'''simple docstring'''





import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


_lowerCAmelCase  :Optional[int]  				=    """true"""


def     __lowerCAmelCase						(       a_       ,      a_=82       ,      a_=16		)      ->	Union[str, Any]:





     '''simple docstring'''





     set_seed(42		)
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       RegressionModel()
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       deepcopy(a_		)
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       RegressionDataset(length=a_		)
     SCREAMING_SNAKE_CASE					:			Tuple    	=       DataLoader(a_       ,      batch_size=a_		)
     model.to(accelerator.device		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       accelerator.prepare(a_       ,      a_		)
     return model, ddp_model, dataloader


def     __lowerCAmelCase						(       a_       ,      a_=False		)      ->	Dict:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			int    	=       AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'		)
     SCREAMING_SNAKE_CASE					:			Any    	=       load_dataset('glue'       ,      'mrpc'       ,      split='validation'		)

     def tokenize_function(a_		):
          SCREAMING_SNAKE_CASE					:			Dict    	=       tokenizer(examples['sentence1']       ,      examples['sentence2']       ,      truncation=a_       ,      max_length=a_		)
          return outputs

     with accelerator.main_process_first():
          SCREAMING_SNAKE_CASE					:			int    	=       dataset.map(
              a_       ,      batched=a_       ,      remove_columns=['idx', 'sentence1', 'sentence2']       ,      )

     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       tokenized_datasets.rename_column('label'       ,      'labels'		)

     def collate_fn(a_		):
          if use_longest:
               return tokenizer.pad(a_       ,      padding='longest'       ,      return_tensors='pt'		)
          return tokenizer.pad(a_       ,      padding='max_length'       ,      max_length=128       ,      return_tensors='pt'		)

     return DataLoader(a_       ,      shuffle=a_       ,      collate_fn=a_       ,      batch_size=16		)


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       Accelerator(dispatch_batches=a_       ,      split_batches=a_		)
     SCREAMING_SNAKE_CASE					:			Any    	=       get_dataloader(a_       ,      not dispatch_batches		)
     SCREAMING_SNAKE_CASE					:			Any    	=       AutoModelForSequenceClassification.from_pretrained(
         'hf-internal-testing/mrpc-bert-base-cased'       ,      return_dict=a_		)
     SCREAMING_SNAKE_CASE					:			int    	=       accelerator.prepare(a_       ,      a_		)
     return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	int:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			int    	=       []
     for batch in dataloader:
          SCREAMING_SNAKE_CASE					:			str    	=       batch.values()
          with torch.no_grad():
               SCREAMING_SNAKE_CASE					:			int    	=       model(a_		)
               SCREAMING_SNAKE_CASE					:			int    	=       accelerator.gather_for_metrics((logit, target)		)
               logits_and_targets.append((logit, target)		)
     SCREAMING_SNAKE_CASE					:			int    	=       [], []
     for logit, targ in logits_and_targets:
          logits.append(a_		)
          targs.append(a_		)
     SCREAMING_SNAKE_CASE					:			Any    	=       torch.cat(a_		), torch.cat(a_		)
     return logits, targs


def     __lowerCAmelCase						(       a_       ,      a_=82       ,      a_=False       ,      a_=False       ,      a_=16		)      ->	Dict:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       get_basic_setup(a_       ,      a_       ,      a_		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       generate_predictions(a_       ,      a_       ,      a_		)
     assert (
         len(a_		) == num_samples
     ), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(a_		)}"""


def     __lowerCAmelCase						(       a_ = False       ,      a_ = False		)      ->	Any:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       evaluate.load('glue'       ,      'mrpc'		)
     SCREAMING_SNAKE_CASE					:			List[str]    	=       get_mrpc_setup(a_       ,      a_		)
     # First do baseline
     SCREAMING_SNAKE_CASE					:			List[str]    	=       setup['no']
     model.to(a_		)
     model.eval()
     for batch in dataloader:
          batch.to(a_		)
          with torch.inference_mode():
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model(**a_		)
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       outputs.logits.argmax(dim=-1		)
          metric.add_batch(predictions=a_       ,      references=batch['labels']		)
     SCREAMING_SNAKE_CASE					:			str    	=       metric.compute()

     # Then do distributed
     SCREAMING_SNAKE_CASE					:			Dict    	=       setup['ddp']
     model.eval()
     for batch in dataloader:
          with torch.inference_mode():
               SCREAMING_SNAKE_CASE					:			List[str]    	=       model(**a_		)
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       outputs.logits.argmax(dim=-1		)
          SCREAMING_SNAKE_CASE					:			int    	=       batch['labels']
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       accelerator.gather_for_metrics((preds, references)		)
          metric.add_batch(predictions=a_       ,      references=a_		)
     SCREAMING_SNAKE_CASE					:			Any    	=       metric.compute()

     for key in "accuracy f1".split():
          assert math.isclose(
              baseline[key]       ,      distributed[key]		), f"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""


def     __lowerCAmelCase						(       )      ->	Optional[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       Accelerator(split_batches=a_       ,      dispatch_batches=a_		)
     if accelerator.is_local_main_process:
          datasets.utils.logging.set_verbosity_warning()
          transformers.utils.logging.set_verbosity_warning()
     else:
          datasets.utils.logging.set_verbosity_error()
          transformers.utils.logging.set_verbosity_error()
     # These are a bit slower so they should only be ran on the GPU or TPU
     if torch.cuda.is_available() or is_tpu_available():
          if accelerator.is_local_main_process:
               print('**Testing gather_for_metrics**'		)
          for split_batches in [True, False]:
               for dispatch_batches in [True, False]:
                    if accelerator.is_local_main_process:
                         print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"""		)
                    test_mrpc(a_       ,      a_		)
                    accelerator.state._reset_state()
     if accelerator.is_local_main_process:
          print('**Test torch metrics**'		)
     for split_batches in [True, False]:
          for dispatch_batches in [True, False]:
               SCREAMING_SNAKE_CASE					:			Dict    	=       Accelerator(split_batches=a_       ,      dispatch_batches=a_		)
               if accelerator.is_local_main_process:
                    print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""		)
               test_torch_metrics(a_       ,      99		)
               accelerator.state._reset_state()
     if accelerator.is_local_main_process:
          print('**Test last batch is not dropped when perfectly divisible**'		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       Accelerator()
     test_torch_metrics(a_       ,      512		)
     accelerator.state._reset_state()


def     __lowerCAmelCase						(       a_		)      ->	Any:





     '''simple docstring'''





     main()


if __name__ == "__main__":
 main()




'''simple docstring'''





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowerCAmelCase  :Union[str, Any]  				=    logging.get_logger(__name__)  # pylint: disable=invalid-name


def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     warnings.warn(
         'The preprocess method is deprecated and will be removed in a future version. Please'
         ' use VaeImageProcessor.preprocess instead'       ,      a_       ,      )
     if isinstance(a_       ,      torch.Tensor		):
          return image
     elif isinstance(a_       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE					:			str    	=       [image]

     if isinstance(image[0]       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[str]    	=       image[0].size
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

          SCREAMING_SNAKE_CASE					:			Any    	=       [np.array(i.resize((w, h)       ,      resample=PIL_INTERPOLATION['lanczos']		)		)[None, :] for i in image]
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       np.concatenate(a_       ,      axis=0		)
          SCREAMING_SNAKE_CASE					:			Tuple    	=       np.array(a_		).astype(np.floataa		) / 255.0
          SCREAMING_SNAKE_CASE					:			Tuple    	=       image.transpose(0       ,      3       ,      1       ,      2		)
          SCREAMING_SNAKE_CASE					:			Dict    	=       2.0 * image - 1.0
          SCREAMING_SNAKE_CASE					:			int    	=       torch.from_numpy(a_		)
     elif isinstance(image[0]       ,      torch.Tensor		):
          SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.cat(a_       ,      dim=0		)
     return image


def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     if isinstance(a_       ,      torch.Tensor		):
          return mask
     elif isinstance(a_       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [mask]

     if isinstance(mask[0]       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[Any]    	=       mask[0].size
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
          SCREAMING_SNAKE_CASE					:			Tuple    	=       [np.array(m.convert('L'		).resize((w, h)       ,      resample=PIL_INTERPOLATION['nearest']		)		)[None, :] for m in mask]
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       np.concatenate(a_       ,      axis=0		)
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       mask.astype(np.floataa		) / 255.0
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       0
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       1
          SCREAMING_SNAKE_CASE					:			str    	=       torch.from_numpy(a_		)
     elif isinstance(mask[0]       ,      torch.Tensor		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.cat(a_       ,      dim=0		)
     return mask






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   UNetaDModel
    snake_case__							:   RePaintScheduler






    def __init__(       self    ,							lowercase__    ,							lowercase__ )							-> Union[str, Any]:
         super().__init__()
         self.register_modules(unet=lowercase__    ,							scheduler=lowercase__ )






    @torch.no_grad()
    def __call__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = 250    ,							lowercase__ = 0.0    ,							lowercase__ = 10    ,							lowercase__ = 10    ,							lowercase__ = None    ,							lowercase__ = "pil"    ,							lowercase__ = True    ,							)							-> Union[ImagePipelineOutput, Tuple]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       image

         SCREAMING_SNAKE_CASE					:			List[str]    	=       _preprocess_image(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       original_image.to(device=self.device    ,							dtype=self.unet.dtype )
         SCREAMING_SNAKE_CASE					:			Any    	=       _preprocess_mask(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       mask_image.to(device=self.device    ,							dtype=self.unet.dtype )

         SCREAMING_SNAKE_CASE					:			int    	=       original_image.shape[0]

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

         SCREAMING_SNAKE_CASE					:			Dict    	=       original_image.shape
         SCREAMING_SNAKE_CASE					:			Any    	=       randn_tensor(lowercase__    ,							generator=lowercase__    ,							device=self.device    ,							dtype=self.unet.dtype )

         # set step values
         self.scheduler.set_timesteps(lowercase__    ,							lowercase__    ,							lowercase__    ,							self.device )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       eta

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.scheduler.timesteps[0] + 1
         SCREAMING_SNAKE_CASE					:			Tuple    	=       generator[0] if isinstance(lowercase__    ,							lowercase__ ) else generator
         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
              if t < t_last:
                   # predict the noise residual
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.unet(lowercase__    ,							lowercase__ ).sample
                   # compute previous image: x_t -> x_t-1
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ ).prev_sample

              else:
                   # compute the reverse: x_t-1 -> x_t
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       self.scheduler.undo_step(lowercase__    ,							lowercase__    ,							lowercase__ )
              SCREAMING_SNAKE_CASE					:			int    	=       t

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       (image / 2 + 0.5).clamp(0    ,							1 )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       image.cpu().permute(0    ,							2    ,							3    ,							1 ).numpy()
         if output_type == "pil":
              SCREAMING_SNAKE_CASE					:			Dict    	=       self.numpy_to_pil(lowercase__ )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=lowercase__ )



