





'''simple docstring'''

import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa





class lowerCamelCase    (__A       ):
  _lowercase	:						List[Any]							       =					['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']







  def __init__(		self  ,  lowercase__		)		->    List[str]:






        """simple docstring"""





        assert Path(hparams.data_dir		).exists()
        _snake_case		:						Any         =  Path(hparams.output_dir		)
        self.output_dir.mkdir(exist_ok=__snake_case		)

        _snake_case		:						List[str]         =  self.output_dir.joinpath('''student'''		)

        _snake_case		:						List[str]         =  str(__snake_case		)  # Tell lightning we are training the student
        _snake_case		:						Tuple         =  AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher		).eval()
        use_task_specific_params(__snake_case  ,  hparams.task		)  # We copy good generation parameters to student by default
        if hparams.student is not None:
              _snake_case		:						Dict         =  AutoModelForSeqaSeqLM.from_pretrained(hparams.student		)
              use_task_specific_params(__snake_case  ,  hparams.task		)
              _snake_case		:						Tuple         =  None, None
        else:
              _snake_case		:						Optional[Any]         =  create_student_by_copying_alternating_layers(
                  __snake_case  ,  e=hparams.student_encoder_layers  ,  d=hparams.student_decoder_layers  ,  save_path=__snake_case		)

        if hparams.length_penalty != -1:
              _snake_case		:						int         =  hparams.length_penalty
        _snake_case		:						Any         =  hparams.teacher  # Use teacher's tokenizer
        super().__init__(__snake_case  ,  model=__snake_case  ,  config=student.config		)
        assert student.config.model_type == teacher.config.model_type, (
            F'''teacher, student model types should be the same, got {student.config.model_type} !='''
            F''' {teacher.config.model_type}'''
        )

        if student.config.model_type == "t5":
              _snake_case		:						Tuple         =  len(student.get_encoder().block		)
              _snake_case		:						int         =  len(student.get_decoder().block		)
              _snake_case		:						List[str]         =  len(teacher.get_encoder().block		)
              _snake_case		:						Optional[int]         =  len(teacher.get_decoder().block		)
        else:
              _snake_case		:						int         =  student.config.encoder_layers
              _snake_case		:						str         =  student.config.decoder_layers
              _snake_case		:						List[str]         =  teacher.config.encoder_layers
              _snake_case		:						str         =  teacher.config.decoder_layers

        _snake_case		:						Any         =  not (hparams.student is None or hparams.teacher == hparams.student)
        _snake_case		:						str         =  (not self.different_base_models) and hparams.alpha_hid > 0
        _snake_case		:						Tuple         =  self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
        # self.different_encoder determines whether we need to run the teacher encoder
        _snake_case		:						List[str]         =  teacher
        freeze_params(self.teacher		)

        if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
              try:
                    del self.teacher.model.encoder
              except AttributeError:  # T5
                    del self.teacher.encoder

        if e_layer_ids is None:
              _snake_case		:						List[Any]         =  list(range(__snake_case		)		)
        if d_layer_ids is None:
              _snake_case		:						int         =  list(range(__snake_case		)		)

        _snake_case		:						List[str]         =  e_layer_ids, d_layer_ids  # type: List[int], List[int]

        if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
              if hparams.supervise_forward:
                    _snake_case		:						str         =  get_layers_to_supervise(
                        n_student=len(self.e_layer_ids		)  ,  n_teacher=__snake_case		)
                    _snake_case		:						List[Any]         =  get_layers_to_supervise(
                        n_student=len(self.d_layer_ids		)  ,  n_teacher=__snake_case		)
              else:  # student layer should emulate hidden states of the teacher layer it was copied from
                    _snake_case		:						Optional[Any]         =  self.e_layer_ids
                    _snake_case		:						int         =  self.d_layer_ids
        else:
              _snake_case		:						int         =  None
              _snake_case		:						Any         =  None

        _snake_case		:						List[str]         =  nn.KLDivLoss(reduction='''batchmean'''		)
        _snake_case		:						Any         =  2.0
        _snake_case		:						int         =  hparams.alpha_mlm
        _snake_case		:						Optional[Any]         =  hparams.alpha_ce
        _snake_case		:						Optional[int]         =  hparams.alpha_hid
        gc.collect()
        torch.cuda.empty_cache()







  def 					UpperCAmelCase_					(		self  ,  lowercase__  ,  lowercase__  ,  lowercase__		)		->    Any:






        """simple docstring"""





        _snake_case		:						Dict         =  mask[:, :, None].expand_as(__snake_case		)
        _snake_case		:						int         =  s_logits.size(-1		)
        _snake_case		:						List[str]         =  torch.masked_select(__snake_case  ,  __snake_case		)  # (bs * seq_length * voc_size) modulo the 1s in mask
        _snake_case		:						Optional[int]         =  torch.masked_select(__snake_case  ,  __snake_case		)  # (bs * seq_length * voc_size) modulo the 1s in mask
        _snake_case		:						str         =  s_logits_slct.view(-1  ,  __snake_case		)  # (bs * seq_length, voc_size) modulo the 1s in mask
        _snake_case		:						Union[str, Any]         =  t_logits_slct.view(-1  ,  __snake_case		)  # (bs * seq_length, voc_size) modulo the 1s in mask
        assert t_logits_slct.size() == s_logits_slct.size()
        _snake_case		:						List[str]         =  (
            self.ce_loss_fct(
                nn.functional.log_softmax(s_logits_slct / self.temperature  ,  dim=-1		)  ,  nn.functional.softmax(t_logits_slct / self.temperature  ,  dim=-1		)  ,  )
            * (self.temperature) ** 2
        )
        return loss_ce







  @staticmethod
  def 					UpperCAmelCase_					(		lowercase__  ,  lowercase__		)		->    List[Any]:






        """simple docstring"""





        SummarizationModule.add_model_specific_args(__snake_case  ,  __snake_case		)
        add_distill_args(__snake_case		)
        return parser







  def 					UpperCAmelCase_					(		self  ,  lowercase__		)		->    tuple:






        """simple docstring"""





        _snake_case		:						Optional[int]         =  self.tokenizer.pad_token_id
        _snake_case		:						int         =  batch["input_ids"], batch["attention_mask"], batch["labels"]
        if isinstance(self.model  ,  __snake_case		):
              _snake_case		:						Optional[Any]         =  self.model._shift_right(__snake_case		)
        else:
              _snake_case		:						Dict         =  shift_tokens_right(__snake_case  ,  __snake_case		)

        # noinspection PyCallingNonCallable
        _snake_case		:						List[Any]         =  self(
            __snake_case  ,  attention_mask=__snake_case  ,  decoder_input_ids=__snake_case  ,  output_hidden_states=self.do_calc_hidden_loss  ,  output_attentions=__snake_case  ,  use_cache=__snake_case  ,  )
        _snake_case		:						Dict         =  student_outputs["logits"]

        # Same cross entropy vs. label smoothing logic as finetune.py
        assert lm_logits.shape[-1] == self.model.config.vocab_size
        if self.hparams.label_smoothing == 0:
              # Same behavior as modeling_bart.py, besides ignoring pad_token_id
              _snake_case		:						Union[str, Any]         =  nn.CrossEntropyLoss(ignore_index=__snake_case		)
              _snake_case		:						int         =  loss_fct(lm_logits.view(-1  ,  lm_logits.shape[-1]		)  ,  labels.view(-1		)		)
        else:
              _snake_case		:						Optional[Any]         =  nn.functional.log_softmax(__snake_case  ,  dim=-1		)
              _snake_case		:						Optional[int]         =  label_smoothed_nll_loss(
                  __snake_case  ,  __snake_case  ,  self.hparams.label_smoothing  ,  ignore_index=__snake_case		)

        def zero_tensor():
              return torch.tensor(0.0		).type_as(__snake_case		)

        _snake_case		:						List[str]         =  student_outputs[
            "encoder_last_hidden_state"
        ]  # use this unless self.different_base_models
        _snake_case		:						str         =  zero_tensor(), zero_tensor()
        if self.different_encoder:  # compute encoder hidden state loss
              _snake_case		:						Any         =  self.teacher.get_encoder()(
                  __snake_case  ,  attention_mask=__snake_case  ,  output_hidden_states=self.do_calc_hidden_loss  ,  )
              if self.different_base_models:
                    _snake_case		:						Optional[Any]         =  all_teacher_encoder_outputs["last_hidden_state"]
              elif self.do_calc_hidden_loss:
                    _snake_case		:						List[Any]         =  self.calc_hidden_loss(
                        __snake_case  ,  student_outputs['''encoder_hidden_states''']  ,  all_teacher_encoder_outputs['''hidden_states''']  ,  self.e_matches  ,  normalize_hidden=self.hparams.normalize_hidden  ,  )

        _snake_case		:						List[Any]         =  self.teacher(
            __snake_case  ,  attention_mask=__snake_case  ,  encoder_outputs=(teacher_enc_outputs,)  ,  decoder_input_ids=__snake_case  ,  output_hidden_states=self.do_calc_hidden_loss  ,  use_cache=__snake_case  ,  )
        _snake_case		:						int         =  decoder_input_ids.ne(__snake_case		)
        _snake_case		:						List[Any]         =  self.calc_ce_loss(__snake_case  ,  __snake_case  ,  teacher_outputs['''logits''']		)
        if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
              _snake_case		:						str         =  self.calc_hidden_loss(
                  __snake_case  ,  student_outputs['''decoder_hidden_states''']  ,  teacher_outputs['''decoder_hidden_states''']  ,  self.d_matches  ,  normalize_hidden=self.hparams.normalize_hidden  ,  )

        _snake_case		:						Tuple         =  (
            self.alpha_ce * loss_ce
            + self.alpha_mlm * student_lm_loss
            + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
        )
        return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec







  @staticmethod
  def 					UpperCAmelCase_					(		lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__		)		->    Union[str, Any]:






        """simple docstring"""





        _snake_case		:						Union[str, Any]         =  "expected list or tuple for hidden_states, got tensor of shape: "
        assert not isinstance(__snake_case  ,  torch.Tensor		), F'''{msg}{hidden_states.shape}'''
        assert not isinstance(__snake_case  ,  torch.Tensor		), F'''{msg}{hidden_states_T.shape}'''
        _snake_case		:						Optional[int]         =  attention_mask.to(hidden_states[0]		)
        _snake_case		:						Optional[int]         =  mask.sum() * hidden_states[0].size(-1		)
        _snake_case		:						Tuple         =  torch.stack([hidden_states[i] for i in range(len(__snake_case		)		)]		)
        _snake_case		:						Optional[int]         =  torch.stack([hidden_states_T[j] for j in matches]		)
        assert student_states.shape == teacher_states.shape, F'''{student_states.shape} != {teacher_states.shape}'''
        if normalize_hidden:
              _snake_case		:						int         =  nn.functional.layer_norm(__snake_case  ,  student_states.shape[1:]		)
              _snake_case		:						List[Any]         =  nn.functional.layer_norm(__snake_case  ,  teacher_states.shape[1:]		)
        _snake_case		:						List[str]         =  nn.functional.mse_loss(__snake_case  ,  __snake_case  ,  reduction='''none'''		)
        _snake_case		:						Optional[int]         =  (mse * mask.unsqueeze(0		).unsqueeze(-1		)).sum() / valid_count
        return masked_mse




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      parser.add_argument('''--teacher'''      ,   type=lowerCAmelCase_						)
      parser.add_argument('''--alpha_ce'''      ,   default=0.8      ,   type=lowerCAmelCase_						)
      parser.add_argument('''--alpha_mlm'''      ,   default=0.2      ,   type=lowerCAmelCase_						)
      parser.add_argument('''--alpha_hid'''      ,   default=0.0      ,   type=lowerCAmelCase_      ,   required=lowerCAmelCase_						)
      parser.add_argument('''--student'''      ,   type=lowerCAmelCase_      ,   required=lowerCAmelCase_						)
      parser.add_argument('''--student_decoder_layers'''      ,   default=12      ,   type=lowerCAmelCase_      ,   required=lowerCAmelCase_						)
      parser.add_argument('''--student_encoder_layers'''      ,   default=12      ,   type=lowerCAmelCase_      ,   required=lowerCAmelCase_						)
      parser.add_argument('''--no_teacher'''      ,   action='''store_true'''      ,   default=lowerCAmelCase_						)
      parser.add_argument('''--length_penalty'''      ,   type=lowerCAmelCase_      ,   default=-1						)
      parser.add_argument('''--supervise_forward'''      ,   action='''store_true'''      ,   default=lowerCAmelCase_						)
      parser.add_argument('''--normalize_hidden'''      ,   action='''store_true'''      ,   default=lowerCAmelCase_						)





class lowerCamelCase    (__A       ):
  _lowercase	:						str							       =					'translation'
  _lowercase	:						Tuple							       =					['bleu']
  _lowercase	:						Optional[Any]							       =					'bleu'







  def __init__(		self  ,  lowercase__  ,  **lowercase__		)		->    str:






        """simple docstring"""





        super().__init__(__snake_case  ,  **__snake_case		)
        assert hparams.src_lang is not None
        assert hparams.tgt_lang is not None
        _snake_case		:						Optional[int]         =  hparams.src_lang
        _snake_case		:						int         =  hparams.tgt_lang
        if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer  ,  __snake_case		):
              _snake_case		:						Union[str, Any]         =  self.tokenizer.lang_code_to_id[hparams.tgt_lang]







  def 					UpperCAmelCase_					(		self  ,  lowercase__  ,  lowercase__		)		->    dict:






        """simple docstring"""





        return calculate_bleu(__snake_case  ,  __snake_case		)







  @staticmethod
  def 					UpperCAmelCase_					(		lowercase__  ,  lowercase__		)		->    Any:






        """simple docstring"""





        TranslationModule.add_model_specific_args(__snake_case  ,  __snake_case		)
        add_distill_args(__snake_case		)
        return parser




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      if args.no_teacher:
            _snake_case		:						List[Any]         =  TranslationModule if "translation" in args.task else SummarizationModule
      else:  # DISTILL WITH TEACHER
            _snake_case		:						Any         =  TranslationDistiller if "translation" in args.task else SummarizationDistiller
      _snake_case		:						str         =  module_cls.__name__
      print(f'''using module {args.setup_cls}'''						)
      _snake_case		:						Any         =  module_cls(lowerCAmelCase_						)
      return model




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      Path(args.output_dir						).mkdir(exist_ok=lowerCAmelCase_						)
      check_output_dir(lowerCAmelCase_      ,   expected_items=3						)

      _snake_case		:						Dict         =  create_module(lowerCAmelCase_						)
      return ft_main(lowerCAmelCase_      ,   model=lowerCAmelCase_						)


if __name__ == "__main__":
   UpperCAmelCase       :		List[Any]		=				argparse.ArgumentParser()
   UpperCAmelCase       :		Optional[int]		=				pl.Trainer.add_argparse_args(parser)
   UpperCAmelCase       :		Tuple		=				SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
   UpperCAmelCase       :		List[Any]		=				parser.parse_args()

   distill_main(args)









import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 							_a(     __A						, unittest.TestCase     ):
		lowerCamelCase__  :Optional[int] 					=		PaintByExamplePipeline
		lowerCamelCase__  :Union[str, Any] 					=		IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
		lowerCamelCase__  :Optional[Any] 					=		IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		lowerCamelCase__  :int 					=		frozenset([]     )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







		def 							lowercase							(     self					)   ->   Dict:


							'''simple docstring'''

							torch.manual_seed(0					)
							_snake_case							:       Union[str, Any]												= UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)			,    layers_per_block=2			,    sample_size=3_2			,    in_channels=9			,    out_channels=4			,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,    cross_attention_dim=3_2			,    )
							_snake_case							:       int												= PNDMScheduler(skip_prk_steps=__snake_case					)
							torch.manual_seed(0					)
							_snake_case							:       Any												= AutoencoderKL(
							    block_out_channels=[3_2, 6_4]			,    in_channels=3			,    out_channels=3			,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,    latent_channels=4			,    )
							torch.manual_seed(0					)
							_snake_case							:       Dict												= CLIPVisionConfig(
							    hidden_size=3_2			,    projection_dim=3_2			,    intermediate_size=3_7			,    layer_norm_eps=1E-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    image_size=3_2			,    patch_size=4			,    )
							_snake_case							:       Any												= PaintByExampleImageEncoder(__snake_case			,    proj_size=3_2					)
							_snake_case							:       Dict												= CLIPImageProcessor(crop_size=3_2			,    size=3_2					)

							_snake_case							:       Any												= {
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							    "image_encoder": image_encoder,
							    "safety_checker": None,
							    "feature_extractor": feature_extractor,
							}
							return components







		def 							lowercase							(     self			,    __snake_case					)   ->   Tuple:


							'''simple docstring'''

							_snake_case							:       List[str]												= np.array(image.convert("RGB"					)					)
							_snake_case							:       str												= image[None].transpose(0			,    3			,    1			,    2					)
							_snake_case							:       Dict												= torch.from_numpy(__snake_case					).to(dtype=torch.floataa					) / 1_27.5 - 1.0
							return image







		def 							lowercase							(     self			,    __snake_case="cpu"			,    __snake_case=0					)   ->   Any:


							'''simple docstring'''

							_snake_case							:       Tuple												= floats_tensor((1, 3, 3_2, 3_2)			,    rng=random.Random(__snake_case					)					).to(__snake_case					)
							_snake_case							:       Union[str, Any]												= image.cpu().permute(0			,    2			,    3			,    1					)[0]
							_snake_case							:       List[Any]												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					).resize((6_4, 6_4)					)
							_snake_case							:       List[Any]												= Image.fromarray(np.uinta(image + 4					)					).convert("RGB"					).resize((6_4, 6_4)					)
							_snake_case							:       Any												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					).resize((3_2, 3_2)					)

							if str(__snake_case					).startswith("mps"					):
												_snake_case							:       Union[str, Any]												= torch.manual_seed(__snake_case					)
							else:
												_snake_case							:       List[Any]												= torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
							_snake_case							:       Optional[int]												= {
							    "example_image": example_image,
							    "image": init_image,
							    "mask_image": mask_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "guidance_scale": 6.0,
							    "output_type": "numpy",
							}
							return inputs







		def 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							_snake_case							:       int												= PaintByExamplePipeline(**__snake_case					)
							_snake_case							:       List[Any]												= pipe.to("cpu"					)
							pipe.set_progress_bar_config(disable=__snake_case					)

							_snake_case							:       List[str]												= self.get_dummy_inputs()
							_snake_case							:       int												= pipe(**__snake_case					)
							_snake_case							:       Dict												= output.images

							_snake_case							:       Optional[int]												= image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							_snake_case							:       int												= np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2







		def 							lowercase							(     self					)   ->   List[Any]:


							'''simple docstring'''

							_snake_case							:       Optional[int]												= "cpu"
							_snake_case							:       str												= self.get_dummy_inputs()
							inputs.pop("mask_image"					)
							_snake_case							:       Union[str, Any]												= self.convert_to_pt(inputs.pop("image"					)					)
							_snake_case							:       Union[str, Any]												= image.clamp(0			,    1					) / 2

							# make sure here that pndm scheduler skips prk
							_snake_case							:       Dict												= PaintByExamplePipeline(**self.get_dummy_components()					)
							_snake_case							:       Union[str, Any]												= pipe.to(__snake_case					)
							pipe.set_progress_bar_config(disable=__snake_case					)

							_snake_case							:       int												= pipe(image=__snake_case			,    mask_image=mask_image[:, 0]			,    **__snake_case					)
							_snake_case							:       Optional[int]												= output.images

							_snake_case							:       Any												= image.cpu().permute(0			,    2			,    3			,    1					)[0]
							_snake_case							:       List[str]												= mask_image.cpu().permute(0			,    2			,    3			,    1					)[0]

							_snake_case							:       Any												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					)
							_snake_case							:       int												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					)

							_snake_case							:       Optional[int]												= pipe(**self.get_dummy_inputs()					)
							_snake_case							:       Dict												= output.images

							assert out_a.shape == (1, 6_4, 6_4, 3)
							assert np.abs(out_a.flatten() - out_a.flatten()					).max() < 5E-2







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


							'''simple docstring'''

							super().test_inference_batch_single_identical(expected_max_diff=3E-3					)






@slow
@require_torch_gpu
class 							_a(     unittest.TestCase     ):







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


							'''simple docstring'''

							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()







		def 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Tuple												= load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/dog_in_bucket.png"					)
							_snake_case							:       Optional[Any]												= load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/mask.png"					)
							_snake_case							:       Optional[int]												= load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/panda.jpg"					)

							_snake_case							:       Any												= PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"					)
							_snake_case							:       Any												= pipe.to(__snake_case					)
							pipe.set_progress_bar_config(disable=__snake_case					)

							_snake_case							:       Union[str, Any]												= torch.manual_seed(3_2_1					)
							_snake_case							:       int												= pipe(
							    image=__snake_case			,    mask_image=__snake_case			,    example_image=__snake_case			,    generator=__snake_case			,    guidance_scale=5.0			,    num_inference_steps=5_0			,    output_type="np"			,    )

							_snake_case							:       str												= output.images

							_snake_case							:       Optional[int]												= image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							_snake_case							:       int												= np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2