





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_model_parallelism.py',
            'model_name_or_path': 'roberta-large',
            'instance_type': 'ml.p3dn.24xlarge',
            'results': {'train_runtime': 1_6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
        {
            'framework': 'pytorch',
            'script': 'run_glue.py',
            'model_name_or_path': 'roberta-large',
            'instance_type': 'ml.p3dn.24xlarge',
            'results': {'train_runtime': 1_6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
    ]							)
class 						UpperCamelCase_  (  unittest.TestCase							):



					def   __a     (		self     :     str						):
							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=lowerCamelCase       ,      )
							assert hasattr(self       ,      'env'						)



					def   __a     (		self     :     List[Any]       ,      lowerCamelCase     :     List[str]						):
							# configuration for running training on smdistributed Model Parallel
							lowerCamelCase_	:     Any 						=      {
							    'enabled': True,
							    'processes_per_host': 8,
							}
							lowerCamelCase_	:     Union[str, Any] 						=      {
							    'enabled': True,
							    'parameters': {
							        'microbatches': 4,
							        'placement_strategy': 'spread',
							        'pipeline': 'interleaved',
							        'optimize': 'speed',
							        'partitions': 4,
							        'ddp': True,
							    },
							}

							lowerCamelCase_	:     Union[str, Any] 						=      {'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

							lowerCamelCase_	:     str 						=      'trainer' if self.script == 'run_glue.py' else 'smtrainer'
							# 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}-{instance_count}-smp-{name_extension}"       ,      instance_count=lowerCamelCase       ,      instance_type=self.instance_type       ,      debugger_hook_config=lowerCamelCase       ,      hyperparameters={
							        **self.env.hyperparameters,
							        'model_name_or_path': self.model_name_or_path,
							        'max_steps': 5_00,
							    }       ,      metric_definitions=self.env.metric_definitions       ,      distribution=lowerCamelCase       ,      py_version='py36'       ,      )



					def   __a     (		self     :     List[str]       ,      lowerCamelCase     :     Tuple						):
							TrainingJobAnalytics(lowerCamelCase						).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv"						)





					@parameterized.expand([(1,)]						)
					def   __a     (		self     :     int       ,      lowerCamelCase     :     List[Any]						):
							# create estimator
							lowerCamelCase_	:     Union[str, Any] 						=      self.create_estimator(lowerCamelCase						)

							# run training
							estimator.fit()

							# result dataframe
							lowerCamelCase_	:     List[Any] 						=      TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

							# extract kpis
							lowerCamelCase_	:     Optional[Any] 						=      list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']						)
							lowerCamelCase_	:     List[Any] 						=      list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']						)
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							lowerCamelCase_	:     List[str] 						=      (
							    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}       ,      lowerCamelCase						)







import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowercase		:  List[str]			       =5_0000
_lowercase		:  str			       =5000

_lowercase       , _lowercase		:  List[str]			       =os.path.split(__file__)
_lowercase		:  Union[str, Any]			       =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(0							,len(lowerCAmelCase__						)							,lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i : i + batch_size]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(lowerCAmelCase__						):
						lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(0							,lowerCAmelCase__							,lowerCAmelCase__						):
						lowerCamelCase_	:     Optional[int] 						=      dataset[i : i + batch_size]


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Any 						=      {'num examples': SPEED_TEST_N_EXAMPLES}
		lowerCamelCase_	:     Optional[Any] 						=      [
		    (read, {'length': SMALL_TEST}),
		    (read, {'length': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
		]

		lowerCamelCase_	:     Optional[Any] 						=      [
		    (read, {'length': SMALL_TEST}),
		    (read, {'length': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
		]
		with tempfile.TemporaryDirectory() as tmp_dir:
				print('generating dataset'						)
				lowerCamelCase_	:     List[Any] 						=      datasets.Features(
				    {'list': datasets.Sequence(datasets.Value('float32'						)						), 'numbers': datasets.Value('float32'						)}						)
				lowerCamelCase_	:     Union[str, Any] 						=      generate_example_dataset(
				    os.path.join(lowerCAmelCase__							,'dataset.arrow'						)							,lowerCAmelCase__							,num_examples=lowerCAmelCase__							,seq_shapes={'list': (1_00,)}							,)
				print('first set of iterations'						)
				for func, kwargs in functions:
						print(func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[int] 						=      func(lowerCAmelCase__							,**lowerCAmelCase__						)

				print('shuffling dataset'						)
				lowerCamelCase_	:     int 						=      dataset.shuffle()
				print('Second set of iterations (after shuffling'						)
				for func, kwargs in functions_shuffled:
						print('shuffled '							,func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[Any] 						=      func(
						    lowerCAmelCase__							,**lowerCAmelCase__						)

		with open(lowerCAmelCase__							,'wb'						) as f:
				f.write(json.dumps(lowerCAmelCase__						).encode('utf-8'						)						)


if __name__ == "__main__":  # useful to run the profiler
		benchmark_iterating()
