'''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''': 6_5_0, '''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''': 6_0_0, '''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''': 6_0_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]	)
class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def 							a_       (   self):




					"""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       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  f"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
					# distributed data settings
					lowerCAmelCase   					=  {"""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=__lowerCAmelCase       ,    instance_count=__lowerCAmelCase       ,    instance_type=self.instance_type       ,    debugger_hook_config=__lowerCAmelCase       ,    hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}       ,    metric_definitions=self.env.metric_definitions       ,    distribution=__lowerCAmelCase       ,    py_version="""py36"""       ,    )



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


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







			@parameterized.expand([(2,)])
			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  self.create_estimator(__lowerCAmelCase)

					# run training
					estimator.fit()

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

					# extract kpis
					lowerCAmelCase   					=  list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
					lowerCAmelCase   					=  list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""])
					# get train time from SageMaker job, this includes starting, preprocessing, stopping
					lowerCAmelCase   					=  (
					    Session().describe_training_job(estimator.latest_training_job.name).get("""TrainingTimeInSeconds"""       ,    999999)
					)

					# 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)





'''simple docstring'''




import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			@property
			def 							a_       (   self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase   					=  UNetaDModel(
					    block_out_channels=(32, 64)       ,    layers_per_block=2       ,    sample_size=32       ,    in_channels=3       ,    out_channels=3       ,    down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")       ,    up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")       ,    )
					return model







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.dummy_uncond_unet
					lowerCAmelCase   					=  PNDMScheduler()

					lowerCAmelCase   					=  PNDMPipeline(unet=__lowerCAmelCase       ,    scheduler=__lowerCAmelCase)
					pndm.to(__lowerCAmelCase)
					pndm.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    num_inference_steps=20       ,    output_type="""numpy""").images

					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    num_inference_steps=20       ,    output_type="""numpy"""       ,    return_dict=__lowerCAmelCase)[0]

					lowerCAmelCase   					=  image[0, -3:, -3:, -1]
					lowerCAmelCase   					=  image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2







@slow
@require_torch
class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  """google/ddpm-cifar10-32"""

					lowerCAmelCase   					=  UNetaDModel.from_pretrained(__lowerCAmelCase)
					lowerCAmelCase   					=  PNDMScheduler()

					lowerCAmelCase   					=  PNDMPipeline(unet=__lowerCAmelCase       ,    scheduler=__lowerCAmelCase)
					pndm.to(__lowerCAmelCase)
					pndm.set_progress_bar_config(disable=__lowerCAmelCase)
					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    output_type="""numpy""").images

					lowerCAmelCase   					=  image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125])

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2




