
'''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_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 16_00, "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": 16_00, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]				)
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							def 	lowercase							(self  )					->       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=UpperCAmelCase  ,  )
														assert hasattr(self  ,  """env"""  )

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       str:
														# configuration for running training on smdistributed Model Parallel
														_snake_case       =  {
														    """enabled""": True,
														    """processes_per_host""": 8,
														}
														_snake_case       =  {
														    """enabled""": True,
														    """parameters""": {
														        """microbatches""": 4,
														        """placement_strategy""": """spread""",
														        """pipeline""": """interleaved""",
														        """optimize""": """speed""",
														        """partitions""": 4,
														        """ddp""": True,
														    },
														}

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

														_snake_case       =  """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=UpperCAmelCase  ,  instance_type=self.instance_type  ,  debugger_hook_config=UpperCAmelCase  ,  hyperparameters={
														        **self.env.hyperparameters,
														        """model_name_or_path""": self.model_name_or_path,
														        """max_steps""": 500,
														    }  ,  metric_definitions=self.env.metric_definitions  ,  distribution=UpperCAmelCase  ,  py_version="""py36"""  ,  )

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       Tuple:
														TrainingJobAnalytics(UpperCAmelCase  ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""  )





							@parameterized.expand([(1,)]  )
							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														# create estimator
														_snake_case       =  self.create_estimator(UpperCAmelCase  )

														# run training
														estimator.fit()

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

														# extract kpis
														_snake_case       =  list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]  )
														_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
														_snake_case       =  (
														    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}  ,  UpperCAmelCase  )

'''simple docstring'''
import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
				import jax
				import jax.numpy as jnp
				from flax.jax_utils import replicate
				from flax.training.common_utils import shard


@slow
@require_flax
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							def 	lowercase							(self  )					->       Optional[int]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()

							def 	lowercase							(self  )					->       Dict:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-canny"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

														_snake_case       =  """bird"""
														_snake_case       =  jax.device_count()
														_snake_case       =  pipe.prepare_text_inputs([prompts] * num_samples  )

														_snake_case       =  load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""  )
														_snake_case       =  pipe.prepare_image_inputs([canny_image] * num_samples  )

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

														_snake_case       =  pipe(
														    prompt_ids=UpperCAmelCase  ,  image=UpperCAmelCase  ,  params=UpperCAmelCase  ,  prng_seed=UpperCAmelCase  ,  num_inference_steps=50  ,  jit=UpperCAmelCase  ,  ).images
														assert images.shape == (jax.device_count(), 1, 768, 512, 3)

														_snake_case       =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
														_snake_case       =  images[0, 253:256, 253:256, -1]

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  jnp.array(
														    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]  )
														print(f"""output_slice: {output_slice}"""  )
														assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2





							def 	lowercase							(self  )					->       Optional[int]:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-openpose"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

														_snake_case       =  """Chef in the kitchen"""
														_snake_case       =  jax.device_count()
														_snake_case       =  pipe.prepare_text_inputs([prompts] * num_samples  )

														_snake_case       =  load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"""  )
														_snake_case       =  pipe.prepare_image_inputs([pose_image] * num_samples  )

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

														_snake_case       =  pipe(
														    prompt_ids=UpperCAmelCase  ,  image=UpperCAmelCase  ,  params=UpperCAmelCase  ,  prng_seed=UpperCAmelCase  ,  num_inference_steps=50  ,  jit=UpperCAmelCase  ,  ).images
														assert images.shape == (jax.device_count(), 1, 768, 512, 3)

														_snake_case       =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
														_snake_case       =  images[0, 253:256, 253:256, -1]

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  jnp.array(
														    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]  )
														print(f"""output_slice: {output_slice}"""  )
														assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2