






"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AlignProcessor, EfficientNetImageProcessor




@require_vision
class    lowercase_    (unittest.TestCase     ):






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        _a							=	tempfile.mkdtemp()

        _a							=	[
            '''[UNK]''',
            '''[CLS]''',
            '''[SEP]''',
            '''[PAD]''',
            '''[MASK]''',
            '''want''',
            '''##want''',
            '''##ed''',
            '''wa''',
            '''un''',
            '''runn''',
            '''##ing''',
            ''',''',
            '''low''',
            '''lowest''',
        ]
        _a							=	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]	)	)

        _a							=	{
            '''do_resize''': True,
            '''size''': 2_0,
            '''do_center_crop''': True,
            '''crop_size''': 1_8,
            '''do_normalize''': True,
            '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
            '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
        }
        _a							=	os.path.join(self.tmpdirname     ,     _UpperCAmelCase	)
        with open(self.image_processor_file     ,     "w"     ,     encoding="utf-8"	) as fp:
          json.dump(_UpperCAmelCase     ,     _UpperCAmelCase	)






      def 			lowerCamelCase__    (    self     ,     **a_	)	->Dict:







        '''simple docstring'''


        return BertTokenizer.from_pretrained(self.tmpdirname     ,     **_UpperCAmelCase	)






      def 			lowerCamelCase__    (    self     ,     **a_	)	->int:







        '''simple docstring'''


        return BertTokenizerFast.from_pretrained(self.tmpdirname     ,     **_UpperCAmelCase	)






      def 			lowerCamelCase__    (    self     ,     **a_	)	->int:







        '''simple docstring'''


        return EfficientNetImageProcessor.from_pretrained(self.tmpdirname     ,     **_UpperCAmelCase	)






      def 			lowerCamelCase__    (    self	)	->List[str]:







        '''simple docstring'''


        shutil.rmtree(self.tmpdirname	)






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a							=	[np.random.randint(2_5_5     ,     size=(3, 3_0, 4_0_0)     ,     dtype=np.uinta	)]
        _a							=	[Image.fromarray(np.moveaxis(_UpperCAmelCase     ,     0     ,     -1	)	) for x in image_inputs]
        return image_inputs






      def 			lowerCamelCase__    (    self	)	->Tuple:







        '''simple docstring'''


        _a							=	self.get_tokenizer()
        _a							=	self.get_rust_tokenizer()
        _a							=	self.get_image_processor()

        _a							=	AlignProcessor(tokenizer=_UpperCAmelCase     ,     image_processor=_UpperCAmelCase	)
        processor_slow.save_pretrained(self.tmpdirname	)
        _a							=	AlignProcessor.from_pretrained(self.tmpdirname     ,     use_fast=_UpperCAmelCase	)

        _a							=	AlignProcessor(tokenizer=_UpperCAmelCase     ,     image_processor=_UpperCAmelCase	)
        processor_fast.save_pretrained(self.tmpdirname	)
        _a							=	AlignProcessor.from_pretrained(self.tmpdirname	)

        self.assertEqual(processor_slow.tokenizer.get_vocab()     ,     tokenizer_slow.get_vocab()	)
        self.assertEqual(processor_fast.tokenizer.get_vocab()     ,     tokenizer_fast.get_vocab()	)
        self.assertEqual(tokenizer_slow.get_vocab()     ,     tokenizer_fast.get_vocab()	)
        self.assertIsInstance(processor_slow.tokenizer     ,     _UpperCAmelCase	)
        self.assertIsInstance(processor_fast.tokenizer     ,     _UpperCAmelCase	)

        self.assertEqual(processor_slow.image_processor.to_json_string()     ,     image_processor.to_json_string()	)
        self.assertEqual(processor_fast.image_processor.to_json_string()     ,     image_processor.to_json_string()	)
        self.assertIsInstance(processor_slow.image_processor     ,     _UpperCAmelCase	)
        self.assertIsInstance(processor_fast.image_processor     ,     _UpperCAmelCase	)






      def 			lowerCamelCase__    (    self	)	->Optional[int]:







        '''simple docstring'''


        _a							=	AlignProcessor(tokenizer=self.get_tokenizer()     ,     image_processor=self.get_image_processor()	)
        processor.save_pretrained(self.tmpdirname	)

        _a							=	self.get_tokenizer(bos_token="(BOS)"     ,     eos_token="(EOS)"	)
        _a							=	self.get_image_processor(do_normalize=_UpperCAmelCase     ,     padding_value=1.0	)

        _a							=	AlignProcessor.from_pretrained(
            self.tmpdirname     ,     bos_token="(BOS)"     ,     eos_token="(EOS)"     ,     do_normalize=_UpperCAmelCase     ,     padding_value=1.0	)

        self.assertEqual(processor.tokenizer.get_vocab()     ,     tokenizer_add_kwargs.get_vocab()	)
        self.assertIsInstance(processor.tokenizer     ,     _UpperCAmelCase	)

        self.assertEqual(processor.image_processor.to_json_string()     ,     image_processor_add_kwargs.to_json_string()	)
        self.assertIsInstance(processor.image_processor     ,     _UpperCAmelCase	)






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        _a							=	self.get_image_processor()
        _a							=	self.get_tokenizer()

        _a							=	AlignProcessor(tokenizer=_UpperCAmelCase     ,     image_processor=_UpperCAmelCase	)

        _a							=	self.prepare_image_inputs()

        _a							=	image_processor(_UpperCAmelCase     ,     return_tensors="np"	)
        _a							=	processor(images=_UpperCAmelCase     ,     return_tensors="np"	)

        for key in input_image_proc.keys():
          self.assertAlmostEqual(input_image_proc[key].sum()     ,     input_processor[key].sum()     ,     delta=1E-2	)






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        _a							=	self.get_image_processor()
        _a							=	self.get_tokenizer()

        _a							=	AlignProcessor(tokenizer=_UpperCAmelCase     ,     image_processor=_UpperCAmelCase	)

        _a							=	'''lower newer'''

        _a							=	processor(text=_UpperCAmelCase	)

        _a							=	tokenizer(_UpperCAmelCase     ,     padding="max_length"     ,     max_length=6_4	)

        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]     ,     encoded_processor[key]	)






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        _a							=	self.get_image_processor()
        _a							=	self.get_tokenizer()

        _a							=	AlignProcessor(tokenizer=_UpperCAmelCase     ,     image_processor=_UpperCAmelCase	)

        _a							=	'''lower newer'''
        _a							=	self.prepare_image_inputs()

        _a							=	processor(text=_UpperCAmelCase     ,     images=_UpperCAmelCase	)

        self.assertListEqual(list(inputs.keys()	)     ,     ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]	)

        # test if it raises when no input is passed
        with pytest.raises(_UpperCAmelCase	):
          processor()






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        _a							=	self.get_image_processor()
        _a							=	self.get_tokenizer()

        _a							=	AlignProcessor(tokenizer=_UpperCAmelCase     ,     image_processor=_UpperCAmelCase	)

        _a							=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _a							=	processor.batch_decode(_UpperCAmelCase	)
        _a							=	tokenizer.batch_decode(_UpperCAmelCase	)

        self.assertListEqual(_UpperCAmelCase     ,     _UpperCAmelCase	)






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        _a							=	self.get_image_processor()
        _a							=	self.get_tokenizer()

        _a							=	AlignProcessor(tokenizer=_UpperCAmelCase     ,     image_processor=_UpperCAmelCase	)

        _a							=	'''lower newer'''
        _a							=	self.prepare_image_inputs()

        _a							=	processor(text=_UpperCAmelCase     ,     images=_UpperCAmelCase	)

        self.assertListEqual(list(inputs.keys()	)     ,     processor.model_input_names	)










"""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''': 650, '''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''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]     )
class    lowercase_    (unittest.TestCase     ):






      def 			lowerCamelCase__    (    self	)	->Union[str, Any]:







        '''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     ,     a_=1	)	->int:







        '''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     ,     a_	)	->str:







        '''simple docstring'''


        TrainingJobAnalytics(a_	).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''	)






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        _a							=	self.create_estimator()

        # run training
        estimator.fit()

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

        # extract kpis
        _a							=	list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]	)
        _a							=	list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]	)
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        _a							=	(
            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_	)


