



'''simple docstring'''





from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_lowerCAmelCase      						=	transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    if isinstance(UpperCamelCase							,    torch.Tensor			):
        return image
    elif isinstance(UpperCamelCase							,    PIL.Image.Image			):
        lowerCAmelCase__ :  Union[str, Any]							      =					[image]

    lowerCAmelCase__ :  Any							      =					[trans(img.convert("""RGB"""			)			) for img in image]
    lowerCAmelCase__ :  str							      =					torch.stack(UpperCamelCase			)
    return image






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		List[Any]:
      super().__init__()

      # make sure scheduler can always be converted to DDIM
      lowerCAmelCase__ :  Dict							      =					DDIMScheduler.from_config(scheduler.config      )

      self.register_modules(unet=__UpperCAmelCase  ,scheduler=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Any:
      if strength < 0 or strength > 1:
          raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Tuple:
      # get the original timestep using init_timestep
      lowerCAmelCase__ :  str							      =					min(int(num_inference_steps * strength      )  ,__UpperCAmelCase      )

      lowerCAmelCase__ :  Optional[Any]							      =					max(num_inference_steps - init_timestep  ,0      )
      lowerCAmelCase__ :  Optional[Any]							      =					self.scheduler.timesteps[t_start:]

      return timesteps, num_inference_steps - t_start




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None      )    ->		List[str]:
      if not isinstance(__UpperCAmelCase  ,(torch.Tensor, PIL.Image.Image, list)      ):
          raise ValueError(
              F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__UpperCAmelCase      )}"""      )

      lowerCAmelCase__ :  Tuple							      =					image.to(device=__UpperCAmelCase  ,dtype=__UpperCAmelCase      )

      if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ) and len(__UpperCAmelCase      ) != batch_size:
          raise ValueError(
              F"""You have passed a list of generators of length {len(__UpperCAmelCase      )}, but requested an effective batch"""
              F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""      )

      lowerCAmelCase__ :  Union[str, Any]							      =					init_latents.shape
      lowerCAmelCase__ :  List[str]							      =					randn_tensor(__UpperCAmelCase  ,generator=__UpperCAmelCase  ,device=__UpperCAmelCase  ,dtype=__UpperCAmelCase      )

      # get latents
      print("""add noise to latents at timestep"""  ,__UpperCAmelCase      )
      lowerCAmelCase__ :  Dict							      =					self.scheduler.add_noise(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )
      lowerCAmelCase__ :  Dict							      =					init_latents

      return latents






  @torch.no_grad()
  def __call__(    self  ,__UpperCAmelCase = None  ,__UpperCAmelCase = 0.8  ,__UpperCAmelCase = 1  ,__UpperCAmelCase = None  ,__UpperCAmelCase = 0.0  ,__UpperCAmelCase = 50  ,__UpperCAmelCase = None  ,__UpperCAmelCase = "pil"  ,__UpperCAmelCase = True  ,)    ->		Union[ImagePipelineOutput, Tuple]:
      self.check_inputs(__UpperCAmelCase      )

      # 2. Preprocess image
      lowerCAmelCase__ :  List[str]							      =					preprocess(__UpperCAmelCase      )

      # 3. set timesteps
      self.scheduler.set_timesteps(__UpperCAmelCase  ,device=self.device      )
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[Any]							      =					self.get_timesteps(__UpperCAmelCase  ,__UpperCAmelCase  ,self.device      )
      lowerCAmelCase__ :  Optional[int]							      =					timesteps[:1].repeat(__UpperCAmelCase      )

      # 4. Prepare latent variables
      lowerCAmelCase__ :  Tuple							      =					self.prepare_latents(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,self.unet.dtype  ,self.device  ,__UpperCAmelCase      )
      lowerCAmelCase__ :  List[Any]							      =					latents

      # 5. Denoising loop
      for t in self.progress_bar(__UpperCAmelCase      ):
          # 1. predict noise model_output
          lowerCAmelCase__ :  str							      =					self.unet(__UpperCAmelCase  ,__UpperCAmelCase      ).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
          lowerCAmelCase__ :  str							      =					self.scheduler.step(
              __UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,eta=__UpperCAmelCase  ,use_clipped_model_output=__UpperCAmelCase  ,generator=__UpperCAmelCase  ,).prev_sample

      lowerCAmelCase__ :  Optional[Any]							      =					(image / 2 + 0.5).clamp(0  ,1      )
      lowerCAmelCase__ :  Optional[int]							      =					image.cpu().permute(0  ,2  ,3  ,1      ).numpy()
      if output_type == "pil":
          lowerCAmelCase__ :  List[str]							      =					self.numpy_to_pil(__UpperCAmelCase      )

      if not return_dict:
          return (image, latent_timestep.item())

      return ImagePipelineOutput(images=__UpperCAmelCase      )







'''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.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_ddp.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf_dist.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]     )
class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      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 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Optional[int]							      =					F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
      # distributed data settings
      lowerCAmelCase__ :  Any							      =					{"""smdistributed""": {"""dataparallel""": {"""enabled""": True}}} if self.script != """run_ddp.py""" else None

      # 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=__UpperCAmelCase  ,instance_count=__UpperCAmelCase  ,instance_type=self.instance_type  ,debugger_hook_config=__UpperCAmelCase  ,hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}  ,metric_definitions=self.env.metric_definitions  ,distribution=__UpperCAmelCase  ,py_version="""py36"""  ,)




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      TrainingJobAnalytics(__UpperCAmelCase      ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""      )






  @parameterized.expand([(2,)]      )
  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Any:
      # create estimator
      lowerCAmelCase__ :  List[Any]							      =					self.create_estimator(__UpperCAmelCase      )

      # run training
      estimator.fit()

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

      # extract kpis
      lowerCAmelCase__ :  int							      =					list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]      )
      lowerCAmelCase__ :  List[Any]							      =					list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]      )
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      lowerCAmelCase__ :  List[str]							      =					(
          Session().describe_training_job(estimator.latest_training_job.name      ).get("""TrainingTimeInSeconds"""  ,99_9999      )
      )

      # 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      )


