




import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
 from tokenizers import Tokenizer
 from tokenizers.models import WordPiece







class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   def 							lowerCAmelCase_    (					self      :			Dict						,   __lowerCamelCase      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   list(tokenizer_class.max_model_input_sizes.keys()					)
      for model_name in sa_models[:1]:
         _SCREAMING_SNAKE_CASE =   tokenizer_class.from_pretrained(__lowerCamelCase					)
         self.assertIsNotNone(__lowerCamelCase					)
         self.assertIsInstance(__lowerCamelCase						,   __lowerCamelCase					)
         self.assertIsInstance(__lowerCamelCase						,   __lowerCamelCase					)

         for special_tok in tokenizer.all_special_tokens:
            self.assertIsInstance(__lowerCamelCase						,   __lowerCamelCase					)
            _SCREAMING_SNAKE_CASE =   tokenizer.convert_tokens_to_ids(__lowerCamelCase					)
            self.assertIsInstance(__lowerCamelCase						,   __lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			BatchEncoding						,   __lowerCamelCase      :			Optional[Callable] = None					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   pickle.dumps(__lowerCamelCase					)
      self.assertIsNotNone(__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   pickle.loads(__lowerCamelCase					)

      # Ensure is_fast is correctly restored
      self.assertEqual(be_restored.is_fast						,   be_original.is_fast					)

      # Ensure encodings are potentially correctly restored
      if be_original.is_fast:
         self.assertIsNotNone(be_restored.encodings					)
      else:
         self.assertIsNone(be_restored.encodings					)

      # Ensure the keys are the same
      for original_v, restored_v in zip(be_original.values()						,   be_restored.values()					):
         if equal_op:
            self.assertTrue(equal_op(__lowerCamelCase						,   __lowerCamelCase					)					)
         else:
            self.assertEqual(__lowerCamelCase						,   __lowerCamelCase					)



   @slow
   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      self.check_tokenizer_from_pretrained(__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      self.assertEqual(TensorType("tf"					)						,   TensorType.TENSORFLOW					)
      self.assertEqual(TensorType("pt"					)						,   TensorType.PYTORCH					)
      self.assertEqual(TensorType("np"					)						,   TensorType.NUMPY					)



   @require_tokenizers
   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      import numpy as np

      _SCREAMING_SNAKE_CASE =   BertTokenizer.from_pretrained("bert-base-cased"					)
      _SCREAMING_SNAKE_CASE =   BertTokenizerFast.from_pretrained("bert-base-cased"					)

      # Python no tensor
      with self.subTest("BatchEncoding (Python, return_tensors=None)"					):
         self.assert_dump_and_restore(tokenizer_p("Small example to encode"					)					)

      with self.subTest("BatchEncoding (Python, return_tensors=NUMPY)"					):
         self.assert_dump_and_restore(
             tokenizer_p("Small example to encode"						,   return_tensors=TensorType.NUMPY					)						,   np.array_equal					)

      with self.subTest("BatchEncoding (Rust, return_tensors=None)"					):
         self.assert_dump_and_restore(tokenizer_r("Small example to encode"					)					)

      with self.subTest("BatchEncoding (Rust, return_tensors=NUMPY)"					):
         self.assert_dump_and_restore(
             tokenizer_r("Small example to encode"						,   return_tensors=TensorType.NUMPY					)						,   np.array_equal					)



   @require_tf
   @require_tokenizers
   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      import tensorflow as tf

      def tf_array_equals(__lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			List[Any]					):
         return tf.reduce_all(tf.equal(__lowerCamelCase						,   __lowerCamelCase					)					)

      _SCREAMING_SNAKE_CASE =   BertTokenizer.from_pretrained("bert-base-cased"					)
      _SCREAMING_SNAKE_CASE =   BertTokenizerFast.from_pretrained("bert-base-cased"					)

      with self.subTest("BatchEncoding (Python, return_tensors=TENSORFLOW)"					):
         self.assert_dump_and_restore(
             tokenizer_p("Small example to encode"						,   return_tensors=TensorType.TENSORFLOW					)						,   __lowerCamelCase					)

      with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"					):
         self.assert_dump_and_restore(
             tokenizer_r("Small example to encode"						,   return_tensors=TensorType.TENSORFLOW					)						,   __lowerCamelCase					)



   @require_torch
   @require_tokenizers
   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      import torch

      _SCREAMING_SNAKE_CASE =   BertTokenizer.from_pretrained("bert-base-cased"					)
      _SCREAMING_SNAKE_CASE =   BertTokenizerFast.from_pretrained("bert-base-cased"					)

      with self.subTest("BatchEncoding (Python, return_tensors=PYTORCH)"					):
         self.assert_dump_and_restore(
             tokenizer_p("Small example to encode"						,   return_tensors=TensorType.PYTORCH					)						,   torch.equal					)

      with self.subTest("BatchEncoding (Rust, return_tensors=PYTORCH)"					):
         self.assert_dump_and_restore(
             tokenizer_r("Small example to encode"						,   return_tensors=TensorType.PYTORCH					)						,   torch.equal					)



   @require_tokenizers
   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   BertTokenizer.from_pretrained("bert-base-cased"					)
      _SCREAMING_SNAKE_CASE =   BertTokenizerFast.from_pretrained("bert-base-cased"					)

      with self.subTest("Python Tokenizer"					):
         self.assertFalse(tokenizer_p("Small example to_encode"					).is_fast					)

      with self.subTest("Rust Tokenizer"					):
         self.assertTrue(tokenizer_r("Small example to_encode"					).is_fast					)



   @require_tokenizers
   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   BertTokenizerFast.from_pretrained("bert-base-cased"					)
      _SCREAMING_SNAKE_CASE =   tokenizer_r(["Test", "\xad", "test"]						,   is_split_into_words=__lowerCamelCase					)

      self.assertEqual(encoded.word_to_tokens(0					)						,   TokenSpan(start=1						,   end=2					)					)
      self.assertEqual(encoded.word_to_tokens(1					)						,   __lowerCamelCase					)
      self.assertEqual(encoded.word_to_tokens(2					)						,   TokenSpan(start=2						,   end=3					)					)



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
      _SCREAMING_SNAKE_CASE =   batch.convert_to_tensors(tensor_type="np"					)
      self.assertEqual(tensor_batch["inputs"].shape						,   (2, 3)					)
      self.assertEqual(tensor_batch["labels"].shape						,   (2,)					)
      # test converting the converted
      with CaptureStderr() as cs:
         _SCREAMING_SNAKE_CASE =   batch.convert_to_tensors(tensor_type="np"					)
      self.assertFalse(len(cs.err					)						,   msg=F"""should have no warning, but got {cs.err}"""					)

      _SCREAMING_SNAKE_CASE =   BatchEncoding({"inputs": [1, 2, 3], "labels": 0}					)
      _SCREAMING_SNAKE_CASE =   batch.convert_to_tensors(tensor_type="np"						,   prepend_batch_axis=__lowerCamelCase					)
      self.assertEqual(tensor_batch["inputs"].shape						,   (1, 3)					)
      self.assertEqual(tensor_batch["labels"].shape						,   (1,)					)



   @require_torch
   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
      _SCREAMING_SNAKE_CASE =   batch.convert_to_tensors(tensor_type="pt"					)
      self.assertEqual(tensor_batch["inputs"].shape						,   (2, 3)					)
      self.assertEqual(tensor_batch["labels"].shape						,   (2,)					)
      # test converting the converted
      with CaptureStderr() as cs:
         _SCREAMING_SNAKE_CASE =   batch.convert_to_tensors(tensor_type="pt"					)
      self.assertFalse(len(cs.err					)						,   msg=F"""should have no warning, but got {cs.err}"""					)

      _SCREAMING_SNAKE_CASE =   BatchEncoding({"inputs": [1, 2, 3], "labels": 0}					)
      _SCREAMING_SNAKE_CASE =   batch.convert_to_tensors(tensor_type="pt"						,   prepend_batch_axis=__lowerCamelCase					)
      self.assertEqual(tensor_batch["inputs"].shape						,   (1, 3)					)
      self.assertEqual(tensor_batch["labels"].shape						,   (1,)					)



   @require_tf
   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
      _SCREAMING_SNAKE_CASE =   batch.convert_to_tensors(tensor_type="tf"					)
      self.assertEqual(tensor_batch["inputs"].shape						,   (2, 3)					)
      self.assertEqual(tensor_batch["labels"].shape						,   (2,)					)
      # test converting the converted
      with CaptureStderr() as cs:
         _SCREAMING_SNAKE_CASE =   batch.convert_to_tensors(tensor_type="tf"					)
      self.assertFalse(len(cs.err					)						,   msg=F"""should have no warning, but got {cs.err}"""					)

      _SCREAMING_SNAKE_CASE =   BatchEncoding({"inputs": [1, 2, 3], "labels": 0}					)
      _SCREAMING_SNAKE_CASE =   batch.convert_to_tensors(tensor_type="tf"						,   prepend_batch_axis=__lowerCamelCase					)
      self.assertEqual(tensor_batch["inputs"].shape						,   (1, 3)					)
      self.assertEqual(tensor_batch["labels"].shape						,   (1,)					)



   @require_flax
   def 							lowerCAmelCase_    (					self      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
      _SCREAMING_SNAKE_CASE =   batch.convert_to_tensors(tensor_type="jax"					)
      self.assertEqual(tensor_batch["inputs"].shape						,   (2, 3)					)
      self.assertEqual(tensor_batch["labels"].shape						,   (2,)					)
      # test converting the converted
      with CaptureStderr() as cs:
         _SCREAMING_SNAKE_CASE =   batch.convert_to_tensors(tensor_type="jax"					)
      self.assertFalse(len(cs.err					)						,   msg=F"""should have no warning, but got {cs.err}"""					)

      _SCREAMING_SNAKE_CASE =   BatchEncoding({"inputs": [1, 2, 3], "labels": 0}					)
      _SCREAMING_SNAKE_CASE =   batch.convert_to_tensors(tensor_type="jax"						,   prepend_batch_axis=__lowerCamelCase					)
      self.assertEqual(tensor_batch["inputs"].shape						,   (1, 3)					)
      self.assertEqual(tensor_batch["labels"].shape						,   (1,)					)



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   [{"input_ids": np.array([0, 1, 2]					)}, {"input_ids": np.array([0, 1, 2, 3]					)}]
      _SCREAMING_SNAKE_CASE =   BertTokenizer.from_pretrained("bert-base-cased"					)

      _SCREAMING_SNAKE_CASE =   tokenizer.pad(__lowerCamelCase						,   padding=__lowerCamelCase					)
      self.assertTrue(isinstance(batch["input_ids"]						,   np.ndarray					)					)
      self.assertEqual(batch["input_ids"].tolist()						,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)
      _SCREAMING_SNAKE_CASE =   tokenizer.pad(__lowerCamelCase						,   padding=__lowerCamelCase						,   return_tensors="np"					)
      self.assertTrue(isinstance(batch["input_ids"]						,   np.ndarray					)					)
      self.assertEqual(batch["input_ids"].tolist()						,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)



   @require_torch
   def 							lowerCAmelCase_    (					self      :			str					):

      """simple docstring"""
      import torch

      _SCREAMING_SNAKE_CASE =   [{"input_ids": torch.tensor([0, 1, 2]					)}, {"input_ids": torch.tensor([0, 1, 2, 3]					)}]
      _SCREAMING_SNAKE_CASE =   BertTokenizer.from_pretrained("bert-base-cased"					)

      _SCREAMING_SNAKE_CASE =   tokenizer.pad(__lowerCamelCase						,   padding=__lowerCamelCase					)
      self.assertTrue(isinstance(batch["input_ids"]						,   torch.Tensor					)					)
      self.assertEqual(batch["input_ids"].tolist()						,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)
      _SCREAMING_SNAKE_CASE =   tokenizer.pad(__lowerCamelCase						,   padding=__lowerCamelCase						,   return_tensors="pt"					)
      self.assertTrue(isinstance(batch["input_ids"]						,   torch.Tensor					)					)
      self.assertEqual(batch["input_ids"].tolist()						,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)



   @require_tf
   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      import tensorflow as tf

      _SCREAMING_SNAKE_CASE =   [{"input_ids": tf.constant([0, 1, 2]					)}, {"input_ids": tf.constant([0, 1, 2, 3]					)}]
      _SCREAMING_SNAKE_CASE =   BertTokenizer.from_pretrained("bert-base-cased"					)

      _SCREAMING_SNAKE_CASE =   tokenizer.pad(__lowerCamelCase						,   padding=__lowerCamelCase					)
      self.assertTrue(isinstance(batch["input_ids"]						,   tf.Tensor					)					)
      self.assertEqual(batch["input_ids"].numpy().tolist()						,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)
      _SCREAMING_SNAKE_CASE =   tokenizer.pad(__lowerCamelCase						,   padding=__lowerCamelCase						,   return_tensors="tf"					)
      self.assertTrue(isinstance(batch["input_ids"]						,   tf.Tensor					)					)
      self.assertEqual(batch["input_ids"].numpy().tolist()						,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)



   @require_tokenizers
   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   Tokenizer(WordPiece(unk_token="[UNK]"					)					)
      PreTrainedTokenizerFast(tokenizer_object=__lowerCamelCase					)



   @require_tokenizers
   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   Tokenizer(WordPiece(unk_token="[UNK]"					)					)
      with tempfile.TemporaryDirectory() as tmpdirname:
         bert_tokenizer.save(os.path.join(__lowerCamelCase						,   "tokenizer.json"					)					)
         PreTrainedTokenizerFast(tokenizer_file=os.path.join(__lowerCamelCase						,   "tokenizer.json"					)					)







'''simple docstring'''



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 	lowercase_					(				A  ,						unittest.TestCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		PaintByExamplePipeline
   lowerCamelCase_       						=		IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   lowerCamelCase_       						=		IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   lowerCamelCase_       						=		frozenset([]     )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      torch.manual_seed(0					)
      _SCREAMING_SNAKE_CASE =   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						,   )
      _SCREAMING_SNAKE_CASE =   PNDMScheduler(skip_prk_steps=__lowerCamelCase					)
      torch.manual_seed(0					)
      _SCREAMING_SNAKE_CASE =   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					)
      _SCREAMING_SNAKE_CASE =   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						,   )
      _SCREAMING_SNAKE_CASE =   PaintByExampleImageEncoder(__lowerCamelCase						,   proj_size=3_2					)
      _SCREAMING_SNAKE_CASE =   CLIPImageProcessor(crop_size=3_2						,   size=3_2					)

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



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   np.array(image.convert("RGB"					)					)
      _SCREAMING_SNAKE_CASE =   image[None].transpose(0						,   3						,   1						,   2					)
      _SCREAMING_SNAKE_CASE =   torch.from_numpy(__lowerCamelCase					).to(dtype=torch.floataa					) / 1_2_7.5 - 1.0
      return image



   def 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			Optional[int]="cpu"						,   __lowerCamelCase      :			Union[str, Any]=0					):

      """simple docstring"""
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      _SCREAMING_SNAKE_CASE =   floats_tensor((1, 3, 3_2, 3_2)						,   rng=random.Random(__lowerCamelCase					)					).to(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   image.cpu().permute(0						,   2						,   3						,   1					)[0]
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					).resize((6_4, 6_4)					)
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(image + 4					)					).convert("RGB"					).resize((6_4, 6_4)					)
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					).resize((3_2, 3_2)					)

      if str(__lowerCamelCase					).startswith("mps"					):
         _SCREAMING_SNAKE_CASE =   torch.manual_seed(__lowerCamelCase					)
      else:
         _SCREAMING_SNAKE_CASE =   torch.Generator(device=__lowerCamelCase					).manual_seed(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   {
          "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 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.get_dummy_components()

      # make sure here that pndm scheduler skips prk
      _SCREAMING_SNAKE_CASE =   PaintByExamplePipeline(**__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   pipe.to("cpu"					)
      pipe.set_progress_bar_config(disable=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   self.get_dummy_inputs()
      _SCREAMING_SNAKE_CASE =   pipe(**__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   output.images

      _SCREAMING_SNAKE_CASE =   image[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)
      _SCREAMING_SNAKE_CASE =   np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]					)

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



   def 							lowerCAmelCase_    (					self      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "cpu"
      _SCREAMING_SNAKE_CASE =   self.get_dummy_inputs()
      inputs.pop("mask_image"					)
      _SCREAMING_SNAKE_CASE =   self.convert_to_pt(inputs.pop("image"					)					)
      _SCREAMING_SNAKE_CASE =   image.clamp(0						,   1					) / 2

      # make sure here that pndm scheduler skips prk
      _SCREAMING_SNAKE_CASE =   PaintByExamplePipeline(**self.get_dummy_components()					)
      _SCREAMING_SNAKE_CASE =   pipe.to(__lowerCamelCase					)
      pipe.set_progress_bar_config(disable=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   pipe(image=__lowerCamelCase						,   mask_image=mask_image[:, 0]						,   **__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   output.images

      _SCREAMING_SNAKE_CASE =   image.cpu().permute(0						,   2						,   3						,   1					)[0]
      _SCREAMING_SNAKE_CASE =   mask_image.cpu().permute(0						,   2						,   3						,   1					)[0]

      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					)
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					)

      _SCREAMING_SNAKE_CASE =   pipe(**self.get_dummy_inputs()					)
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      super().test_inference_batch_single_identical(expected_max_diff=3e-3					)







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


   """simple docstring"""

   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      # make sure here that pndm scheduler skips prk
      _SCREAMING_SNAKE_CASE =   load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/paint_by_example/dog_in_bucket.png"					)
      _SCREAMING_SNAKE_CASE =   load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/paint_by_example/mask.png"					)
      _SCREAMING_SNAKE_CASE =   load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/paint_by_example/panda.jpg"					)

      _SCREAMING_SNAKE_CASE =   PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"					)
      _SCREAMING_SNAKE_CASE =   pipe.to(__lowerCamelCase					)
      pipe.set_progress_bar_config(disable=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   torch.manual_seed(3_2_1					)
      _SCREAMING_SNAKE_CASE =   pipe(
          image=__lowerCamelCase						,   mask_image=__lowerCamelCase						,   example_image=__lowerCamelCase						,   generator=__lowerCamelCase						,   guidance_scale=5.0						,   num_inference_steps=5_0						,   output_type="np"						,   )

      _SCREAMING_SNAKE_CASE =   output.images

      _SCREAMING_SNAKE_CASE =   image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      _SCREAMING_SNAKE_CASE =   np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]					)

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

