


'''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''': 650, '''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''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]		)
class 						_snake_case   (       unittest.TestCase		):




					def 			snake_case__  ( self):
								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=_SCREAMING_SNAKE_CASE							,							)
								assert hasattr(self							,							"""env""")




					def 			snake_case__  ( self							,							_lowerCamelCase=1):
								# creates estimator
								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=_SCREAMING_SNAKE_CASE							,							instance_type=self.instance_type							,							debugger_hook_config=_SCREAMING_SNAKE_CASE							,							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							,							_lowerCamelCase):
								TrainingJobAnalytics(_SCREAMING_SNAKE_CASE).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv''')




					def 			snake_case__  ( self):
								# create estimator
								UpperCAmelCase__					:   Tuple       				=       self.create_estimator()

								# run training
								estimator.fit()

								# result dataframe
								UpperCAmelCase__					:   str       				=       TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

								# extract kpis
								UpperCAmelCase__					:   List[Any]       				=       list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
								UpperCAmelCase__					:   str       				=       list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""])
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								UpperCAmelCase__					:   Dict       				=       (
								    Session().describe_training_job(estimator.latest_training_job.name).get("""TrainingTimeInSeconds"""							,							99_9999)
								)

								# 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}							,							_SCREAMING_SNAKE_CASE)



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A		={
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'M2M100ForConditionalGeneration',
		    'M2M100Model',
		    'M2M100PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
		from .tokenization_mam_aaa import MaMaaaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mam_aaa import (
				    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaMaaaForConditionalGeneration,
				    MaMaaaModel,
				    MaMaaaPreTrainedModel,
				)


else:
		import sys

		__A		=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)