

"""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': 6_50, '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_00, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]     )
class    _UpperCAmelCase(   unittest.TestCase     ):



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






							'''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 						UpperCAmelCase							( self    ,		__a=1)	->	Dict:






							'''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 						UpperCAmelCase							( self    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							TrainingJobAnalytics(__a).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv''')




		def 						UpperCAmelCase							( self)	->	int:






							'''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)








"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a														=		logging.get_logger(__name__)

_a														=		{
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'levit'



		def __init__( self    ,		__a=2_24    ,		__a=3    ,		__a=3    ,		__a=2    ,		__a=1    ,		__a=16    ,		__a=[1_28, 2_56, 3_84]    ,		__a=[4, 8, 12]    ,		__a=[4, 4, 4]    ,		__a=[16, 16, 16]    ,		__a=0    ,		__a=[2, 2, 2]    ,		__a=[2, 2, 2]    ,		__a=0.02    ,		**__a    ,		)	->	List[str]:






							'''simple docstring'''
							super().__init__(**__a)
							_UpperCamelCase										=  image_size
							_UpperCamelCase										=  num_channels
							_UpperCamelCase										=  kernel_size
							_UpperCamelCase										=  stride
							_UpperCamelCase										=  padding
							_UpperCamelCase										=  hidden_sizes
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  depths
							_UpperCamelCase										=  key_dim
							_UpperCamelCase										=  drop_path_rate
							_UpperCamelCase										=  patch_size
							_UpperCamelCase										=  attention_ratio
							_UpperCamelCase										=  mlp_ratio
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  [
							    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
							    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
							]






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				version.parse('1.11'     )



		@property
		def 						UpperCAmelCase							( self)	->	Mapping[str, Mapping[int, str]]:






							'''simple docstring'''
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ])




		@property
		def 						UpperCAmelCase							( self)	->	float:






							'''simple docstring'''
							return 1e-4






