
"""simple docstring"""





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 		SCREAMING_SNAKE_CASE_					(		unittest.TestCase					):




		"""simple docstring"""





		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__):
				__SCREAMING_SNAKE_CASE				=					f"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
				# distributed data settings
				__SCREAMING_SNAKE_CASE				=					{"""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       snake_case_	(	self				,    lowerCAmelCase__):
				TrainingJobAnalytics(SCREAMING_SNAKE_CASE__).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv")




		@parameterized.expand([(2,)])
		def       snake_case_	(	self				,    lowerCAmelCase__):
				# create estimator
				__SCREAMING_SNAKE_CASE				=					self.create_estimator(SCREAMING_SNAKE_CASE__)

				# run training
				estimator.fit()

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

				# extract kpis
				__SCREAMING_SNAKE_CASE				=					list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
				__SCREAMING_SNAKE_CASE				=					list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""])
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				__SCREAMING_SNAKE_CASE				=					(
				    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__)









"""simple docstring"""







from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_pt_objects import *  # noqa F403
else:
			from .scheduling_consistency_models import CMStochasticIterativeScheduler
			from .scheduling_ddim import DDIMScheduler
			from .scheduling_ddim_inverse import DDIMInverseScheduler
			from .scheduling_ddim_parallel import DDIMParallelScheduler
			from .scheduling_ddpm import DDPMScheduler
			from .scheduling_ddpm_parallel import DDPMParallelScheduler
			from .scheduling_deis_multistep import DEISMultistepScheduler
			from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
			from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
			from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
			from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
			from .scheduling_euler_discrete import EulerDiscreteScheduler
			from .scheduling_heun_discrete import HeunDiscreteScheduler
			from .scheduling_ipndm import IPNDMScheduler
			from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
			from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
			from .scheduling_karras_ve import KarrasVeScheduler
			from .scheduling_pndm import PNDMScheduler
			from .scheduling_repaint import RePaintScheduler
			from .scheduling_sde_ve import ScoreSdeVeScheduler
			from .scheduling_sde_vp import ScoreSdeVpScheduler
			from .scheduling_unclip import UnCLIPScheduler
			from .scheduling_unipc_multistep import UniPCMultistepScheduler
			from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
			from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_flax_objects import *  # noqa F403
else:
			from .scheduling_ddim_flax import FlaxDDIMScheduler
			from .scheduling_ddpm_flax import FlaxDDPMScheduler
			from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
			from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
			from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
			from .scheduling_pndm_flax import FlaxPNDMScheduler
			from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
			from .scheduling_utils_flax import (
			    FlaxKarrasDiffusionSchedulers,
			    FlaxSchedulerMixin,
			    FlaxSchedulerOutput,
			    broadcast_to_shape_from_left,
			)


try:
			if not (is_torch_available() and is_scipy_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
			from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
			if not (is_torch_available() and is_torchsde_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
			from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler




