


'''simple docstring'''

# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
lowercase__					  =		1e-10
lowercase__					  =		"This is incredible! I love it, this is the best chicken I have ever had."
lowercase__					  =		100





class     __SCREAMING_SNAKE_CASE       (   nn.Module			):






     def __init__(      self  ,				__UpperCamelCase  ,				__UpperCamelCase="gpt2-medium"  ,				__UpperCamelCase=False  ,				__UpperCamelCase="cpu"		)							->     Optional[Any]:
          super().__init__()
          _a            =		GPTaTokenizer.from_pretrained(__UpperCamelCase		)
          _a            =		GPTaLMHeadModel.from_pretrained(__UpperCamelCase		)
          _a            =		self.encoder.transformer.config.hidden_size
          _a            =		ClassificationHead(class_size=__UpperCamelCase  ,				embed_size=self.embed_size		)
          _a            =		cached_mode
          _a            =		device






     def 			a_       (      self		)							->     str:
          return self.classifier_head






     def 			a_       (      self		)							->     Optional[int]:
          for param in self.encoder.parameters():
               _a            =		False
          self.classifier_head.train()






     def 			a_       (      self  ,				__UpperCamelCase		)							->     str:
          _a            =		x.ne(0		).unsqueeze(2		).repeat(1  ,				1  ,				self.embed_size		).float().to(self.device		).detach()
          _a            =		self.encoder.transformer(__UpperCamelCase		)["last_hidden_state"]
          _a            =		hidden * mask
          _a            =		torch.sum(__UpperCamelCase  ,				dim=1		) / (torch.sum(__UpperCamelCase  ,				dim=1		).detach() + EPSILON)
          return avg_hidden






     def 			a_       (      self  ,				__UpperCamelCase		)							->     Any:
          if self.cached_mode:
               _a            =		x.to(self.device		)
          else:
               _a            =		self.avg_representation(x.to(self.device		)		)

          _a            =		self.classifier_head(__UpperCamelCase		)
          _a            =		nn.functional.log_softmax(__UpperCamelCase  ,				dim=-1		)

          return probs







class     __SCREAMING_SNAKE_CASE       (   data.Dataset			):






     def __init__(      self  ,				__UpperCamelCase  ,				__UpperCamelCase		)							->     int:
          _a            =		X
          _a            =		y






     def __len__(      self		)							->     List[Any]:
          return len(self.X		)






     def __getitem__(      self  ,				__UpperCamelCase		)							->     Any:
          _a            =		{}
          _a            =		self.X[index]
          _a            =		self.y[index]
          return data







def __UpperCamelCase (      __lowerCamelCase		:			Union[str, Any]					)   ->  int:







     '''simple docstring'''






     def pad_sequences(__lowerCamelCase		:			Union[str, Any]					):
          _a            =		[len(__lowerCamelCase					) for seq in sequences]

          _a            =		torch.zeros(len(__lowerCamelCase					)   ,	max(__lowerCamelCase					)					).long()  # padding value = 0

          for i, seq in enumerate(__lowerCamelCase					):
               _a            =		lengths[i]
               _a            =		seq[:end]

          return padded_sequences, lengths

     _a            =		{}
     for key in data[0].keys():
          _a            =		[d[key] for d in data]

     _a      ,      _a            =		pad_sequences(item_info["X"]					)
     _a            =		torch.tensor(item_info["y"]   ,	dtype=torch.long					)

     return x_batch, y_batch






def __UpperCamelCase (      __lowerCamelCase		:			List[Any]					)   ->  Optional[int]:







     '''simple docstring'''






     _a            =		{}
     for key in data[0].keys():
          _a            =		[d[key] for d in data]

     _a            =		torch.cat(item_info["X"]   ,	0					)
     _a            =		torch.tensor(item_info["y"]   ,	dtype=torch.long					)

     return x_batch, y_batch






def __UpperCamelCase (      __lowerCamelCase		:			Tuple   ,	__lowerCamelCase		:			Optional[Any]   ,	__lowerCamelCase		:			List[Any]   ,	__lowerCamelCase		:			Union[str, Any]=0   ,	__lowerCamelCase		:			Optional[int]=10   ,	__lowerCamelCase		:			List[Any]="cpu"					)   ->  Union[str, Any]:







     '''simple docstring'''






     _a            =		0
     discriminator.train_custom()
     for batch_idx, (input_t, target_t) in enumerate(__lowerCamelCase					):
          _a      ,      _a            =		input_t.to(__lowerCamelCase					), target_t.to(__lowerCamelCase					)

          optimizer.zero_grad()

          _a            =		discriminator(__lowerCamelCase					)
          _a            =		nn.functional.nll_loss(__lowerCamelCase   ,	__lowerCamelCase					)
          loss.backward(retain_graph=__lowerCamelCase					)
          optimizer.step()

          samples_so_far += len(__lowerCamelCase					)

          if batch_idx % log_interval == 0:
               print(
                   "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                       epoch + 1   ,	__lowerCamelCase   ,	len(data_loader.dataset					)   ,	100 * samples_so_far / len(data_loader.dataset					)   ,	loss.item()   ,	)					)






def __UpperCamelCase (      __lowerCamelCase		:			Optional[Any]   ,	__lowerCamelCase		:			List[str]   ,	__lowerCamelCase		:			Any="cpu"					)   ->  List[str]:







     '''simple docstring'''






     discriminator.eval()
     _a            =		0
     _a            =		0
     with torch.no_grad():
          for input_t, target_t in data_loader:
               _a      ,      _a            =		input_t.to(__lowerCamelCase					), target_t.to(__lowerCamelCase					)
               _a            =		discriminator(__lowerCamelCase					)
               # sum up batch loss
               test_loss += nn.functional.nll_loss(__lowerCamelCase   ,	__lowerCamelCase   ,	reduction="sum"					).item()
               # get the index of the max log-probability
               _a            =		output_t.argmax(dim=1   ,	keepdim=__lowerCamelCase					)
               correct += pred_t.eq(target_t.view_as(__lowerCamelCase					)					).sum().item()

     test_loss /= len(data_loader.dataset					)

     print(
         "Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
             __lowerCamelCase   ,	__lowerCamelCase   ,	len(data_loader.dataset					)   ,	1_00.0 * correct / len(data_loader.dataset					)					)					)






def __UpperCamelCase (      __lowerCamelCase		:			Optional[int]   ,	__lowerCamelCase		:			Dict   ,	__lowerCamelCase		:			Dict   ,	__lowerCamelCase		:			Optional[int]=False   ,	__lowerCamelCase		:			Tuple="cpu"					)   ->  List[Any]:







     '''simple docstring'''






     _a            =		model.tokenizer.encode(__lowerCamelCase					)
     _a            =		torch.tensor([input_t]   ,	dtype=torch.long   ,	device=__lowerCamelCase					)
     if cached:
          _a            =		model.avg_representation(__lowerCamelCase					)

     _a            =		model(__lowerCamelCase					).data.cpu().numpy().flatten().tolist()
     print("Input sentence:"   ,	__lowerCamelCase					)
     print(
         "Predictions:"   ,	", ".join("{}: {:.4f}".format(__lowerCamelCase   ,	math.exp(__lowerCamelCase					)					) for c, log_prob in zip(__lowerCamelCase   ,	__lowerCamelCase					)					)   ,	)






def __UpperCamelCase (      __lowerCamelCase		:			Optional[Any]   ,	__lowerCamelCase		:			Union[str, Any]   ,	__lowerCamelCase		:			int   ,	__lowerCamelCase		:			Union[str, Any]=False   ,	__lowerCamelCase		:			List[Any]="cpu"					)   ->  Dict:







     '''simple docstring'''






     _a            =		torch.utils.data.DataLoader(dataset=__lowerCamelCase   ,	batch_size=__lowerCamelCase   ,	collate_fn=__lowerCamelCase					)

     _a            =		[]
     _a            =		[]
     for batch_idx, (x, y) in enumerate(tqdm(__lowerCamelCase   ,	ascii=__lowerCamelCase					)					):
          with torch.no_grad():
               _a            =		x.to(__lowerCamelCase					)
               _a            =		discriminator.avg_representation(__lowerCamelCase					).cpu().detach()
               _a            =		torch.unbind(avg_rep.unsqueeze(1					)					)
               xs += avg_rep_list
               ys += y.cpu().numpy().tolist()

     _a            =		torch.utils.data.DataLoader(
         dataset=Dataset(__lowerCamelCase   ,	__lowerCamelCase					)   ,	batch_size=__lowerCamelCase   ,	shuffle=__lowerCamelCase   ,	collate_fn=__lowerCamelCase					)

     return data_loader






def __UpperCamelCase (      __lowerCamelCase		:			Dict   ,	__lowerCamelCase		:			int=None   ,	__lowerCamelCase		:			Optional[int]="gpt2-medium"   ,	__lowerCamelCase		:			List[Any]=10   ,	__lowerCamelCase		:			str=64   ,	__lowerCamelCase		:			Dict=10   ,	__lowerCamelCase		:			Optional[int]=False   ,	__lowerCamelCase		:			str=False   ,	__lowerCamelCase		:			Optional[Any]=False   ,	)   ->  Dict:







     '''simple docstring'''






     _a            =		"cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

     print("Preprocessing {} dataset...".format(__lowerCamelCase					)					)
     _a            =		time.time()

     if dataset == "SST":
          _a            =		["positive", "negative", "very positive", "very negative", "neutral"]
          _a            =		{c: i for i, c in enumerate(__lowerCamelCase					)}

          _a            =		Discriminator(
              class_size=len(__lowerCamelCase					)   ,	pretrained_model=__lowerCamelCase   ,	cached_mode=__lowerCamelCase   ,	device=__lowerCamelCase					).to(__lowerCamelCase					)

          _a            =		torchtext_data.Field()
          _a            =		torchtext_data.Field(sequential=__lowerCamelCase					)
          _a      ,      _a      ,      _a            =		datasets.SST.splits(
              __lowerCamelCase   ,	__lowerCamelCase   ,	fine_grained=__lowerCamelCase   ,	train_subtrees=__lowerCamelCase   ,	)

          _a            =		[]
          _a            =		[]
          for i in trange(len(__lowerCamelCase					)   ,	ascii=__lowerCamelCase					):
               _a            =		TreebankWordDetokenizer().detokenize(vars(train_data[i]					)["text"]					)
               _a            =		discriminator.tokenizer.encode(__lowerCamelCase					)
               _a            =		torch.tensor([5_0256] + seq   ,	device=__lowerCamelCase   ,	dtype=torch.long					)
               x.append(__lowerCamelCase					)
               y.append(classaidx[vars(train_data[i]					)["label"]]					)
          _a            =		Dataset(__lowerCamelCase   ,	__lowerCamelCase					)

          _a            =		[]
          _a            =		[]
          for i in trange(len(__lowerCamelCase					)   ,	ascii=__lowerCamelCase					):
               _a            =		TreebankWordDetokenizer().detokenize(vars(test_data[i]					)["text"]					)
               _a            =		discriminator.tokenizer.encode(__lowerCamelCase					)
               _a            =		torch.tensor([5_0256] + seq   ,	device=__lowerCamelCase   ,	dtype=torch.long					)
               test_x.append(__lowerCamelCase					)
               test_y.append(classaidx[vars(test_data[i]					)["label"]]					)
          _a            =		Dataset(__lowerCamelCase   ,	__lowerCamelCase					)

          _a            =		{
              "class_size": len(__lowerCamelCase					),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 2,
          }

     elif dataset == "clickbait":
          _a            =		["non_clickbait", "clickbait"]
          _a            =		{c: i for i, c in enumerate(__lowerCamelCase					)}

          _a            =		Discriminator(
              class_size=len(__lowerCamelCase					)   ,	pretrained_model=__lowerCamelCase   ,	cached_mode=__lowerCamelCase   ,	device=__lowerCamelCase					).to(__lowerCamelCase					)

          with open("datasets/clickbait/clickbait_train_prefix.txt"					) as f:
               _a            =		[]
               for i, line in enumerate(__lowerCamelCase					):
                    try:
                         data.append(eval(__lowerCamelCase					)					)
                    except Exception:
                         print("Error evaluating line {}: {}".format(__lowerCamelCase   ,	__lowerCamelCase					)					)
                         continue
          _a            =		[]
          _a            =		[]
          with open("datasets/clickbait/clickbait_train_prefix.txt"					) as f:
               for i, line in enumerate(tqdm(__lowerCamelCase   ,	ascii=__lowerCamelCase					)					):
                    try:
                         _a            =		eval(__lowerCamelCase					)
                         _a            =		discriminator.tokenizer.encode(d["text"]					)

                         if len(__lowerCamelCase					) < max_length_seq:
                              _a            =		torch.tensor([5_0256] + seq   ,	device=__lowerCamelCase   ,	dtype=torch.long					)
                         else:
                              print("Line {} is longer than maximum length {}".format(__lowerCamelCase   ,	__lowerCamelCase					)					)
                              continue
                         x.append(__lowerCamelCase					)
                         y.append(d["label"]					)
                    except Exception:
                         print("Error evaluating / tokenizing line {}, skipping it".format(__lowerCamelCase					)					)
                         pass

          _a            =		Dataset(__lowerCamelCase   ,	__lowerCamelCase					)
          _a            =		int(0.9 * len(__lowerCamelCase					)					)
          _a            =		len(__lowerCamelCase					) - train_size
          _a      ,      _a            =		torch.utils.data.random_split(__lowerCamelCase   ,	[train_size, test_size]					)

          _a            =		{
              "class_size": len(__lowerCamelCase					),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 1,
          }

     elif dataset == "toxic":
          _a            =		["non_toxic", "toxic"]
          _a            =		{c: i for i, c in enumerate(__lowerCamelCase					)}

          _a            =		Discriminator(
              class_size=len(__lowerCamelCase					)   ,	pretrained_model=__lowerCamelCase   ,	cached_mode=__lowerCamelCase   ,	device=__lowerCamelCase					).to(__lowerCamelCase					)

          _a            =		[]
          _a            =		[]
          with open("datasets/toxic/toxic_train.txt"					) as f:
               for i, line in enumerate(tqdm(__lowerCamelCase   ,	ascii=__lowerCamelCase					)					):
                    try:
                         _a            =		eval(__lowerCamelCase					)
                         _a            =		discriminator.tokenizer.encode(d["text"]					)

                         if len(__lowerCamelCase					) < max_length_seq:
                              _a            =		torch.tensor([5_0256] + seq   ,	device=__lowerCamelCase   ,	dtype=torch.long					)
                         else:
                              print("Line {} is longer than maximum length {}".format(__lowerCamelCase   ,	__lowerCamelCase					)					)
                              continue
                         x.append(__lowerCamelCase					)
                         y.append(int(np.sum(d["label"]					) > 0					)					)
                    except Exception:
                         print("Error evaluating / tokenizing line {}, skipping it".format(__lowerCamelCase					)					)
                         pass

          _a            =		Dataset(__lowerCamelCase   ,	__lowerCamelCase					)
          _a            =		int(0.9 * len(__lowerCamelCase					)					)
          _a            =		len(__lowerCamelCase					) - train_size
          _a      ,      _a            =		torch.utils.data.random_split(__lowerCamelCase   ,	[train_size, test_size]					)

          _a            =		{
              "class_size": len(__lowerCamelCase					),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 0,
          }

     else:  # if dataset == "generic":
          # This assumes the input dataset is a TSV with the following structure:
          # class \t text

          if dataset_fp is None:
               raise ValueError("When generic dataset is selected, dataset_fp needs to be specified aswell."					)

          _a            =		set()
          with open(__lowerCamelCase					) as f:
               _a            =		csv.reader(__lowerCamelCase   ,	delimiter="\t"					)
               for row in tqdm(__lowerCamelCase   ,	ascii=__lowerCamelCase					):
                    if row:
                         classes.add(row[0]					)

          _a            =		sorted(__lowerCamelCase					)
          _a            =		{c: i for i, c in enumerate(__lowerCamelCase					)}

          _a            =		Discriminator(
              class_size=len(__lowerCamelCase					)   ,	pretrained_model=__lowerCamelCase   ,	cached_mode=__lowerCamelCase   ,	device=__lowerCamelCase					).to(__lowerCamelCase					)

          _a            =		[]
          _a            =		[]
          with open(__lowerCamelCase					) as f:
               _a            =		csv.reader(__lowerCamelCase   ,	delimiter="\t"					)
               for i, row in enumerate(tqdm(__lowerCamelCase   ,	ascii=__lowerCamelCase					)					):
                    if row:
                         _a            =		row[0]
                         _a            =		row[1]

                         try:
                              _a            =		discriminator.tokenizer.encode(__lowerCamelCase					)
                              if len(__lowerCamelCase					) < max_length_seq:
                                   _a            =		torch.tensor([5_0256] + seq   ,	device=__lowerCamelCase   ,	dtype=torch.long					)

                              else:
                                   print("Line {} is longer than maximum length {}".format(__lowerCamelCase   ,	__lowerCamelCase					)					)
                                   continue

                              x.append(__lowerCamelCase					)
                              y.append(classaidx[label]					)

                         except Exception:
                              print("Error tokenizing line {}, skipping it".format(__lowerCamelCase					)					)
                              pass

          _a            =		Dataset(__lowerCamelCase   ,	__lowerCamelCase					)
          _a            =		int(0.9 * len(__lowerCamelCase					)					)
          _a            =		len(__lowerCamelCase					) - train_size
          _a      ,      _a            =		torch.utils.data.random_split(__lowerCamelCase   ,	[train_size, test_size]					)

          _a            =		{
              "class_size": len(__lowerCamelCase					),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 0,
          }

     _a            =		time.time()
     print("Preprocessed {} data points".format(len(__lowerCamelCase					) + len(__lowerCamelCase					)					)					)
     print("Data preprocessing took: {:.3f}s".format(end - start					)					)

     if cached:
          print("Building representation cache..."					)

          _a            =		time.time()

          _a            =		get_cached_data_loader(__lowerCamelCase   ,	__lowerCamelCase   ,	__lowerCamelCase   ,	shuffle=__lowerCamelCase   ,	device=__lowerCamelCase					)

          _a            =		get_cached_data_loader(__lowerCamelCase   ,	__lowerCamelCase   ,	__lowerCamelCase   ,	device=__lowerCamelCase					)

          _a            =		time.time()
          print("Building representation cache took: {:.3f}s".format(end - start					)					)

     else:
          _a            =		torch.utils.data.DataLoader(
              dataset=__lowerCamelCase   ,	batch_size=__lowerCamelCase   ,	shuffle=__lowerCamelCase   ,	collate_fn=__lowerCamelCase					)
          _a            =		torch.utils.data.DataLoader(dataset=__lowerCamelCase   ,	batch_size=__lowerCamelCase   ,	collate_fn=__lowerCamelCase					)

     if save_model:
          with open("{}_classifier_head_meta.json".format(__lowerCamelCase					)   ,	"w"					) as meta_file:
               json.dump(__lowerCamelCase   ,	__lowerCamelCase					)

     _a            =		optim.Adam(discriminator.parameters()   ,	lr=0.00_01					)

     for epoch in range(__lowerCamelCase					):
          _a            =		time.time()
          print("\nEpoch"   ,	epoch + 1					)

          train_epoch(
              discriminator=__lowerCamelCase   ,	data_loader=__lowerCamelCase   ,	optimizer=__lowerCamelCase   ,	epoch=__lowerCamelCase   ,	log_interval=__lowerCamelCase   ,	device=__lowerCamelCase   ,	)
          evaluate_performance(data_loader=__lowerCamelCase   ,	discriminator=__lowerCamelCase   ,	device=__lowerCamelCase					)

          _a            =		time.time()
          print("Epoch took: {:.3f}s".format(end - start					)					)

          print("\nExample prediction"					)
          predict(__lowerCamelCase   ,	__lowerCamelCase   ,	__lowerCamelCase   ,	cached=__lowerCamelCase   ,	device=__lowerCamelCase					)

          if save_model:
               # torch.save(discriminator.state_dict(),
               #           "{}_discriminator_{}.pt".format(
               #               args.dataset, epoch + 1
               #               ))
               torch.save(
                   discriminator.get_classifier().state_dict()   ,	"{}_classifier_head_epoch_{}.pt".format(__lowerCamelCase   ,	epoch + 1					)   ,	)


if __name__ == "__main__":
   lowercase__					  =		argparse.ArgumentParser(description="Train a discriminator on top of GPT-2 representations")
   parser.add_argument(
       "--dataset",
       type=str,
       default="SST",
       choices=("SST", "clickbait", "toxic", "generic"),
       help=(
           "dataset to train the discriminator on."
           "In case of generic, the dataset is expected"
           "to be a TSBV file with structure: class \\t text"
       ),
   )
   parser.add_argument(
       "--dataset_fp",
       type=str,
       default="",
       help="File path of the dataset to use. Needed only in case of generic datadset",
   )
   parser.add_argument(
       "--pretrained_model", type=str, default="gpt2-medium", help="Pretrained model to use as encoder"
   )
   parser.add_argument("--epochs", type=int, default=10, metavar="N", help="Number of training epochs")
   parser.add_argument(
       "--batch_size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
   )
   parser.add_argument(
       "--log_interval",
       type=int,
       default=10,
       metavar="N",
       help="how many batches to wait before logging training status",
   )
   parser.add_argument("--save_model", action="store_true", help="whether to save the model")
   parser.add_argument("--cached", action="store_true", help="whether to cache the input representations")
   parser.add_argument("--no_cuda", action="store_true", help="use to turn off cuda")
   lowercase__					  =		parser.parse_args()

   train_discriminator(**(vars(args)))





'''simple docstring'''

import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			,      unittest.TestCase			):
     UpperCAmelCase			       =					RePaintPipeline
     UpperCAmelCase			       =					IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
     UpperCAmelCase			       =					PipelineTesterMixin.required_optional_params - {
         '''latents''',
         '''num_images_per_prompt''',
         '''callback''',
         '''callback_steps''',
     }
     UpperCAmelCase			       =					IMAGE_INPAINTING_BATCH_PARAMS
     UpperCAmelCase			       =					False






     def 			a_       (      self		)							->     int:
          torch.manual_seed(0		)
          torch.manual_seed(0		)
          _a            =		UNetaDModel(
              block_out_channels=(32, 64)  ,				layers_per_block=2  ,				sample_size=32  ,				in_channels=3  ,				out_channels=3  ,				down_block_types=("DownBlock2D", "AttnDownBlock2D")  ,				up_block_types=("AttnUpBlock2D", "UpBlock2D")  ,				)
          _a            =		RePaintScheduler()
          _a            =		{"unet": unet, "scheduler": scheduler}
          return components






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=0		)							->     Tuple:
          if str(__UpperCamelCase		).startswith("mps"		):
               _a            =		torch.manual_seed(__UpperCamelCase		)
          else:
               _a            =		torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)
          _a            =		np.random.RandomState(__UpperCamelCase		).standard_normal((1, 3, 32, 32)		)
          _a            =		torch.from_numpy(__UpperCamelCase		).to(device=__UpperCamelCase  ,				dtype=torch.floataa		)
          _a            =		(image > 0).to(device=__UpperCamelCase  ,				dtype=torch.floataa		)
          _a            =		{
              "image": image,
              "mask_image": mask,
              "generator": generator,
              "num_inference_steps": 5,
              "eta": 0.0,
              "jump_length": 2,
              "jump_n_sample": 2,
              "output_type": "numpy",
          }
          return inputs






     def 			a_       (      self		)							->     Optional[Any]:
          _a            =		"cpu"  # ensure determinism for the device-dependent torch.Generator
          _a            =		self.get_dummy_components()
          _a            =		RePaintPipeline(**__UpperCamelCase		)
          _a            =		sd_pipe.to(__UpperCamelCase		)
          sd_pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		self.get_dummy_inputs(__UpperCamelCase		)
          _a            =		sd_pipe(**__UpperCamelCase		).images
          _a            =		image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          _a            =		np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]		)

          assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3






     @skip_mps
     def 			a_       (      self		)							->     Union[str, Any]:
          return super().test_save_load_local()






     @unittest.skip("non-deterministic pipeline"		)
     def 			a_       (      self		)							->     int:
          return super().test_inference_batch_single_identical()






     @skip_mps
     def 			a_       (      self		)							->     str:
          return super().test_dict_tuple_outputs_equivalent()






     @skip_mps
     def 			a_       (      self		)							->     int:
          return super().test_save_load_optional_components()






     @skip_mps
     def 			a_       (      self		)							->     Optional[int]:
          return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






     def 			a_       (      self		)							->     List[str]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def 			a_       (      self		)							->     Union[str, Any]:
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
              "repaint/celeba_hq_256.png"		)
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"		)
          _a            =		load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
              "repaint/celeba_hq_256_result.npy"		)

          _a            =		"google/ddpm-ema-celebahq-256"
          _a            =		UNetaDModel.from_pretrained(__UpperCamelCase		)
          _a            =		RePaintScheduler.from_pretrained(__UpperCamelCase		)

          _a            =		RePaintPipeline(unet=__UpperCamelCase  ,				scheduler=__UpperCamelCase		).to(__UpperCamelCase		)
          repaint.set_progress_bar_config(disable=__UpperCamelCase		)
          repaint.enable_attention_slicing()

          _a            =		torch.manual_seed(0		)
          _a            =		repaint(
              __UpperCamelCase  ,				__UpperCamelCase  ,				num_inference_steps=250  ,				eta=0.0  ,				jump_length=10  ,				jump_n_sample=10  ,				generator=__UpperCamelCase  ,				output_type="np"  ,				)
          _a            =		output.images[0]

          assert image.shape == (256, 256, 3)
          assert np.abs(expected_image - image		).mean() < 1e-2

