






"""simple docstring"""




import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel






def        lowerCAmelCase (      )       ->							Union[str, Any]:







  '''simple docstring'''







  _a							=	argparse.ArgumentParser()
  parser.add_argument(
      "-m"     , "--pretrained_model_name_or_path"     , type=__UpperCAmelCase     , default=__UpperCAmelCase     , required=__UpperCAmelCase     , help="Path to pretrained model or model identifier from huggingface.co/models."     , )
  parser.add_argument(
      "-c"     , "--caption"     , type=__UpperCAmelCase     , default="robotic cat with wings"     , help="Text used to generate images."     , )
  parser.add_argument(
      "-n"     , "--images_num"     , type=__UpperCAmelCase     , default=4     , help="How much images to generate."     , )
  parser.add_argument(
      "-s"     , "--seed"     , type=__UpperCAmelCase     , default=42     , help="Seed for random process."     , )
  parser.add_argument(
      "-ci"     , "--cuda_id"     , type=__UpperCAmelCase     , default=0     , help="cuda_id."     , )
  _a							=	parser.parse_args()
  return args






def        lowerCAmelCase (      UpperCamelCase_:							List[str]     , UpperCamelCase_:							Optional[Any]     , UpperCamelCase_:							int					)       ->							Optional[Any]:







  '''simple docstring'''







  if not len(__UpperCAmelCase					) == rows * cols:
    raise ValueError("The specified number of rows and columns are not correct."					)

  _a			,	_a							=	imgs[0].size
  _a							=	Image.new("RGB"     , size=(cols * w, rows * h)					)
  _a			,	_a							=	grid.size

  for i, img in enumerate(__UpperCAmelCase					):
    grid.paste(__UpperCAmelCase     , box=(i % cols * w, i // cols * h)					)
  return grid






def        lowerCAmelCase (      UpperCamelCase_:							Dict     , UpperCamelCase_:							Union[str, Any]="robotic cat with wings"     , UpperCamelCase_:							List[Any]=7.5     , UpperCamelCase_:							Tuple=50     , UpperCamelCase_:							str=1     , UpperCamelCase_:							str=42     , )       ->							Optional[Any]:







  '''simple docstring'''







  _a							=	torch.Generator(pipeline.device					).manual_seed(__UpperCAmelCase					)
  _a							=	pipeline(
      __UpperCAmelCase     , guidance_scale=__UpperCAmelCase     , num_inference_steps=__UpperCAmelCase     , generator=__UpperCAmelCase     , num_images_per_prompt=__UpperCAmelCase     , ).images
  _a							=	int(math.sqrt(__UpperCAmelCase					)					)
  _a							=	image_grid(__UpperCAmelCase     , rows=_rows     , cols=num_images_per_prompt // _rows					)
  return grid, images


UpperCamelCase							       =    parse_args()
# Load models and create wrapper for stable diffusion
UpperCamelCase							       =    CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
UpperCamelCase							       =    CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
UpperCamelCase							       =    AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
UpperCamelCase							       =    UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

UpperCamelCase							       =    StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
UpperCamelCase							       =    lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
     UpperCamelCase							       =    load(args.pretrained_model_name_or_path, model=unet)
     unet.eval()
     setattr(pipeline, """unet""", unet)
else:
     UpperCamelCase							       =    unet.to(torch.device("""cuda""", args.cuda_id))
UpperCamelCase							       =    pipeline.to(unet.device)
UpperCamelCase       ,							UpperCamelCase							       =    generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, """{}.png""".format("""_""".join(args.caption.split()))))
UpperCamelCase							       =    os.path.join(args.pretrained_model_name_or_path, """_""".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
     image.save(os.path.join(dirname, """{}.png""".format(idx + 1)))










"""simple docstring"""




import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import 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.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 650, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]     )
class    lowercase_    (unittest.TestCase     ):






      def 			lowerCamelCase__    (    self	)	->Union[str, 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=a_     ,     )
        assert hasattr(self     ,     "env"	)






      def 			lowerCamelCase__    (    self     ,     a_=1	)	->int:







        '''simple docstring'''


        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}-single'''     ,     instance_count=a_     ,     instance_type=self.instance_type     ,     debugger_hook_config=a_     ,     hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}     ,     metric_definitions=self.env.metric_definitions     ,     py_version="py36"     ,     )






      def 			lowerCamelCase__    (    self     ,     a_	)	->str:







        '''simple docstring'''


        TrainingJobAnalytics(a_	).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''	)






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        _a							=	self.create_estimator()

        # 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"     ,     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}     ,     a_	)


