
import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, 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.p3.16xlarge',
            'results': {'train_runtime': 650, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
        },
        {
            'framework': 'pytorch',
            'script': 'run_ddp.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 600, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf_dist.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 600, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ]							)
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (       self  :			Optional[int]       )       ->     Optional[int]:
					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 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			List[str]       )       ->     Optional[int]:
					lowercase_       =					f'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
					# distributed data settings
					lowercase_       =					{'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None

					# 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=SCREAMING_SNAKE_CASE_       ,							instance_count=SCREAMING_SNAKE_CASE_       ,							instance_type=self.instance_type       ,							debugger_hook_config=SCREAMING_SNAKE_CASE_       ,							hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}       ,							metric_definitions=self.env.metric_definitions       ,							distribution=SCREAMING_SNAKE_CASE_       ,							py_version='''py36'''       ,							)
	def 						_lowercase  (       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			str       )       ->     List[Any]:
					TrainingJobAnalytics(SCREAMING_SNAKE_CASE_       ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''       )




	@parameterized.expand([(2,)]       )
	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Optional[int]:
					# create estimator
					lowercase_       =					self.create_estimator(SCREAMING_SNAKE_CASE_       )

					# run training
					estimator.fit()

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

					# extract kpis
					lowercase_       =					list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']       )
					lowercase_       =					list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']       )
					# get train time from SageMaker job, this includes starting, preprocessing, stopping
					lowercase_       =					(
					    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}       ,							SCREAMING_SNAKE_CASE_       )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__a  			=					{
    'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
    'processing_trocr': ['TrOCRProcessor'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TrOCRForCausalLM',
						    'TrOCRPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

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




