

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  A						(							unittest.TestCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       List[str]		)->    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=lowercase_,)
        assert hasattr(self,'env'		)


    def 						snake_case__     (      self :       int,lowercase_ :       Optional[Any]=1		)->    Optional[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=lowercase_,instance_type=self.instance_type,debugger_hook_config=lowercase_,hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path},metric_definitions=self.env.metric_definitions,py_version='py36',)


    def 						snake_case__     (      self :       List[Any],lowercase_ :       List[Any]		)->    Optional[int]:





        '''simple docstring'''







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


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





        '''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},lowercase_		)








from collections import OrderedDict
from typing import Mapping

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


A    :					Union[str, Any] =      logging.get_logger(__name__)

A    :					int =      {
    '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/config.json''',
    '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/config.json''',
    '''xlm-roberta-large-finetuned-conll02-dutch''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll02-spanish''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-english''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-german''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'''
    ),
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Any							=    '''xlm-roberta'''






			def __init__(			self   :    Optional[Any]							,	__lowerCAmelCase   :    List[Any]=3_05_22							,	__lowerCAmelCase   :    int=7_68							,	__lowerCAmelCase   :    Tuple=12							,	__lowerCAmelCase   :    str=12							,	__lowerCAmelCase   :    Union[str, Any]=30_72							,	__lowerCAmelCase   :    Union[str, Any]="gelu"							,	__lowerCAmelCase   :    Optional[int]=0.1							,	__lowerCAmelCase   :    Dict=0.1							,	__lowerCAmelCase   :    List[str]=5_12							,	__lowerCAmelCase   :    Tuple=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=1e-12							,	__lowerCAmelCase   :    Union[str, Any]=1							,	__lowerCAmelCase   :    str=0							,	__lowerCAmelCase   :    Optional[int]=2							,	__lowerCAmelCase   :    Tuple="absolute"							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Any=None							,	**__lowerCAmelCase   :    str							,	)	->	Optional[Any]:







					"""simple docstring"""






					super().__init__(pad_token_id=__lowerCAmelCase							,	bos_token_id=__lowerCAmelCase							,	eos_token_id=__lowerCAmelCase							,	**__lowerCAmelCase   )

					A__        =   vocab_size
					A__        =   hidden_size
					A__        =   num_hidden_layers
					A__        =   num_attention_heads
					A__        =   hidden_act
					A__        =   intermediate_size
					A__        =   hidden_dropout_prob
					A__        =   attention_probs_dropout_prob
					A__        =   max_position_embeddings
					A__        =   type_vocab_size
					A__        =   initializer_range
					A__        =   layer_norm_eps
					A__        =   position_embedding_type
					A__        =   use_cache
					A__        =   classifier_dropout


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




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







					"""simple docstring"""






					if self.task == "multiple-choice":
							A__        =   {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							A__        =   {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ]   )



