






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  lowerCamelCase_   (		__lowercase       ,  unittest.TestCase   ):
							SCREAMING_SNAKE_CASE_     =	PaintByExamplePipeline
							SCREAMING_SNAKE_CASE_     =	IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
							SCREAMING_SNAKE_CASE_     =	IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							SCREAMING_SNAKE_CASE_     =	frozenset([]   )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
							def        SCREAMING_SNAKE_CASE_       (  self						:    Optional[Any]    ):





								'''simple docstring'''



								torch.manual_seed(0    )
								a				=	UNetaDConditionModel(
								    block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=9							,out_channels=4							,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')							,cross_attention_dim=32							,)
								a				=	PNDMScheduler(skip_prk_steps=__lowerCamelCase    )
								torch.manual_seed(0    )
								a				=	AutoencoderKL(
								    block_out_channels=[32, 64]							,in_channels=3							,out_channels=3							,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']							,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']							,latent_channels=4							,)
								torch.manual_seed(0    )
								a				=	CLIPVisionConfig(
								    hidden_size=32							,projection_dim=32							,intermediate_size=37							,layer_norm_eps=1e-05							,num_attention_heads=4							,num_hidden_layers=5							,image_size=32							,patch_size=4							,)
								a				=	PaintByExampleImageEncoder(__lowerCamelCase							,proj_size=32    )
								a				=	CLIPImageProcessor(crop_size=32							,size=32    )

								a				=	{
								    "unet": unet,
								    "scheduler": scheduler,
								    "vae": vae,
								    "image_encoder": image_encoder,
								    "safety_checker": None,
								    "feature_extractor": feature_extractor,
								}
								return components
							def        SCREAMING_SNAKE_CASE_       (  self						:    Union[str, Any]							,__lowerCamelCase						:    Any    ):





								'''simple docstring'''



								a				=	np.array(image.convert('''RGB'''    )    )
								a				=	image[None].transpose(0							,3							,1							,2    )
								a				=	torch.from_numpy(__lowerCamelCase    ).to(dtype=torch.floataa    ) / 127.5 - 1.0
								return image
							def        SCREAMING_SNAKE_CASE_       (  self						:    int							,__lowerCamelCase						:    List[Any]="cpu"							,__lowerCamelCase						:    Union[str, Any]=0    ):





								'''simple docstring'''



								a				=	floats_tensor((1, 3, 32, 32)							,rng=random.Random(__lowerCamelCase    )    ).to(__lowerCamelCase    )
								a				=	image.cpu().permute(0							,2							,3							,1    )[0]
								a				=	Image.fromarray(np.uinta(__lowerCamelCase    )    ).convert('''RGB'''    ).resize((64, 64)    )
								a				=	Image.fromarray(np.uinta(image + 4    )    ).convert('''RGB'''    ).resize((64, 64)    )
								a				=	Image.fromarray(np.uinta(__lowerCamelCase    )    ).convert('''RGB'''    ).resize((32, 32)    )

								if str(__lowerCamelCase    ).startswith('''mps'''    ):
									a				=	torch.manual_seed(__lowerCamelCase    )
								else:
									a				=	torch.Generator(device=__lowerCamelCase    ).manual_seed(__lowerCamelCase    )
								a				=	{
								    "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        SCREAMING_SNAKE_CASE_       (  self						:    Tuple    ):





								'''simple docstring'''



								a				=	self.get_dummy_components()

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

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

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

								assert image.shape == (1, 64, 64, 3)
								a				=	np.array([0.4_686, 0.5_687, 0.4_007, 0.5_218, 0.5_741, 0.4_482, 0.4_940, 0.4_629, 0.4_503]    )

								assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
							def        SCREAMING_SNAKE_CASE_       (  self						:    str    ):





								'''simple docstring'''



								a				=	"cpu"
								a				=	self.get_dummy_inputs()
								inputs.pop('''mask_image'''    )
								a				=	self.convert_to_pt(inputs.pop('''image'''    )    )
								a				=	image.clamp(0							,1    ) / 2

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

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

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

								a				=	Image.fromarray(np.uinta(__lowerCamelCase    )    ).convert('''RGB'''    )
								a				=	Image.fromarray(np.uinta(__lowerCamelCase    )    ).convert('''RGB'''    )

								a				=	pipe(**self.get_dummy_inputs()    )
								a				=	output.images

								assert out_a.shape == (1, 64, 64, 3)
								assert np.abs(out_a.flatten() - out_a.flatten()    ).max() < 5e-2




							def        SCREAMING_SNAKE_CASE_       (  self						:    Optional[Any]    ):





								'''simple docstring'''



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







@slow
@require_torch_gpu
class  lowerCamelCase_   (		unittest.TestCase   ):
							def        SCREAMING_SNAKE_CASE_       (  self						:    Optional[int]    ):





								'''simple docstring'''



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




							def        SCREAMING_SNAKE_CASE_       (  self						:    Tuple    ):





								'''simple docstring'''



								a				=	load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/paint_by_example/dog_in_bucket.png'''    )
								a				=	load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/paint_by_example/mask.png'''    )
								a				=	load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/paint_by_example/panda.jpg'''    )

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

								a				=	torch.manual_seed(3_21    )
								a				=	pipe(
								    image=__lowerCamelCase							,mask_image=__lowerCamelCase							,example_image=__lowerCamelCase							,generator=__lowerCamelCase							,guidance_scale=5.0							,num_inference_steps=50							,output_type='''np'''							,)

								a				=	output.images

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

								assert image.shape == (1, 5_12, 5_12, 3)
								a				=	np.array([0.4_834, 0.4_811, 0.4_874, 0.5_122, 0.5_081, 0.5_144, 0.5_291, 0.5_290, 0.5_374]    )

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












# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       @classmethod
       def 			UpperCamelCase_	(					cls							)	->   Tuple:
            return cls







       @slow
       @require_torch_gpu
       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.get_auto_remove_tmp_dir()
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							F"""
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        """.split()

            # we want this test to run even if there is only one GPU, but if there are more we use them all
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							get_gpu_count()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							F"""
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        """.split()
            _SCREAMING_SNAKE_CASE				:		int  			=							[sys.executable] + distributed_args + args
            execute_subprocess_async(__lowerCamelCase			,  env=self.get_env()							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							os.path.join(__lowerCamelCase			,  "test_bleu.json"							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							load_json(__lowerCamelCase							)
            # print(metrics)
            self.assertGreaterEqual(metrics["bleu"]			,  2_5							)