


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  snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						def   lowercase_       ( self							)    ->			Any:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()




						def   lowercase_       ( self							)    ->			Any:
										lowerCAmelCase_       :					List[str]			 = FlaxControlNetModel.from_pretrained(
										    '''lllyasviel/sd-controlnet-canny'''      ,  from_pt=__lowercase      ,  dtype=jnp.bfloataa							)
										lowerCAmelCase_       :					Union[str, Any]			 = FlaxStableDiffusionControlNetPipeline.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5'''      ,  controlnet=__lowercase      ,  from_pt=__lowercase      ,  dtype=jnp.bfloataa							)
										lowerCAmelCase_       :					Union[str, Any]			 = controlnet_params

										lowerCAmelCase_       :					Union[str, Any]			 = """bird"""
										lowerCAmelCase_       :					List[Any]			 = jax.device_count()
										lowerCAmelCase_       :					List[Any]			 = pipe.prepare_text_inputs([prompts] * num_samples							)

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

										lowerCAmelCase_       :					Any			 = jax.random.PRNGKey(0							)
										lowerCAmelCase_       :					List[str]			 = jax.random.split(__lowercase      ,  jax.device_count()							)

										lowerCAmelCase_       :					List[Any]			 = replicate(__lowercase							)
										lowerCAmelCase_       :					List[str]			 = shard(__lowercase							)
										lowerCAmelCase_       :					str			 = shard(__lowercase							)

										lowerCAmelCase_       :					Dict			 = pipe(
										    prompt_ids=__lowercase      ,  image=__lowercase      ,  params=__lowercase      ,  prng_seed=__lowercase      ,  num_inference_steps=5_0      ,  jit=__lowercase      ,  ).images
										assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

										lowerCAmelCase_       :					str			 = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										lowerCAmelCase_       :					Union[str, Any]			 = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										lowerCAmelCase_       :					str			 = jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										lowerCAmelCase_       :					Dict			 = jnp.array(
										    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]							)
										print(f"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2




						def   lowercase_       ( self							)    ->			Dict:
										lowerCAmelCase_       :					List[Any]			 = FlaxControlNetModel.from_pretrained(
										    '''lllyasviel/sd-controlnet-openpose'''      ,  from_pt=__lowercase      ,  dtype=jnp.bfloataa							)
										lowerCAmelCase_       :					int			 = FlaxStableDiffusionControlNetPipeline.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5'''      ,  controlnet=__lowercase      ,  from_pt=__lowercase      ,  dtype=jnp.bfloataa							)
										lowerCAmelCase_       :					str			 = controlnet_params

										lowerCAmelCase_       :					Optional[int]			 = """Chef in the kitchen"""
										lowerCAmelCase_       :					Union[str, Any]			 = jax.device_count()
										lowerCAmelCase_       :					Any			 = pipe.prepare_text_inputs([prompts] * num_samples							)

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

										lowerCAmelCase_       :					str			 = jax.random.PRNGKey(0							)
										lowerCAmelCase_       :					str			 = jax.random.split(__lowercase      ,  jax.device_count()							)

										lowerCAmelCase_       :					Tuple			 = replicate(__lowercase							)
										lowerCAmelCase_       :					str			 = shard(__lowercase							)
										lowerCAmelCase_       :					int			 = shard(__lowercase							)

										lowerCAmelCase_       :					List[str]			 = pipe(
										    prompt_ids=__lowercase      ,  image=__lowercase      ,  params=__lowercase      ,  prng_seed=__lowercase      ,  num_inference_steps=5_0      ,  jit=__lowercase      ,  ).images
										assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

										lowerCAmelCase_       :					str			 = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										lowerCAmelCase_       :					int			 = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										lowerCAmelCase_       :					Dict			 = jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										lowerCAmelCase_       :					Optional[int]			 = jnp.array(
										    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]							)
										print(f"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2




"""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""": 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 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Any						)   ->	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=snake_case						,     )
											assert hasattr(self						,     """env"""						)






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							Dict						)   ->	List[Any]:
											# configuration for running training on smdistributed Model Parallel
											snake_case_ :Tuple						=			{
											    """enabled""": True,
											    """processes_per_host""": 8,
											}
											snake_case_ :List[Any]						=			{
											    """enabled""": True,
											    """parameters""": {
											        """microbatches""": 4,
											        """placement_strategy""": """spread""",
											        """pipeline""": """interleaved""",
											        """optimize""": """speed""",
											        """partitions""": 4,
											        """ddp""": True,
											    },
											}

											snake_case_ :Tuple						=			{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

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






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							Tuple						)   ->	List[str]:
											TrainingJobAnalytics(snake_case						).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""						)






					@parameterized.expand([(1,)]						)
					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							Dict						)   ->	List[Any]:
											# create estimator
											snake_case_ :List[Any]						=			self.create_estimator(snake_case						)

											# run training
											estimator.fit()

											# result dataframe
											snake_case_ :Any						=			TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

											# extract kpis
											snake_case_ :Tuple						=			list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]						)
											snake_case_ :Dict						=			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_ :int						=			(
											    Session().describe_training_job(estimator.latest_training_job.name						).get("""TrainingTimeInSeconds"""						,     999_999						)
											)

											# 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}						,     snake_case						)
