
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

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class       _lowerCAmelCase   (   __snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						DiTPipeline
							lowerCAmelCase_ 						=						CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
							lowerCAmelCase_ 						=						PipelineTesterMixin.required_optional_params - {
							    "latents",
							    "num_images_per_prompt",
							    "callback",
							    "callback_steps",
							}
							lowerCAmelCase_ 						=						CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
							lowerCAmelCase_ 						=						False

							def 	lowercase							(self  )					->       Union[str, Any]:
														torch.manual_seed(0  )
														_snake_case       =  TransformeraDModel(
														    sample_size=16  ,  num_layers=2  ,  patch_size=4  ,  attention_head_dim=8  ,  num_attention_heads=2  ,  in_channels=4  ,  out_channels=8  ,  attention_bias=UpperCAmelCase  ,  activation_fn="""gelu-approximate"""  ,  num_embeds_ada_norm=1000  ,  norm_type="""ada_norm_zero"""  ,  norm_elementwise_affine=UpperCAmelCase  ,  )
														_snake_case       =  AutoencoderKL()
														_snake_case       =  DDIMScheduler()
														_snake_case       =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
														return components

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase=0  )					->       List[str]:
														if str(UpperCAmelCase  ).startswith("""mps"""  ):
																					_snake_case       =  torch.manual_seed(UpperCAmelCase  )
														else:
																					_snake_case       =  torch.Generator(device=UpperCAmelCase  ).manual_seed(UpperCAmelCase  )
														_snake_case       =  {
														    """class_labels""": [1],
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """output_type""": """numpy""",
														}
														return inputs

							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  """cpu"""

														_snake_case       =  self.get_dummy_components()
														_snake_case       =  self.pipeline_class(**UpperCAmelCase  )
														pipe.to(UpperCAmelCase  )
														pipe.set_progress_bar_config(disable=UpperCAmelCase  )

														_snake_case       =  self.get_dummy_inputs(UpperCAmelCase  )
														_snake_case       =  pipe(**UpperCAmelCase  ).images
														_snake_case       =  image[0, -3:, -3:, -1]

														self.assertEqual(image.shape  ,  (1, 16, 16, 3)  )
														_snake_case       =  np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]  )
														_snake_case       =  np.abs(image_slice.flatten() - expected_slice  ).max()
														self.assertLessEqual(UpperCAmelCase  ,  1e-3  )

							def 	lowercase							(self  )					->       List[str]:
														self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase  ,  expected_max_diff=1e-3  )





							@unittest.skipIf(
							    torch_device != """cuda""" or not is_xformers_available()  ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""  ,  )
							def 	lowercase							(self  )					->       Union[str, Any]:
														self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )


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







							'''simple docstring'''

							def 	lowercase							(self  )					->       Tuple:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  torch.manual_seed(0  )

														_snake_case       =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""  )
														pipe.to("""cuda"""  )

														_snake_case       =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
														_snake_case       =  pipe.get_label_ids(UpperCAmelCase  )

														_snake_case       =  pipe(UpperCAmelCase  ,  generator=UpperCAmelCase  ,  num_inference_steps=40  ,  output_type="""np"""  ).images

														for word, image in zip(UpperCAmelCase  ,  UpperCAmelCase  ):
																					_snake_case       =  load_numpy(
																					    f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""  )
																					assert np.abs((expected_image - image).max()  ) < 1e-2





							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""  )
														_snake_case       =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
														pipe.to("""cuda"""  )

														_snake_case       =  ["""vase""", """umbrella"""]
														_snake_case       =  pipe.get_label_ids(UpperCAmelCase  )

														_snake_case       =  torch.manual_seed(0  )
														_snake_case       =  pipe(UpperCAmelCase  ,  generator=UpperCAmelCase  ,  num_inference_steps=25  ,  output_type="""np"""  ).images

														for word, image in zip(UpperCAmelCase  ,  UpperCAmelCase  ):
																					_snake_case       =  load_numpy(
																					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
																					    f"""/dit/{word}_512.npy"""  )

																					assert np.abs((expected_image - image).max()  ) < 1e-1