"""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


lowerCAmelCase_     =	get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')




@require_sentencepiece
@require_tokenizers
class  _snake_case		(    __snake_case   ,       unittest.TestCase ):







				"""simple docstring"""






				a			=						GPTSwaTokenizer
				a			=						False
				a			=						True
				a			=						False





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											super().setUp()

											# We have a SentencePiece fixture for testing
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    GPTSwaTokenizer(_A      ,    eos_token="""<unk>"""      ,    bos_token="""<unk>"""      ,    pad_token="""<unk>""")

											tokenizer.save_pretrained(self.tmpdirname)





				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    """This is a test"""
											_SCREAMING_SNAKE_CASE		:						Any			  =    """This is a test"""
											return input_text, output_text





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    """<s>"""
											_SCREAMING_SNAKE_CASE		:						Dict			  =    1

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





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    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(_A)      ,    2_0_0_0)





				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											self.assertEqual(self.get_tokenizer().vocab_size      ,    2_0_0_0)





				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    GPTSwaTokenizer(_A)

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    tokenizer.tokenize("""This is a test""")
											self.assertListEqual(_A      ,    ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])

											self.assertListEqual(tokenizer.convert_tokens_to_ids(_A)      ,    [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2])

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

											_SCREAMING_SNAKE_CASE		:						Any			  =    tokenizer.convert_tokens_to_ids(_A)
											self.assertListEqual(
											    _A      ,    [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0]      ,    )

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





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    GPTSwaTokenizer(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    ["""This is a test""", """I was born in 92000, and this is falsé."""]
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    [
											    [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2],
											    [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0],
											]

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

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





				@slow
				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    [
											    """<|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
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {"""input_ids""": [[6_3_4_2_3, 5, 6_8_1_1, 1_4_9_5_4, 2_8_2, 8_1_6, 3_8_2_1, 6_3_4_6_6, 6_3_4_2_5, 6_3_4_6_2, 1_8, 6_3_9_7_8, 6_7_8, 3_0_1, 1_3_2_0, 6_3_4_2_3, 6_3_4_5_5, 6_3_4_5_8, 1_8, 6_3_9_8_2, 4_2_4_6, 3_9_4_0, 1_9_0_1, 4_7_7_8_9, 5_5_4_7, 1_8_9_9_4], [1_9_6_3_0, 1_1_0_0, 6_3_4_4_6, 1_3_4_2, 6_3_3, 5_4_4, 4_4_8_8, 5_9_3, 5_1_0_2, 2_4_1_6, 6_3_4_9_5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_6_5_2, 4_2_8, 2_6_8, 1_9_3_6, 5_1_5, 2_6_8, 5_8_5_9_3, 2_2_4_1_3, 9_1_0_6, 5_4_6, 2_6_8, 3_3_2_1_3, 6_3_9_7_9, 6_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5_1_3_0, 6_3_4_5_0, 9_2_4, 6_3_4_4_9, 2_2_4_9, 4_0_6_2, 1_5_5_8, 3_1_8, 6_3_5_0_4, 2_1_4_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_0_9, 3_7_7, 2_8_2_7, 2_5_5_9, 3_3_2, 6_5_7_5, 6_3_4_4_3, 2_6_8_0_1, 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=_A      ,    model_name="""AI-Sweden/gpt-sw3-126m"""      ,    sequences=_A      ,    )



"""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": 6_50, "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": 6_00, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ] )
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											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 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							str=1):



											"""simple docstring"""







											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 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							Union[str, Any]):



											"""simple docstring"""







											TrainingJobAnalytics(_A).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""")





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.create_estimator()

											# run training
											estimator.fit()

											# result dataframe
											_SCREAMING_SNAKE_CASE		:						Any			  =    TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

											# extract kpis
											_SCREAMING_SNAKE_CASE		:						Any			  =    list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    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		:						int			  =    (
											    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}      ,    _A)


