





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''': 1600, '''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''': 1600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]   )
class   A      (unittest.TestCase   ):
			'''simple docstring'''




			def        a_	(      self       :   List[Any]       )     ->     Tuple:







					"""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=__lowerCAmelCase							,     )
					assert hasattr(self							,     """env"""       )






			def        a_	(      self       :   List[Any]							,     __lowerCAmelCase       :   Tuple       )     ->     Optional[int]:







					"""simple docstring"""






					A__  				=       {
					    """enabled""": True,
					    """processes_per_host""": 8,
					}
					A__  				=       {
					    """enabled""": True,
					    """parameters""": {
					        """microbatches""": 4,
					        """placement_strategy""": """spread""",
					        """pipeline""": """interleaved""",
					        """optimize""": """speed""",
					        """partitions""": 4,
					        """ddp""": True,
					    },
					}

					A__  				=       {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

					A__  				=       """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       )     ->     Tuple:







					"""simple docstring"""






					TrainingJobAnalytics(__lowerCAmelCase       ).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'       )







			@parameterized.expand([(1,)]       )
			def        a_	(      self       :   Dict							,     __lowerCAmelCase       :   Dict       )     ->     List[Any]:







					"""simple docstring"""






					A__  				=       self.create_estimator(__lowerCAmelCase       )

					# 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"""							,     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       )










from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .notes_encoder import SpectrogramNotesEncoder
					from .continous_encoder import SpectrogramContEncoder
					from .pipeline_spectrogram_diffusion import (
					    SpectrogramContEncoder,
					    SpectrogramDiffusionPipeline,
					    TaFilmDecoder,
					)

try:
					if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
					from .midi_utils import MidiProcessor



