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 					_snake_case						(						_lowercase					,      unittest.TestCase				):
					lowerCamelCase__:		Optional[Any]  =     PaintByExamplePipeline
					lowerCamelCase__:		List[Any]  =     IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
					lowerCamelCase__:		str  =     IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					lowerCamelCase__:		List[str]  =     frozenset([]				)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
					def    _lowerCamelCase (  self:				Dict     )    ->				Optional[int]:
												torch.manual_seed(0     )
												__UpperCAmelCase						:			List[str]         =   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	,  )
												__UpperCAmelCase						:			Optional[int]         =   PNDMScheduler(skip_prk_steps=__lowerCamelCase     )
												torch.manual_seed(0     )
												__UpperCAmelCase						:			Optional[Any]         =   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     )
												__UpperCAmelCase						:			Dict         =   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	,  )
												__UpperCAmelCase						:			str         =   PaintByExampleImageEncoder(__lowerCamelCase	,  proj_size=32     )
												__UpperCAmelCase						:			str         =   CLIPImageProcessor(crop_size=32	,  size=32     )

												__UpperCAmelCase						:			Optional[Any]         =   {
												    "unet": unet,
												    "scheduler": scheduler,
												    "vae": vae,
												    "image_encoder": image_encoder,
												    "safety_checker": None,
												    "feature_extractor": feature_extractor,
												}
												return components
					def    _lowerCamelCase (  self:				Dict	,  __lowerCamelCase:				Dict     )    ->				Union[str, Any]:
												__UpperCAmelCase						:			str         =   np.array(image.convert("RGB"     )     )
												__UpperCAmelCase						:			Dict         =   image[None].transpose(0	,  3	,  1	,  2     )
												__UpperCAmelCase						:			Any         =   torch.from_numpy(__lowerCamelCase     ).to(dtype=torch.floataa     ) / 1_27.5 - 1.0
												return image
					def    _lowerCamelCase (  self:				Dict	,  __lowerCamelCase:				List[Any]="cpu"	,  __lowerCamelCase:				Union[str, Any]=0     )    ->				Dict:
												# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
												__UpperCAmelCase						:			Union[str, Any]         =   floats_tensor((1, 3, 32, 32)	,  rng=random.Random(__lowerCamelCase     )     ).to(__lowerCamelCase     )
												__UpperCAmelCase						:			Dict         =   image.cpu().permute(0	,  2	,  3	,  1     )[0]
												__UpperCAmelCase						:			List[str]         =   Image.fromarray(np.uinta(__lowerCamelCase     )     ).convert("RGB"     ).resize((64, 64)     )
												__UpperCAmelCase						:			List[str]         =   Image.fromarray(np.uinta(image + 4     )     ).convert("RGB"     ).resize((64, 64)     )
												__UpperCAmelCase						:			Tuple         =   Image.fromarray(np.uinta(__lowerCamelCase     )     ).convert("RGB"     ).resize((32, 32)     )

												if str(__lowerCamelCase     ).startswith("mps"     ):
																			__UpperCAmelCase						:			Optional[int]         =   torch.manual_seed(__lowerCamelCase     )
												else:
																			__UpperCAmelCase						:			int         =   torch.Generator(device=__lowerCamelCase     ).manual_seed(__lowerCamelCase     )
												__UpperCAmelCase						:			Dict         =   {
												    "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:				Optional[int]     )    ->				Any:
												__UpperCAmelCase						:			str         =   self.get_dummy_components()

												# make sure here that pndm scheduler skips prk
												__UpperCAmelCase						:			Union[str, Any]         =   PaintByExamplePipeline(**__lowerCamelCase     )
												__UpperCAmelCase						:			Optional[int]         =   pipe.to("cpu"     )
												pipe.set_progress_bar_config(disable=__lowerCamelCase     )

												__UpperCAmelCase						:			str         =   self.get_dummy_inputs()
												__UpperCAmelCase						:			Any         =   pipe(**__lowerCamelCase     )
												__UpperCAmelCase						:			Any         =   output.images

												__UpperCAmelCase						:			Tuple         =   image[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)
												__UpperCAmelCase						:			List[str]         =   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    _lowerCamelCase (  self:				Union[str, Any]     )    ->				Dict:
												__UpperCAmelCase						:			Tuple         =   "cpu"
												__UpperCAmelCase						:			Dict         =   self.get_dummy_inputs()
												inputs.pop("mask_image"     )
												__UpperCAmelCase						:			Dict         =   self.convert_to_pt(inputs.pop("image"     )     )
												__UpperCAmelCase						:			List[str]         =   image.clamp(0	,  1     ) / 2

												# make sure here that pndm scheduler skips prk
												__UpperCAmelCase						:			Optional[int]         =   PaintByExamplePipeline(**self.get_dummy_components()     )
												__UpperCAmelCase						:			Dict         =   pipe.to(__lowerCamelCase     )
												pipe.set_progress_bar_config(disable=__lowerCamelCase     )

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

												__UpperCAmelCase						:			int         =   image.cpu().permute(0	,  2	,  3	,  1     )[0]
												__UpperCAmelCase						:			Dict         =   mask_image.cpu().permute(0	,  2	,  3	,  1     )[0]

												__UpperCAmelCase						:			Union[str, Any]         =   Image.fromarray(np.uinta(__lowerCamelCase     )     ).convert("RGB"     )
												__UpperCAmelCase						:			str         =   Image.fromarray(np.uinta(__lowerCamelCase     )     ).convert("RGB"     )

												__UpperCAmelCase						:			Dict         =   pipe(**self.get_dummy_inputs()     )
												__UpperCAmelCase						:			Union[str, Any]         =   output.images

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





					def    _lowerCamelCase (  self:				Any     )    ->				int:
												super().test_inference_batch_single_identical(expected_max_diff=3e-3     )




@slow
@require_torch_gpu
class 					_snake_case						(						unittest.TestCase				):
					def    _lowerCamelCase (  self:				List[str]     )    ->				Tuple:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()





					def    _lowerCamelCase (  self:				List[str]     )    ->				str:
												# make sure here that pndm scheduler skips prk
												__UpperCAmelCase						:			int         =   load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/paint_by_example/dog_in_bucket.png"     )
												__UpperCAmelCase						:			Tuple         =   load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/paint_by_example/mask.png"     )
												__UpperCAmelCase						:			Dict         =   load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/paint_by_example/panda.jpg"     )

												__UpperCAmelCase						:			Union[str, Any]         =   PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"     )
												__UpperCAmelCase						:			Dict         =   pipe.to(__lowerCamelCase     )
												pipe.set_progress_bar_config(disable=__lowerCamelCase     )

												__UpperCAmelCase						:			Optional[Any]         =   torch.manual_seed(3_21     )
												__UpperCAmelCase						:			Optional[Any]         =   pipe(
												    image=__lowerCamelCase	,  mask_image=__lowerCamelCase	,  example_image=__lowerCamelCase	,  generator=__lowerCamelCase	,  guidance_scale=5.0	,  num_inference_steps=50	,  output_type="np"	,  )

												__UpperCAmelCase						:			Optional[Any]         =   output.images

												__UpperCAmelCase						:			Any         =   image[0, -3:, -3:, -1]

												assert image.shape == (1, 5_12, 5_12, 3)
												__UpperCAmelCase						:			Dict         =   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





import io
import json
import subprocess


_snake_case			=						[
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

_snake_case			=						8




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   List[Any]:
							__UpperCAmelCase						:			int         =   {}
							for src, tgt in pairs:
														__UpperCAmelCase						:			Optional[Any]         =   f'''{src}-{tgt}'''
														__UpperCAmelCase						:			Union[str, Any]         =   f'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
														__UpperCAmelCase						:			Any         =   subprocess.run(snake_case__,    stdout=subprocess.PIPE							).stdout.decode("utf-8"							).splitlines()
														__UpperCAmelCase						:			Tuple         =   f'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
														__UpperCAmelCase						:			List[Any]         =   subprocess.run(snake_case__,    stdout=subprocess.PIPE							).stdout.decode("utf-8"							).splitlines()
														__UpperCAmelCase						:			str         =   {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
							return text


_snake_case			=						get_all_data(pairs, n_objs)
_snake_case			=						'''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
	_snake_case			=						json.dump(text, f, indent=2, ensure_ascii=False)




