



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      _a				(  self       :							Any       ):






								"""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=_snake_case					,       )
								assert hasattr(self					,       'env'       )



					def      _a				(  self       :							Optional[int]					,       _snake_case       :							int       ):






								"""simple docstring"""


								A__	  =     {
								    'enabled': True,
								    'processes_per_host': 8,
								}
								A__	  =     {
								    'enabled': True,
								    'parameters': {
								        'microbatches': 4,
								        'placement_strategy': 'spread',
								        'pipeline': 'interleaved',
								        'optimize': 'speed',
								        'partitions': 4,
								        'ddp': True,
								    },
								}

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

								A__	  =     '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': 5_00,
								    }					,       metric_definitions=self.env.metric_definitions					,       distribution=_snake_case					,       py_version='py36'					,       )



					def      _a				(  self       :							List[str]					,       _snake_case       :							Optional[Any]       ):






								"""simple docstring"""


								TrainingJobAnalytics(_snake_case       ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''       )



					@parameterized.expand([(1,)]       )
					def      _a				(  self       :							Dict					,       _snake_case       :							Optional[int]       ):






								"""simple docstring"""


								A__	  =     self.create_estimator(_snake_case       )

								# run training
								estimator.fit()

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

								# extract kpis
								A__	  =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']       )
								A__	  =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']       )
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								A__	  =     (
								    Session().describe_training_job(estimator.latest_training_job.name       ).get('TrainingTimeInSeconds'					,       99_99_99       )
								)

								# 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       )









import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			A__	  =     OmegaConf.load(__UpperCamelCase       )
			A__	  =     torch.load(__UpperCamelCase			,						map_location='cpu'       )['model']
			A__	  =     list(state_dict.keys()       )

			# extract state_dict for VQVAE
			A__	  =     {}
			A__	  =     'first_stage_model.'
			for key in keys:
						if key.startswith(__UpperCamelCase       ):
									A__	  =     state_dict[key]

    # extract state_dict for UNetLDM
			A__	  =     {}
			A__	  =     'model.diffusion_model.'
			for key in keys:
						if key.startswith(__UpperCamelCase       ):
									A__	  =     state_dict[key]

			A__	  =     config.model.params.first_stage_config.params
			A__	  =     config.model.params.unet_config.params

			A__	  =     VQModel(**__UpperCamelCase       ).eval()
			vqvae.load_state_dict(__UpperCamelCase       )

			A__	  =     UNetLDMModel(**__UpperCamelCase       ).eval()
			unet.load_state_dict(__UpperCamelCase       )

			A__	  =     DDIMScheduler(
			    timesteps=config.model.params.timesteps			,						beta_schedule='scaled_linear'			,						beta_start=config.model.params.linear_start			,						beta_end=config.model.params.linear_end			,						clip_sample=__UpperCamelCase			,						)

			A__	  =     LDMPipeline(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			pipeline.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()
							parser.add_argument('''--checkpoint_path''', type=str, required=True)
							parser.add_argument('''--config_path''', type=str, required=True)
							parser.add_argument('''--output_path''', type=str, required=True)
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()

							convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)




