

from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


lowercase_									=    {
    "id": "5733be284776f41900661182",
    "title": "University_of_Notre_Dame",
    "context": "Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.",
    "question": "To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?",
    "answers": {"text": ["Saint Bernadette Soubirous"], "answer_start": [515]},
}
@pytest.mark.parametrize(
    'task_cls'       ,     [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]       ,     )
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[str] )				-> List[str]:





    '''simple docstring'''






    A__        =  task_cls()
    A__        =  asdict(UpperCamelCase__ )
    A__        =  task_template_from_dict(UpperCamelCase__ )
    assert task == reloaded


class 					A		:







    """simple docstring"""





    def 		snake_case__	( self					:			Optional[Any]						)->       Optional[Any]:





        '''simple docstring'''







        A__        =  LanguageModeling(text_column='input_text'						)
        assert {"input_text": "text"} == task.column_mapping


    def 		snake_case__	( self					:			str						)->       str:





        '''simple docstring'''







        A__        =  Features({'text': Value('string'						)}						)
        A__        =  {'''text_column''': '''input_text'''}
        A__        =  LanguageModeling.from_dict(_UpperCAmelCase						)
        assert "language-modeling" == task.task
        assert input_schema == task.input_schema




class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    def 		snake_case__	( self					:			int						)->       str:





        '''simple docstring'''







        A__        =  sorted(['pos', 'neg']						)


    def 		snake_case__	( self					:			Union[str, Any]						)->       Optional[int]:





        '''simple docstring'''







        A__        =  TextClassification(text_column='input_text',label_column='input_label'						)
        self.assertDictEqual({'input_text': 'text', 'input_label': 'labels'},task.column_mapping						)


    def 		snake_case__	( self					:			Any						)->       List[Any]:





        '''simple docstring'''







        A__        =  Features({'text': Value('string'						)}						)
        # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
        A__        =  Features({'labels': ClassLabel}						)
        A__        =  {'''text_column''': '''input_text''', '''label_column''': '''input_labels'''}
        A__        =  TextClassification.from_dict(_UpperCAmelCase						)
        self.assertEqual('text-classification',task.task						)
        self.assertEqual(_UpperCAmelCase,task.input_schema						)
        self.assertEqual(_UpperCAmelCase,task.label_schema						)


    def 		snake_case__	( self					:			Dict						)->       Tuple:





        '''simple docstring'''







        A__        =  TextClassification(text_column='input_text',label_column='input_label'						)
        self.assertEqual(task.label_schema['labels'],_UpperCAmelCase						)
        A__        =  task.align_with_features(Features({'input_label': ClassLabel(names=self.labels						)}						)						)
        self.assertEqual(task.label_schema['labels'],ClassLabel(names=self.labels						)						)




class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    def 		snake_case__	( self					:			str						)->       Dict:





        '''simple docstring'''







        A__        =  QuestionAnsweringExtractive(
            context_column='input_context',question_column='input_question',answers_column='input_answers'						)
        self.assertDictEqual(
            {'input_context': 'context', 'input_question': 'question', 'input_answers': 'answers'},task.column_mapping						)


    def 		snake_case__	( self					:			Any						)->       Any:





        '''simple docstring'''







        A__        =  Features({'question': Value('string'						), 'context': Value('string'						)}						)
        A__        =  Features(
            {
                'answers': Sequence(
                    {
                        'text': Value('string'						),
                        'answer_start': Value('int32'						),
                    }						)
            }						)
        A__        =  {
            '''context_column''': '''input_input_context''',
            '''question_column''': '''input_question''',
            '''answers_column''': '''input_answers''',
        }
        A__        =  QuestionAnsweringExtractive.from_dict(_UpperCAmelCase						)
        self.assertEqual('question-answering-extractive',task.task						)
        self.assertEqual(_UpperCAmelCase,task.input_schema						)
        self.assertEqual(_UpperCAmelCase,task.label_schema						)




class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    def 		snake_case__	( self					:			List[str]						)->       Optional[Any]:





        '''simple docstring'''







        A__        =  Summarization(text_column='input_text',summary_column='input_summary'						)
        self.assertDictEqual({'input_text': 'text', 'input_summary': 'summary'},task.column_mapping						)


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





        '''simple docstring'''







        A__        =  Features({'text': Value('string'						)}						)
        A__        =  Features({'summary': Value('string'						)}						)
        A__        =  {'''text_column''': '''input_text''', '''summary_column''': '''input_summary'''}
        A__        =  Summarization.from_dict(_UpperCAmelCase						)
        self.assertEqual('summarization',task.task						)
        self.assertEqual(_UpperCAmelCase,task.input_schema						)
        self.assertEqual(_UpperCAmelCase,task.label_schema						)




class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





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





        '''simple docstring'''







        A__        =  AutomaticSpeechRecognition(audio_column='input_audio',transcription_column='input_transcription'						)
        self.assertDictEqual({'input_audio': 'audio', 'input_transcription': 'transcription'},task.column_mapping						)


    def 		snake_case__	( self					:			Dict						)->       Tuple:





        '''simple docstring'''







        A__        =  Features({'audio': Audio()}						)
        A__        =  Features({'transcription': Value('string'						)}						)
        A__        =  {
            '''audio_column''': '''input_audio''',
            '''transcription_column''': '''input_transcription''',
        }
        A__        =  AutomaticSpeechRecognition.from_dict(_UpperCAmelCase						)
        self.assertEqual('automatic-speech-recognition',task.task						)
        self.assertEqual(_UpperCAmelCase,task.input_schema						)
        self.assertEqual(_UpperCAmelCase,task.label_schema						)




class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    def 		snake_case__	( self					:			Tuple						)->       Any:





        '''simple docstring'''







        A__        =  sorted(['pos', 'neg']						)


    def 		snake_case__	( self					:			Optional[int]						)->       Tuple:





        '''simple docstring'''







        A__        =  AudioClassification(audio_column='input_audio',label_column='input_label'						)
        self.assertDictEqual({'input_audio': 'audio', 'input_label': 'labels'},task.column_mapping						)


    def 		snake_case__	( self					:			List[Any]						)->       Any:





        '''simple docstring'''







        A__        =  Features({'audio': Audio()}						)
        A__        =  Features({'labels': ClassLabel}						)
        A__        =  {
            '''audio_column''': '''input_image''',
            '''label_column''': '''input_label''',
        }
        A__        =  AudioClassification.from_dict(_UpperCAmelCase						)
        self.assertEqual('audio-classification',task.task						)
        self.assertEqual(_UpperCAmelCase,task.input_schema						)
        self.assertEqual(_UpperCAmelCase,task.label_schema						)


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





        '''simple docstring'''







        A__        =  AudioClassification(audio_column='input_audio',label_column='input_label'						)
        self.assertEqual(task.label_schema['labels'],_UpperCAmelCase						)
        A__        =  task.align_with_features(Features({'input_label': ClassLabel(names=self.labels						)}						)						)
        self.assertEqual(task.label_schema['labels'],ClassLabel(names=self.labels						)						)




class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    def 		snake_case__	( self					:			str						)->       Dict:





        '''simple docstring'''







        A__        =  sorted(['pos', 'neg']						)


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





        '''simple docstring'''







        A__        =  ImageClassification(image_column='input_image',label_column='input_label'						)
        self.assertDictEqual({'input_image': 'image', 'input_label': 'labels'},task.column_mapping						)


    def 		snake_case__	( self					:			List[str]						)->       Any:





        '''simple docstring'''







        A__        =  Features({'image': Image()}						)
        A__        =  Features({'labels': ClassLabel}						)
        A__        =  {
            '''image_column''': '''input_image''',
            '''label_column''': '''input_label''',
        }
        A__        =  ImageClassification.from_dict(_UpperCAmelCase						)
        self.assertEqual('image-classification',task.task						)
        self.assertEqual(_UpperCAmelCase,task.input_schema						)
        self.assertEqual(_UpperCAmelCase,task.label_schema						)


    def 		snake_case__	( self					:			Dict						)->       int:





        '''simple docstring'''







        A__        =  ImageClassification(image_column='input_image',label_column='input_label'						)
        self.assertEqual(task.label_schema['labels'],_UpperCAmelCase						)
        A__        =  task.align_with_features(Features({'input_label': ClassLabel(names=self.labels						)}						)						)
        self.assertEqual(task.label_schema['labels'],ClassLabel(names=self.labels						)						)




class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    def 		snake_case__	( self					:			int						)->       List[str]:





        '''simple docstring'''







        A__        =  DatasetInfo(task_templates=QuestionAnsweringExtractive()						)
        A__        =  Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()},info=_UpperCAmelCase						)
        assert isinstance(dataset.info.task_templates,_UpperCAmelCase						)
        assert len(dataset.info.task_templates						) == 1

        def keep_task(lowercase_					:			int						):
            return x

        def dont_keep_task(lowercase_					:			Any						):
            A__        =  deepcopy(_UpperCAmelCase						)
            A__        =  0
            return out

        A__        =  dataset.map(_UpperCAmelCase						)
        assert mapped_dataset.info.task_templates == dataset.info.task_templates
        # reload from cache
        A__        =  dataset.map(_UpperCAmelCase						)
        assert mapped_dataset.info.task_templates == dataset.info.task_templates

        A__        =  dataset.map(_UpperCAmelCase						)
        assert mapped_dataset.info.task_templates == []
        # reload from cache
        A__        =  dataset.map(_UpperCAmelCase						)
        assert mapped_dataset.info.task_templates == []


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





        '''simple docstring'''







        A__        =  Features({'text': Value('string'						), 'label': ClassLabel(names=('pos', 'neg')						)}						)
        A__        =  TextClassification(text_column='text',label_column='label'						)
        A__        =  DatasetInfo(features=_UpperCAmelCase,task_templates=_UpperCAmelCase						)
        A__        =  Dataset.from_dict({'text': ['A sentence.'], 'label': ['pos']},info=_UpperCAmelCase						)

        def process(lowercase_					:			List[Any]						):
            return example

        A__        =  dataset.remove_columns('label'						)
        A__        =  modified_dataset.map(_UpperCAmelCase						)
        assert mapped_dataset.info.task_templates == []








'''simple docstring'''






import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
     from tests.trainer.test_trainer import (  # noqa
         RegressionModelConfig,
         RegressionPreTrainedModel,
         get_regression_trainer,
     )


set_seed(42)

snake_case_     			=						get_tests_dir("""fixtures""")
snake_case_     			=						os.path.join(dirname(get_tests_dir()))
snake_case_     			=						dirname(os.path.abspath(__file__))

# default torch.distributed port
snake_case_     			=						"""10999"""

snake_case_     			=						"""t5-small"""

# *** Working Models ***
snake_case_     			=						"""hf-internal-testing/tiny-albert"""
snake_case_     			=						"""sshleifer/bart-tiny-random"""
snake_case_     			=						"""hf-internal-testing/tiny-bert"""
snake_case_     			=						"""hf-internal-testing/tiny-random-bigbird_pegasus"""
snake_case_     			=						"""hf-internal-testing/tiny-random-big_bird"""
snake_case_     			=						"""hf-internal-testing/tiny-random-blenderbot"""
snake_case_     			=						"""bigscience/bigscience-small-testing"""
snake_case_     			=						"""hf-internal-testing/tiny-random-deberta"""
snake_case_     			=						"""hf-internal-testing/tiny-random-deberta-v2"""
snake_case_     			=						"""sshleifer/tiny-distilbert-base-cased"""
snake_case_     			=						"""hf-internal-testing/tiny-electra"""
snake_case_     			=						"""hf-internal-testing/tiny-random-flaubert"""
snake_case_     			=						"""stas/tiny-wmt19-en-de"""
snake_case_     			=						"""hf-internal-testing/tiny-random-funnel"""
snake_case_     			=						"""sshleifer/tiny-gpt2"""
snake_case_     			=						"""hf-internal-testing/tiny-random-gptj"""
snake_case_     			=						"""hf-internal-testing/tiny-random-gpt_neo"""
snake_case_     			=						"""hf-internal-testing/tiny-layoutlm"""
snake_case_     			=						"""hf-internal-testing/tiny-random-led"""
snake_case_     			=						"""hf-internal-testing/tiny-random-longformer"""
snake_case_     			=						"""stas/tiny-m2m_100"""  # hf tiny model is unsuitable
snake_case_     			=						"""sshleifer/tiny-marian-en-de"""
snake_case_     			=						"""sshleifer/tiny-mbart"""
snake_case_     			=						"""hf-internal-testing/tiny-random-mobilebert"""
snake_case_     			=						"""hf-internal-testing/tiny-random-mpnet"""
snake_case_     			=						"""stas/pegasus-cnn_dailymail-tiny-random"""
snake_case_     			=						"""hf-internal-testing/tiny-random-prophetnet"""
snake_case_     			=						"""sshleifer/tiny-distilroberta-base"""
snake_case_     			=						"""hf-internal-testing/tiny-random-squeezebert"""
snake_case_     			=						"""patrickvonplaten/t5-tiny-random"""
snake_case_     			=						"""hf-internal-testing/tiny-random-t5-v1.1"""
snake_case_     			=						"""hf-internal-testing/tiny-random-vit"""
snake_case_     			=						"""hf-internal-testing/tiny-xlm-roberta"""
snake_case_     			=						"""sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
snake_case_     			=						"""hf-internal-testing/tiny-random-mt5"""
snake_case_     			=						"""hf-internal-testing/tiny-random-camembert"""
snake_case_     			=						"""hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
snake_case_     			=						"""hf-internal-testing/tiny-random-convbert"""
snake_case_     			=						"""hf-internal-testing/tiny-random-layoutlmv2"""
snake_case_     			=						"""hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
snake_case_     			=						"""hf-internal-testing/tiny-random-ctrl"""
snake_case_     			=						"""hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
snake_case_     			=						"""hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
snake_case_     			=						"""hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
snake_case_     			=						"""hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
snake_case_     			=						"""hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
snake_case_     			=						""""""
# - "luke" Entities classes - no plan to make such example
snake_case_     			=						"""hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
snake_case_     			=						"""hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
snake_case_     			=						"""hf-internal-testing/tiny-random-speech_to_text"""
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
snake_case_     			=						"""hf-internal-testing/tiny-random-tapas"""
def 				_lowerCamelCase(	UpperCamelCase__     :			str=False		)		->	List[str]:
    # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
    # - it won't be able to handle that
    # 2. for now testing with just 2 gpus max (since some quality tests may give different
    # results with mode gpus because we use very little data)
    A							:							int				=    min(2     ,							get_gpu_count()		) if distributed else 1
    A							:							Dict				=    os.environ.get('''DS_TEST_PORT'''     ,							UpperCamelCase__		)
    return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()
def 				_lowerCamelCase(	)		->	Any:
    A							:							Tuple				=    F'''{FIXTURE_DIRECTORY}/tests_samples'''
    A							:							int				=    F'''{data_dir_samples}/wmt_en_ro'''
    A							:							List[str]				=    F'''{data_dir_samples}/xsum'''
    A							:							int				=    '''
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        '''.split()

    # try to cover as many models as possible once (it's enough to run on one task per model)
    # but need a tiny model for each
    #
    # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
    A							:							List[str]				=    {
        '''trans''': [
            '''bart''',
            '''fsmt''',
            '''m2m_100''',
            '''marian''',
            '''mbart''',
            '''t5''',
            '''t5_v1''',
            # "mt5", missing model files
        ],
        '''sum''': [
            '''pegasus''',
        ],
        '''clm''': [
            '''big_bird''',
            '''bigbird_pegasus''',
            '''blenderbot''',
            '''bloom''',
            '''gpt2''',
            '''gpt_neo''',
            '''gptj''',
            '''xlm-roberta''',
            '''prophetnet''',
            # "camembert", missing model files
        ],
        '''mlm''': [
            '''albert''',
            '''deberta''',
            '''deberta-v2''',
            '''distilbert''',
            '''electra''',
            '''flaubert''',
            '''funnel''',
            '''layoutlm''',
            # "reformer", # multiple issues with either mlm/qa/clas
        ],
        '''qa''': [
            '''led''',
            '''longformer''',
            '''mobilebert''',
            '''mpnet''',
            '''roberta''',
            '''squeezebert''',
            # "convbert", # missing tokenizer files
            # "layoutlmv2", missing model files
        ],
        '''clas''': [
            '''bert''',
            '''xlnet''',
            # "hubert", # missing tokenizer files
            # "ibert", # multiple issues with either mlm/qa/clas
            # "transfo-xl", # tokenizer issues as ctrl
            # "ctrl", # tokenizer issues
            # "openai-gpt", missing model files
            # "tapas", multiple issues
        ],
        '''img_clas''': [
            '''vit''',
        ],
    }

    A							:							int				=    F'''{ROOT_DIRECTORY}/examples/pytorch'''

    A							:							List[str]				=    {
        '''trans''': F'''
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        ''',
        '''sum''': F'''
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        ''',
        '''clm''': F'''
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        ''',
        '''mlm''': F'''
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        ''',
        '''qa''': F'''
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        ''',
        '''clas''': F'''
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        ''',
        '''img_clas''': F'''
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        ''',
    }

    A							:							int				=    get_launcher(distributed=UpperCamelCase__		)

    A							:							Optional[Any]				=    {}
    for task, args in tasks.items():
        A							:							Dict				=    args.split()
        for model in tasksamodels[task]:
            A							:							Dict				=    globals()[F'''{model.upper().replace('-'     ,							'_'		)}_TINY''']
            A							:							List[str]				=    F'''--model_name_or_path {model_name}'''.split()
            A							:							List[Any]				=    launcher + args + args_model + args_main

            # # generation special case
            # if task == "gen":
            #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
            #     args_model += f"--model_type {model}".split()
            #     cmds[f"{task}_{model}"] = launcher + args + args_model
            # else:

    return cmds


snake_case_     			=						make_task_cmds()

snake_case_     			=						"""zero2"""
snake_case_     			=						"""zero3"""

snake_case_     			=						[ZEROa, ZEROa]
def 				_lowerCamelCase(	UpperCamelCase__     :			Tuple     ,							UpperCamelCase__     :			int     ,							UpperCamelCase__     :			Optional[int]		)		->	Any:
    # customize the test name generator function as we want both params to appear in the sub-test
    # name, as by default it shows only the first param
    A							:							Union[str, Any]				=    parameterized.to_safe_name('''_'''.join(str(UpperCamelCase__		) for x in param.args		)		)
    return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
snake_case_     			=						list(itertools.product(stages, task_cmds.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 	_lowercase    (				a					):


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase       ):
       # return a ready to run train cmd
       if task not in task_cmds:
           raise ValueError(f'''don\'t know of task {task}, have {task_cmds.keys()}'''       )

       A							:							List[str]				=    task_cmds[task]
       A							:							Dict				=    f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

       A							:							Optional[Any]				=    self.get_auto_remove_tmp_dir()
       A							:							Union[str, Any]				=    f'''--output_dir {output_dir}'''.split()

       cmd += args_ds + args_out

       return cmd, output_dir


   @parameterized.expand(_UpperCAmelCase     , name_func=_UpperCAmelCase       )
   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase       ):
       # testing the ability to do a run followed by recovery of full fp32 weights

       A,						A							:							List[str]				=    self.get_task_cmd(_UpperCAmelCase     , _UpperCAmelCase       )

       # 1. generate the checkpoint
       cmd += "--save_steps 1".split()
       # keep for quick debug
       # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
       execute_subprocess_async(_UpperCAmelCase     , env=self.get_env()       )

       # 2. test that the fp32 weights get reconsolidated
       A							:							Dict				=    f'''{output_dir}/checkpoint-1'''
       A							:							Optional[Any]				=    f'''{chkpt_dir}/out.bin'''
       A							:							Optional[Any]				=    f'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
       # keep for quick debug
       # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
       subprocess.check_call(_UpperCAmelCase     , shell=_UpperCAmelCase       )
       assert os.path.exists(_UpperCAmelCase       ), f'''{recovered_model_path} was not found'''

       # possibly could also test that the resulting saved model is usable but given that we use
       # random models we won't know if it's any good
