


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


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







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




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= XGLMTokenizer
     __SCREAMING_SNAKE_CASE  			= XGLMTokenizerFast
     __SCREAMING_SNAKE_CASE  			= True
     __SCREAMING_SNAKE_CASE  			= True


     def  _lowerCamelCase (						self)			->				Dict:
            super().setUp()

            # We have a SentencePiece fixture for testing
            _A      :       Union[str, Any]				    = XGLMTokenizer(__lowerCamelCase ,						keep_accents=__lowerCamelCase)
            tokenizer.save_pretrained(self.tmpdirname)


     def  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       List[Any]				    = "<pad>"
            _A      :       Any				    = 1

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


     def  _lowerCamelCase (						self)			->				int:
            _A      :       List[str]				    = list(self.get_tokenizer().get_vocab().keys())

            self.assertEqual(vocab_keys[0] ,						"<s>")
            self.assertEqual(vocab_keys[1] ,						"<pad>")
            self.assertEqual(len(__lowerCamelCase) ,						1_0_0_8)


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            self.assertEqual(self.get_tokenizer().vocab_size ,						1_0_0_8)


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       List[str]				    = XGLMTokenizer(__lowerCamelCase ,						keep_accents=__lowerCamelCase)

            _A      :       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 [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] ,						)

            _A      :       Optional[int]				    = 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",
                    ".",
                ] ,						)
            _A      :       Dict				    = tokenizer.convert_tokens_to_ids(__lowerCamelCase)
            self.assertListEqual(
                __lowerCamelCase ,						[
                    value + tokenizer.fairseq_offset
                    for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
                ] ,						)

            _A      :       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>",
                    ".",
                ] ,						)


     @cached_property
     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            return XGLMTokenizer.from_pretrained("facebook/xglm-564M")


     def  _lowerCamelCase (						self)			->				str:
            with tempfile.NamedTemporaryFile() as f:
                   shutil.copyfile(__lowerCamelCase ,						f.name)
                   _A      :       Optional[Any]				    = XGLMTokenizer(f.name ,						keep_accents=__lowerCamelCase)
                   _A      :       Optional[int]				    = pickle.dumps(__lowerCamelCase)
            pickle.loads(__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				str:
            if not self.test_rust_tokenizer:
                   return

            _A      :       str				    = self.get_tokenizer()
            _A      :       Optional[int]				    = self.get_rust_tokenizer()

            _A      :       str				    = "I was born in 92000, and this is falsé."

            _A      :       List[str]				    = tokenizer.tokenize(__lowerCamelCase)
            _A      :       int				    = rust_tokenizer.tokenize(__lowerCamelCase)
            self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase)

            _A      :       Dict				    = tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase)
            _A      :       Tuple				    = rust_tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase)
            self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase)

            _A      :       int				    = self.get_rust_tokenizer()
            _A      :       Tuple				    = tokenizer.encode(__lowerCamelCase)
            _A      :       str				    = rust_tokenizer.encode(__lowerCamelCase)
            self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase)


     @slow
     def  _lowerCamelCase (						self)			->				List[str]:
            _A      :       int				    = "Hello World!"
            _A      :       Optional[int]				    = [2, 3_1_2_2_7, 4_4_4_7, 3_5]

            self.assertListEqual(__lowerCamelCase ,						self.big_tokenizer.encode(__lowerCamelCase))


     @slow
     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       List[str]				    = (
                "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
                " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
            )
            # fmt: off
            _A      :       Tuple				    = [2, 1_0_1_8, 6_7, 1_1, 1_9_8_8, 2_6_1_7, 5_6_3_1, 2_7_8, 1_1, 3_4_0_7, 4_8, 7_1_6_3_0, 2_8_0_8_5, 4, 3_2_3_4, 1_5_7, 1_3, 6, 5, 6, 4, 3_5_2_6, 7_6_8, 1_5, 6_5_9, 5_7, 2_9_8, 3_9_8_3, 8_6_4, 1_2_9, 2_1, 6, 5, 1_3_6_7_5, 3_7_7, 6_5_2, 7_5_8_0, 1_0_3_4_1, 1_5_5, 2_8_1_7, 4_2_2, 1_6_6_6, 7, 1_6_7_4, 5_3, 1_1_3, 2_0_2_2_7_7, 1_7_8_9_2, 3_3, 6_0, 8_7, 4, 3_2_3_4, 1_5_7, 6_1, 2_6_6_7, 5_2_3_7_6, 1_9, 8_8, 2_3, 7_3_5]
            # fmt: on

            self.assertListEqual(__lowerCamelCase ,						self.big_tokenizer.encode(__lowerCamelCase))


     @slow
     def  _lowerCamelCase (						self)			->				Tuple:
            # fmt: off
            _A      :       Dict				    = {
                "input_ids": [[2, 1_0_8_8_2_5, 1_1_6_3, 1_5, 8_8_0_1_0, 4_7_3, 1_5_8_9_8, 1_5_7, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 2_3_8_0_2_1, 1_1_6_3, 5_3, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 5_3_2_8_3, 1_8_2_3_9_6, 8, 1_8_5_6_6, 1_6, 3_6_7_3_3, 4_1_0_1, 8, 2_3_0, 2_4_4_0_1_7, 1_2_2_5_5_3, 7, 1_5, 1_3_2_5_9_7, 4, 2_9_3, 1_2_5_1_1, 7_6_1_0, 4, 3_4_1_4, 1_3_2_5_9_7, 9, 4, 3_2_3_6_1, 3_6_2, 4, 7_3_4, 2_8_5_1_2, 3_2_5_6_9, 1_8, 4, 3_2_3_6_1, 2_6_0_9_6, 1_4_9_8_2, 7_3, 1_8_7_1_5, 2_1_4_3_3, 2_3_5_2_6_1, 1_5, 4_9_2, 1_2_4_2_7, 1_6, 5_3, 1_8_7_1_5, 2_1_4_3_3, 6_5_4_5_4, 1_5, 2_3_6_5_9, 5_6_3, 1_6, 2_7_8, 5_9_7, 2_8_4_3, 5_9_5, 7_9_3_1, 1_8_2_3_9_6, 6_4_1_8_6, 2_2, 8_8_6, 5_9_5, 1_3_2_9_8_1, 5_3, 2_5_5_4_0, 3_4_4_9, 4_3_9_8_2, 3_9_9_0_1, 5_9_5_1, 8_7_8, 3_3_0, 4, 2_7_6_9_4, 8_0_2_6_9, 3_1_2, 5_3, 6_5_1_7, 1_1_7_8_0, 6_1_1, 2_0_4_0_8, 5], [2, 6, 1_3_2_5_9_7, 6_7, 4_2_8_9_7, 3_3, 5_9_2, 8, 1_6_3_7_2_9, 2_5_5_4_0, 3_6_1, 1_3_6_9_9_7, 1_0_9_5_1_4, 1_7_3_2_3_0, 7, 5_0_1, 6_0, 1_0_2_9_1_3, 1_9_6, 5_6_3_1, 2_3_5, 6_3_2_4_3, 4_7_3, 6, 2_3_1_7_5_7, 7_4, 5_2_7_7, 7_9_0_5, 5_3, 3_0_9_5, 3_7_3_1_7, 2_2, 4_5_4, 1_8_3_8_7_4, 5], [2, 2_6_8, 3_1_2_9_8, 4_6_5_3_0, 6, 1_3_2_9_3_5, 4_3_8_3_1, 7, 5_9_7, 3_2, 2_4, 3_6_8_8, 9_8_6_5, 5]],
                "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, 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, 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, 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]]
            }  # noqa: E501
            # fmt: on

            self.tokenizer_integration_test_util(
                expected_encoding=__lowerCamelCase ,						model_name="facebook/xglm-564M" ,						padding=__lowerCamelCase ,						)




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)
