






"""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": 1600, "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": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]	)
class        a     (   unittest.TestCase	):







    def 			UpperCamelCase_      (			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=_lowerCamelCase							,		)
      assert hasattr(self							,		'env'       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      # configuration for running training on smdistributed Model Parallel
      lowercase							    =       {
          'enabled': True,
          'processes_per_host': 8,
      }
      lowercase							    =       {
          'enabled': True,
          'parameters': {
              'microbatches': 4,
              'placement_strategy': 'spread',
              'pipeline': 'interleaved',
              'optimize': 'speed',
              'partitions': 4,
              'ddp': True,
          },
      }

      lowercase							    =       {'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

      lowercase							    =       '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=_lowerCamelCase							,		instance_type=self.instance_type							,		debugger_hook_config=_lowerCamelCase							,		hyperparameters={
              **self.env.hyperparameters,
              'model_name_or_path': self.model_name_or_path,
              'max_steps': 5_0_0,
          }							,		metric_definitions=self.env.metric_definitions							,		distribution=_lowerCamelCase							,		py_version='py36'							,		)







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      TrainingJobAnalytics(_lowerCamelCase       ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'       )







    @parameterized.expand([(1,)]       )
    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      # create estimator
      lowercase							    =       self.create_estimator(_lowerCamelCase       )

      # run training
      estimator.fit()

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

      # extract kpis
      lowercase							    =       list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']       )
      lowercase							    =       list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']       )
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      lowercase							    =       (
          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}							,		_lowerCamelCase       )










"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class        a     (   a_	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      super().__init__()
      self.register_modules(unet=_lowerCamelCase							,		scheduler=_lowerCamelCase       )







    @torch.no_grad()
    def __call__(			self							,		_lowerCamelCase = 1							,		_lowerCamelCase = None							,		_lowerCamelCase = 5_0							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		**_lowerCamelCase							,		):
      lowercase							    =       torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,		generator=_lowerCamelCase							,		)
      lowercase							    =       image.to(self.device       )

      # set step values
      self.scheduler.set_timesteps(_lowerCamelCase       )

      for t in self.progress_bar(self.scheduler.timesteps       ):
        # 1. predict noise model_output
        lowercase							    =       self.unet(_lowerCamelCase							,		_lowerCamelCase       ).sample

        # 2. predict previous mean of image x_t-1 and add variance depending on eta
        # eta corresponds to η in paper and should be between [0, 1]
        # do x_t -> x_t-1
        lowercase							    =       self.scheduler.step(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ).prev_sample

      lowercase							    =       (image / 2 + 0.5).clamp(0							,		1       )
      lowercase							    =       image.cpu().permute(0							,		2							,		3							,		1       ).numpy()
      if output_type == "pil":
        lowercase							    =       self.numpy_to_pil(_lowerCamelCase       )

      if not return_dict:
        return (image,), "This is a local test"

      return ImagePipelineOutput(images=_lowerCamelCase       ), "This is a local test"


