
'''simple docstring'''

import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
     from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
     UpperCamelCase_            =    None

if is_ray_available():
     import ray  # noqa: E402 # isort:skip
     from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
     UpperCamelCase_            =    None
     UpperCamelCase_            =    None
     UpperCamelCase_            =    None


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[int]						)      ->  Optional[int]:
  if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
    _lowerCAmelCase    :					List[str]							=   unittest.skip("""test requires Datasets, Faiss, psutil"""						)(_lowerCamelCase						)
  return test_case



@require_distributed_retrieval
class 					a_					(_a      ):




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Optional[Any]							=   tempfile.mkdtemp()
         _lowerCAmelCase    :					List[str]							=   8

         # DPR tok
         _lowerCAmelCase    :					List[str]							=   [
             """[UNK]""",
             """[CLS]""",
             """[SEP]""",
             """[PAD]""",
             """[MASK]""",
             """want""",
             """##want""",
             """##ed""",
             """wa""",
             """un""",
             """runn""",
             """##ing""",
             """,""",
             """low""",
             """lowest""",
         ]
         _lowerCAmelCase    :					List[Any]							=   os.path.join(self.tmpdirname				,				"""dpr_tokenizer"""					)
         os.makedirs(snake_case_				,				exist_ok=snake_case_					)
         _lowerCAmelCase    :					Dict							=   os.path.join(snake_case_				,				DPR_VOCAB_FILES_NAMES["""vocab_file"""]					)
         with open(self.vocab_file				,				"""w"""				,				encoding="""utf-8"""					) as vocab_writer:
           vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)

         # BART tok
         _lowerCAmelCase    :					List[str]							=   [
             """l""",
             """o""",
             """w""",
             """e""",
             """r""",
             """s""",
             """t""",
             """i""",
             """d""",
             """n""",
             """\u0120""",
             """\u0120l""",
             """\u0120n""",
             """\u0120lo""",
             """\u0120low""",
             """er""",
             """\u0120lowest""",
             """\u0120newer""",
             """\u0120wider""",
             """<unk>""",
         ]
         _lowerCAmelCase    :					Dict							=   dict(zip(snake_case_				,				range(len(snake_case_					)					)					)					)
         _lowerCAmelCase    :					List[str]							=   ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
         _lowerCAmelCase    :					Any							=   {"""unk_token""": """<unk>"""}

         _lowerCAmelCase    :					Any							=   os.path.join(self.tmpdirname				,				"""bart_tokenizer"""					)
         os.makedirs(snake_case_				,				exist_ok=snake_case_					)
         _lowerCAmelCase    :					Union[str, Any]							=   os.path.join(snake_case_				,				BART_VOCAB_FILES_NAMES["""vocab_file"""]					)
         _lowerCAmelCase    :					Optional[int]							=   os.path.join(snake_case_				,				BART_VOCAB_FILES_NAMES["""merges_file"""]					)
         with open(self.vocab_file				,				"""w"""				,				encoding="""utf-8"""					) as fp:
           fp.write(json.dumps(snake_case_					) + """\n"""					)
         with open(self.merges_file				,				"""w"""				,				encoding="""utf-8"""					) as fp:
           fp.write("""\n""".join(snake_case_					)					)




       def 							__UpperCamelCase    (       self					):
         return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,				"""dpr_tokenizer"""					)					)




       def 							__UpperCamelCase    (       self					):
         return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname				,				"""bart_tokenizer"""					)					)




       def 							__UpperCamelCase    (       self					):
         shutil.rmtree(self.tmpdirname					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					List[str]							=   Dataset.from_dict(
             {
                 """id""": ["""0""", """1"""],
                 """text""": ["""foo""", """bar"""],
                 """title""": ["""Foo""", """Bar"""],
                 """embeddings""": [np.ones(self.retrieval_vector_size					), 2 * np.ones(self.retrieval_vector_size					)],
             }					)
         dataset.add_faiss_index("""embeddings"""				,				string_factory="""Flat"""				,				metric_type=faiss.METRIC_INNER_PRODUCT					)
         return dataset




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_=1_2_3_4_5					):
         _lowerCAmelCase    :					Dict							=   self.get_dummy_dataset()
         _lowerCAmelCase    :					str							=   RagConfig(
             retrieval_vector_size=self.retrieval_vector_size				,				question_encoder=DPRConfig().to_dict()				,				generator=BartConfig().to_dict()				,				)
         with patch("""transformers.models.rag.retrieval_rag.load_dataset"""					) as mock_load_dataset:
           _lowerCAmelCase    :					int							=   dataset
           _lowerCAmelCase    :					List[Any]							=   RagPyTorchDistributedRetriever(
               snake_case_				,				question_encoder_tokenizer=self.get_dpr_tokenizer()				,				generator_tokenizer=self.get_bart_tokenizer()				,				)
           if init_retrieval:
             retriever.init_retrieval(snake_case_					)
         return retriever




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         # Have to run in local mode because sys.path modifications at top of
         # file are not propogated to remote workers.
         # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
         ray.init(local_mode=snake_case_					)
         _lowerCAmelCase    :					List[Any]							=   RagConfig(
             retrieval_vector_size=self.retrieval_vector_size				,				question_encoder=DPRConfig().to_dict()				,				generator=BartConfig().to_dict()				,				)
         _lowerCAmelCase    :					Union[str, Any]							=   ray.remote(snake_case_					)
         _lowerCAmelCase    :					Union[str, Any]							=   [remote_cls.remote() for _ in range(1					)]
         with patch("""transformers.models.rag.retrieval_rag.load_dataset"""					) as mock_load_dataset:
           _lowerCAmelCase    :					List[Any]							=   self.get_dummy_dataset()
           _lowerCAmelCase    :					Union[str, Any]							=   RagRayDistributedRetriever(
               snake_case_				,				question_encoder_tokenizer=self.get_dpr_tokenizer()				,				generator_tokenizer=self.get_bart_tokenizer()				,				retrieval_workers=snake_case_				,				)
           if init_retrieval:
             retriever.init_retrieval()
         return retriever




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_=1_2_3_4_5					):
         _lowerCAmelCase    :					str							=   self.get_dummy_dataset()
         _lowerCAmelCase    :					Optional[int]							=   RagConfig(
             retrieval_vector_size=self.retrieval_vector_size				,				question_encoder=DPRConfig().to_dict()				,				generator=BartConfig().to_dict()				,				index_name="""custom"""				,				)
         if from_disk:
           _lowerCAmelCase    :					int							=   os.path.join(self.tmpdirname				,				"""dataset"""					)
           _lowerCAmelCase    :					Union[str, Any]							=   os.path.join(self.tmpdirname				,				"""index.faiss"""					)
           dataset.get_index("""embeddings"""					).save(os.path.join(self.tmpdirname				,				"""index.faiss"""					)					)
           dataset.drop_index("""embeddings"""					)
           dataset.save_to_disk(os.path.join(self.tmpdirname				,				"""dataset"""					)					)
           del dataset
           _lowerCAmelCase    :					Tuple							=   RagPyTorchDistributedRetriever(
               snake_case_				,				question_encoder_tokenizer=self.get_dpr_tokenizer()				,				generator_tokenizer=self.get_bart_tokenizer()				,				)
         else:
           _lowerCAmelCase    :					Optional[int]							=   RagPyTorchDistributedRetriever(
               snake_case_				,				question_encoder_tokenizer=self.get_dpr_tokenizer()				,				generator_tokenizer=self.get_bart_tokenizer()				,				index=CustomHFIndex(config.retrieval_vector_size				,				snake_case_					)				,				)
         if init_retrieval:
           retriever.init_retrieval(snake_case_					)
         return retriever




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_					):
         # Have to run in local mode because sys.path modifications at top of
         # file are not propogated to remote workers.
         # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
         ray.init(local_mode=snake_case_					)
         _lowerCAmelCase    :					Optional[Any]							=   self.get_dummy_dataset()
         _lowerCAmelCase    :					str							=   RagConfig(
             retrieval_vector_size=self.retrieval_vector_size				,				question_encoder=DPRConfig().to_dict()				,				generator=BartConfig().to_dict()				,				index_name="""custom"""				,				)
         _lowerCAmelCase    :					List[Any]							=   ray.remote(snake_case_					)
         _lowerCAmelCase    :					Union[str, Any]							=   [remote_cls.remote() for _ in range(1					)]
         if from_disk:
           _lowerCAmelCase    :					str							=   os.path.join(self.tmpdirname				,				"""dataset"""					)
           _lowerCAmelCase    :					Tuple							=   os.path.join(self.tmpdirname				,				"""index.faiss"""					)
           dataset.get_index("""embeddings"""					).save(os.path.join(self.tmpdirname				,				"""index.faiss"""					)					)
           dataset.drop_index("""embeddings"""					)
           dataset.save_to_disk(os.path.join(self.tmpdirname				,				"""dataset"""					)					)
           del dataset
           _lowerCAmelCase    :					int							=   RagRayDistributedRetriever(
               snake_case_				,				question_encoder_tokenizer=self.get_dpr_tokenizer()				,				generator_tokenizer=self.get_bart_tokenizer()				,				retrieval_workers=snake_case_				,				index=CustomHFIndex.load_from_disk(
                   vector_size=config.retrieval_vector_size				,				dataset_path=config.passages_path				,				index_path=config.index_path				,				)				,				)
         else:
           _lowerCAmelCase    :					int							=   RagRayDistributedRetriever(
               snake_case_				,				question_encoder_tokenizer=self.get_dpr_tokenizer()				,				generator_tokenizer=self.get_bart_tokenizer()				,				retrieval_workers=snake_case_				,				index=CustomHFIndex(config.retrieval_vector_size				,				snake_case_					)				,				)
         if init_retrieval:
           retriever.init_retrieval()
         return retriever




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_					):
         _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase    :					Optional[Any]							=   retriever.retrieve(snake_case_				,				n_docs=snake_case_					)
         self.assertEqual(retrieved_doc_embeds.shape				,				(2, n_docs, self.retrieval_vector_size)					)
         self.assertEqual(len(snake_case_					)				,				2					)
         self.assertEqual(sorted(doc_dicts[0]					)				,				["""embeddings""", """id""", """text""", """title"""]					)
         self.assertEqual(len(doc_dicts[0]["""id"""]					)				,				snake_case_					)
         self.assertEqual(doc_dicts[0]["""id"""][0]				,				"""1"""					)  # max inner product is reached with second doc
         self.assertEqual(doc_dicts[1]["""id"""][0]				,				"""0"""					)  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist()				,				[[1], [0]]					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Any							=   1
         _lowerCAmelCase    :					Tuple							=   np.array(
             [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,				dtype=np.floataa					)

         self.distributed_retriever_check(
             self.get_dummy_pytorch_distributed_retriever(init_retrieval=snake_case_					)				,				snake_case_				,				snake_case_					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Tuple							=   1
         _lowerCAmelCase    :					Optional[Any]							=   np.array(
             [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,				dtype=np.floataa					)

         self.distributed_retriever_check(
             self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=snake_case_				,				from_disk=snake_case_					)				,				snake_case_				,				snake_case_				,				)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Optional[int]							=   1
         _lowerCAmelCase    :					Optional[int]							=   np.array(
             [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,				dtype=np.floataa					)

         self.distributed_retriever_check(
             self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=snake_case_				,				from_disk=snake_case_					)				,				snake_case_				,				snake_case_				,				)




       @require_ray
       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Tuple							=   1
         _lowerCAmelCase    :					Any							=   np.array(
             [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,				dtype=np.floataa					)

         self.distributed_retriever_check(
             self.get_dummy_ray_distributed_retriever(init_retrieval=snake_case_					)				,				snake_case_				,				snake_case_					)
         ray.shutdown()




       @require_ray
       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Union[str, Any]							=   1
         _lowerCAmelCase    :					Dict							=   np.array(
             [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,				dtype=np.floataa					)
         with self.assertRaises(snake_case_					):
           self.distributed_retriever_check(
               self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case_				,				from_disk=snake_case_					)				,				snake_case_				,				snake_case_				,				)
         ray.shutdown()







       @require_ray
       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Any							=   1
         _lowerCAmelCase    :					Optional[Any]							=   np.array(
             [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,				dtype=np.floataa					)

         self.distributed_retriever_check(
             self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case_				,				from_disk=snake_case_					)				,				snake_case_				,				snake_case_					)
         ray.shutdown()




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




       def 							__UpperCamelCase    (       self					):
         torch.manual_seed(0					)
         _lowerCAmelCase    :					Dict							=   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				,				)
         _lowerCAmelCase    :					List[Any]							=   PNDMScheduler(skip_prk_steps=snake_case_					)
         torch.manual_seed(0					)
         _lowerCAmelCase    :					List[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					)
         _lowerCAmelCase    :					List[str]							=   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				,				)
         _lowerCAmelCase    :					Optional[int]							=   PaintByExampleImageEncoder(snake_case_				,				proj_size=3_2					)
         _lowerCAmelCase    :					Optional[int]							=   CLIPImageProcessor(crop_size=3_2				,				size=3_2					)

         _lowerCAmelCase    :					Union[str, Any]							=   {
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """image_encoder""": image_encoder,
             """safety_checker""": None,
             """feature_extractor""": feature_extractor,
         }
         return components




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         _lowerCAmelCase    :					Optional[int]							=   np.array(image.convert("""RGB"""					)					)
         _lowerCAmelCase    :					Any							=   image[None].transpose(0				,				3				,				1				,				2					)
         _lowerCAmelCase    :					List[str]							=   torch.from_numpy(snake_case_					).to(dtype=torch.floataa					) / 127.5 - 1.0
         return image




       def 							__UpperCamelCase    (       self				,				snake_case_="cpu"				,				snake_case_=0					):
         # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
         _lowerCAmelCase    :					Tuple							=   floats_tensor((1, 3, 3_2, 3_2)				,				rng=random.Random(snake_case_					)					).to(snake_case_					)
         _lowerCAmelCase    :					List[Any]							=   image.cpu().permute(0				,				2				,				3				,				1					)[0]
         _lowerCAmelCase    :					List[Any]							=   Image.fromarray(np.uinta(snake_case_					)					).convert("""RGB"""					).resize((6_4, 6_4)					)
         _lowerCAmelCase    :					List[str]							=   Image.fromarray(np.uinta(image + 4					)					).convert("""RGB"""					).resize((6_4, 6_4)					)
         _lowerCAmelCase    :					Any							=   Image.fromarray(np.uinta(snake_case_					)					).convert("""RGB"""					).resize((3_2, 3_2)					)

         if str(snake_case_					).startswith("""mps"""					):
           _lowerCAmelCase    :					str							=   torch.manual_seed(snake_case_					)
         else:
           _lowerCAmelCase    :					Tuple							=   torch.Generator(device=snake_case_					).manual_seed(snake_case_					)
         _lowerCAmelCase    :					str							=   {
             """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 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Dict							=   self.get_dummy_components()

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

         _lowerCAmelCase    :					int							=   self.get_dummy_inputs()
         _lowerCAmelCase    :					List[str]							=   pipe(**snake_case_					)
         _lowerCAmelCase    :					Any							=   output.images

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

         assert image.shape == (1, 6_4, 6_4, 3)
         _lowerCAmelCase    :					Optional[Any]							=   np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]					)

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




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Optional[int]							=   """cpu"""
         _lowerCAmelCase    :					int							=   self.get_dummy_inputs()
         inputs.pop("""mask_image"""					)
         _lowerCAmelCase    :					Optional[int]							=   self.convert_to_pt(inputs.pop("""image"""					)					)
         _lowerCAmelCase    :					int							=   image.clamp(0				,				1					) / 2

         # make sure here that pndm scheduler skips prk
         _lowerCAmelCase    :					List[Any]							=   PaintByExamplePipeline(**self.get_dummy_components()					)
         _lowerCAmelCase    :					Optional[int]							=   pipe.to(snake_case_					)
         pipe.set_progress_bar_config(disable=snake_case_					)

         _lowerCAmelCase    :					str							=   pipe(image=snake_case_				,				mask_image=mask_image[:, 0]				,				**snake_case_					)
         _lowerCAmelCase    :					str							=   output.images

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

         _lowerCAmelCase    :					Any							=   Image.fromarray(np.uinta(snake_case_					)					).convert("""RGB"""					)
         _lowerCAmelCase    :					Any							=   Image.fromarray(np.uinta(snake_case_					)					).convert("""RGB"""					)

         _lowerCAmelCase    :					str							=   pipe(**self.get_dummy_inputs()					)
         _lowerCAmelCase    :					int							=   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 							__UpperCamelCase    (       self					):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3					)



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




       def 							__UpperCamelCase    (       self					):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







       def 							__UpperCamelCase    (       self					):
         # make sure here that pndm scheduler skips prk
         _lowerCAmelCase    :					Union[str, Any]							=   load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/paint_by_example/dog_in_bucket.png"""					)
         _lowerCAmelCase    :					List[Any]							=   load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/paint_by_example/mask.png"""					)
         _lowerCAmelCase    :					List[Any]							=   load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/paint_by_example/panda.jpg"""					)

         _lowerCAmelCase    :					str							=   PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""					)
         _lowerCAmelCase    :					int							=   pipe.to(snake_case_					)
         pipe.set_progress_bar_config(disable=snake_case_					)

         _lowerCAmelCase    :					str							=   torch.manual_seed(3_2_1					)
         _lowerCAmelCase    :					Optional[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"""				,				)

         _lowerCAmelCase    :					List[str]							=   output.images

         _lowerCAmelCase    :					List[str]							=   image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCAmelCase    :					Any							=   np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]					)

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


