):
								UpperCAmelCase__     :    str         =   self.tokenizer._build_translation_inputs(
								    """A test"""							,							return_tensors="""pt"""							,							src_lang="""en_XX"""							,							tgt_lang="""java""")

								self.assertEqual(
								    nested_simplify(__lowerCamelCase)							,							{
								        # A, test, EOS, en_XX
								        """input_ids""": [[150, 242, 2, 5_0003]],
								        """attention_mask""": [[1, 1, 1, 1]],
								        # java
								        """forced_bos_token_id""": 5_0001,
								    }							,							)



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)
