'''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_model_parallelism.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 16_00, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 16_00, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]			)
class       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        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       ,				_a				)    ->     Union[str, Any]:
        # configuration for running training on smdistributed Model Parallel
        lowercase_					:       Union[str, Any]     				=					{
            'enabled': True,
            'processes_per_host': 8,
        }
        lowercase_					:       List[Any]     				=					{
            'enabled': True,
            'parameters': {
                'microbatches': 4,
                'placement_strategy': 'spread',
                'pipeline': 'interleaved',
                'optimize': 'speed',
                'partitions': 4,
                'ddp': True,
            },
        }

        lowercase_					:       str     				=					{'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

        lowercase_					:       str     				=					'trainer' if self.script == 'run_glue.py' else 'smtrainer'
        # 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}-{instance_count}-smp-{name_extension}'''       ,				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,
                'max_steps': 500,
            }       ,				metric_definitions=self.env.metric_definitions       ,				distribution=_a       ,				py_version='py36'       ,				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     Union[str, Any]:
        TrainingJobAnalytics(_a				).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''				)





   @parameterized.expand([(1,)]				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     int:
        # create estimator
        lowercase_					:       Any     				=					self.create_estimator(_a				)

        # run training
        estimator.fit()

        # result dataframe
        lowercase_					:       Optional[Any]     				=					TrainingJobAnalytics(estimator.latest_training_job.name				).dataframe()

        # extract kpis
        lowercase_					:       Optional[int]     				=					list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']				)
        lowercase_					:       str     				=					list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']				)
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        lowercase_					:       Optional[Any]     				=					(
            Session().describe_training_job(estimator.latest_training_job.name				).get('TrainingTimeInSeconds'       ,				999_999				)
        )

        # 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				)





'''simple docstring'''




import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       str     =    TransfoXLTokenizer
   A							:       Optional[int]     =    False
   A							:       List[str]     =    False
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        super().setUp()

        lowercase_					:       Any     				=					[
            '<unk>',
            '[CLS]',
            '[SEP]',
            'want',
            'unwanted',
            'wa',
            'un',
            'running',
            ',',
            'low',
            'l',
        ]
        lowercase_					:       Any     				=					os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['vocab_file']				)
        with open(self.vocab_file       ,				'w'       ,				encoding='utf-8'				) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]				)				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Union[str, Any]:
        lowercase_					:       str     				=					True
        return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     Union[str, Any]:
        lowercase_					:       int     				=					'<unk> UNwanted , running'
        lowercase_					:       int     				=					'<unk> unwanted, running'
        return input_text, output_text
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Any     				=					TransfoXLTokenizer(vocab_file=self.vocab_file       ,				lower_case=_a				)

        lowercase_					:       Optional[Any]     				=					tokenizer.tokenize('<unk> UNwanted , running'				)
        self.assertListEqual(_a       ,				['<unk>', 'unwanted', ',', 'running']				)

        self.assertListEqual(tokenizer.convert_tokens_to_ids(_a				)       ,				[0, 4, 8, 7]				)
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       List[str]     				=					TransfoXLTokenizer(lower_case=_a				)

        self.assertListEqual(
            tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '				)       ,				['hello', '!', 'how', 'are', 'you', '?']				)
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Dict     				=					TransfoXLTokenizer(lower_case=_a				)

        self.assertListEqual(
            tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '				)       ,				['HeLLo', '!', 'how', 'Are', 'yoU', '?']				)
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       Dict     				=					TransfoXLTokenizer(lower_case=_a				)
        lowercase_					:       int     				=					'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
        lowercase_					:       List[str]     				=					[
            'Hello',
            '(',
            'bracket',
            ')',
            'and',
            'side',
            '@-@',
            'scrolled',
            '[',
            'and',
            ']',
            'Henry',
            '\'s',
            '$',
            '5',
            '@,@',
            '000',
            'with',
            '3',
            '@.@',
            '34',
            'm',
            '.',
            'What',
            '\'s',
            'up',
            '!',
            '?',
        ]

        self.assertListEqual(tokenizer.tokenize(_a				)       ,				_a				)

        self.assertEqual(tokenizer.convert_tokens_to_string(_a				)       ,				_a				)





   def 		_lowerCamelCase (self				)    ->     Any:
        lowercase_					:       Optional[int]     				=					self.get_tokenizer()
        lowercase_					:       str     				=					len(_a				)

        tokenizer.add_tokens(['new1', 'new2']				)
        tokenizer.move_added_token('new1'       ,				1				)

        # Check that moved token is not copied (duplicate)
        self.assertEqual(len(_a				)       ,				original_len + 2				)
        # Check that token is moved to specified id
        self.assertEqual(tokenizer.encode('new1'				)       ,				[1]				)
        self.assertEqual(tokenizer.decode([1]				)       ,				'new1'				)




