from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)


import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

lowercase_     :   Optional[Any] 				=				[
    'cross_validation.py',
    'gradient_accumulation.py',
    'local_sgd.py',
    'multi_process_metrics.py',
    'memory.py',
    'automatic_gradient_accumulation.py',
    'fsdp_with_peak_mem_tracking.py',
    'deepspeed_with_config_support.py',
    'megatron_lm_gpt_pretraining.py',
]






class 		_lowerCamelCase		(   unittest.TestCase    ):
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = None   )       ->					int:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      None
     SCREAMING_SNAKE_CASE__:   Dict=      os.path.abspath(os.path.join('''examples''' , '''by_feature'''   )   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      os.path.abspath('''examples'''   )
     for item in os.listdir(lowerCAmelCase   ):
      if item not in EXCLUDE_EXAMPLES:
       SCREAMING_SNAKE_CASE__:   Tuple=      os.path.join(lowerCAmelCase , lowerCAmelCase   )
       if os.path.isfile(lowerCAmelCase   ) and ".py" in item_path:
        with self.subTest(
            tested_script=lowerCAmelCase , feature_script=lowerCAmelCase , tested_section='''main()''' if parser_only else '''training_function()''' , ):
         SCREAMING_SNAKE_CASE__:   List[Any]=      compare_against_test(
             os.path.join(lowerCAmelCase , lowerCAmelCase   ) , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )
         SCREAMING_SNAKE_CASE__:   str=      '''\n'''.join(lowerCAmelCase   )
         if special_strings is not None:
          for string in special_strings:
           SCREAMING_SNAKE_CASE__:   Union[str, Any]=      diff.replace(lowerCAmelCase , ''''''   )
         self.assertEqual(lowerCAmelCase , ''''''   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     self.one_complete_example('''complete_nlp_example.py''' , lowerCAmelCase   )
     self.one_complete_example('''complete_nlp_example.py''' , lowerCAmelCase   )



    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   List[Any]=      os.path.abspath(os.path.join('''examples''' , '''cv_example.py'''   )   )
     SCREAMING_SNAKE_CASE__:   Dict=      [
         ''' ''' * 16 + '''{\n\n''',
         ''' ''' * 20 + '''"accuracy": eval_metric["accuracy"],\n\n''',
         ''' ''' * 20 + '''"f1": eval_metric["f1"],\n\n''',
         ''' ''' * 20 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
         ''' ''' * 20 + '''"epoch": epoch,\n\n''',
         ''' ''' * 16 + '''},\n\n''',
         ''' ''' * 16 + '''step=epoch,\n''',
         ''' ''' * 12,
         ''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
     ]
     self.one_complete_example('''complete_cv_example.py''' , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )
     self.one_complete_example('''complete_cv_example.py''' , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )






@mock.patch.dict(os.environ    ,     {"TESTING_MOCKED_DATALOADERS": "1"}    )
class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      False
    @classmethod
    def 						UpperCamelCase_				(  cls   )       ->					List[Any]:
     super().setUpClass()
     SCREAMING_SNAKE_CASE__:   List[Any]=      tempfile.mkdtemp()
     SCREAMING_SNAKE_CASE__:   Tuple=      os.path.join(cls._tmpdir , '''default_config.yml'''   )

     write_basic_config(save_location=cls.configPath   )
     SCREAMING_SNAKE_CASE__:   Dict=      ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
    @classmethod
    def 						UpperCamelCase_				(  cls   )       ->					List[Any]:
     super().tearDownClass()
     shutil.rmtree(cls._tmpdir   )
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   Dict=      f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
     run_command(self._launch_args + testargs   )
     self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0'''   )   )   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   List[Any]=      f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
     SCREAMING_SNAKE_CASE__:   List[Any]=      run_command(self._launch_args + testargs   )
     self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2'''   )   )   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   Tuple=      f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir , "epoch_0"   )}\n        '.split()
     SCREAMING_SNAKE_CASE__:   Dict=      run_command(self._launch_args + testargs , return_stdout=lowerCAmelCase   )
     self.assertNotIn('''epoch 0:''' , lowerCAmelCase   )
     self.assertIn('''epoch 1:''' , lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Dict=      f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir , "step_2"   )}\n        '.split()
     SCREAMING_SNAKE_CASE__:   Dict=      run_command(self._launch_args + testargs , return_stdout=lowerCAmelCase   )
     if torch.cuda.is_available():
      SCREAMING_SNAKE_CASE__:   List[str]=      torch.cuda.device_count()
     else:
      SCREAMING_SNAKE_CASE__:   List[Any]=      1
     if num_processes > 1:
      self.assertNotIn('''epoch 0:''' , lowerCAmelCase   )
      self.assertIn('''epoch 1:''' , lowerCAmelCase   )
     else:
      self.assertIn('''epoch 0:''' , lowerCAmelCase   )
      self.assertIn('''epoch 1:''' , lowerCAmelCase   )
    @slow
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   List[str]=      '''
        examples/by_feature/cross_validation.py
        --num_folds 2
        '''.split()
     with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''}   ):
      SCREAMING_SNAKE_CASE__:   Any=      run_command(self._launch_args + testargs , return_stdout=lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Dict=      re.findall('''({.+})''' , lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   int=      [r for r in results if '''accuracy''' in r][-1]
      SCREAMING_SNAKE_CASE__:   List[str]=      ast.literal_eval(lowerCAmelCase   )
      self.assertGreaterEqual(results['''accuracy'''] , 0.75   )
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      ['''examples/by_feature/multi_process_metrics.py''']
     run_command(self._launch_args + testargs   )
    @require_trackers
    @mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''}   )
    def 						UpperCamelCase_				(  self   )       ->					int:
     with tempfile.TemporaryDirectory() as tmpdir:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      f'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
      run_command(self._launch_args + testargs   )
      self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase , '''tracking'''   )   )   )
    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   int=      ['''examples/by_feature/gradient_accumulation.py''']
     run_command(self._launch_args + testargs   )



    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   List[str]=      ['''examples/by_feature/local_sgd.py''']
     run_command(self._launch_args + testargs   )

