



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 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''': 6_5_0, '''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''': 6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]						)
class       __lowerCAmelCase   (      unittest.TestCase						):



			def    _lowerCamelCase	(						self       :  List[Any])       ->   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       :  Any			,		A       :  Tuple=1)       ->   List[str]:



						"""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       :  Dict			,		A       :  int)       ->   str:



						"""simple docstring"""






						TrainingJobAnalytics(A).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv")



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.create_estimator()

						# run training
						estimator.fit()

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

						# extract kpis
						_UpperCAmelCase			=       list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'])
						_UpperCAmelCase			=       list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'])
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						_UpperCAmelCase			=       (
						    Session().describe_training_job(estimator.latest_training_job.name).get('TrainingTimeInSeconds'			,		99_99_99)
						)

						# 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)
