"""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     snake_case	(			__UpperCAmelCase				,   unittest.TestCase    ):




					"""simple docstring"""

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







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									torch.manual_seed(0      )
									UpperCAmelCase__					=							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__					=							PNDMScheduler(skip_prk_steps=lowerCamelCase__      )
									torch.manual_seed(0      )
									UpperCAmelCase__					=							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__					=							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__					=							PaintByExampleImageEncoder(lowerCamelCase__   ,proj_size=32      )
									UpperCAmelCase__					=							CLIPImageProcessor(crop_size=32   ,size=32      )

									UpperCAmelCase__					=							{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'image_encoder': image_encoder,
									    'safety_checker': None,
									    'feature_extractor': feature_extractor,
									}
									return components







					def 		__lowerCAmelCase						(    self       :     List[Any]   ,lowerCamelCase__       :     List[Any]      ):
									UpperCAmelCase__					=							np.array(image.convert('RGB'      )      )
									UpperCAmelCase__					=							image[None].transpose(0   ,3   ,1   ,2      )
									UpperCAmelCase__					=							torch.from_numpy(lowerCamelCase__      ).to(dtype=torch.floataa      ) / 1_2_7.5 - 1.0
									return image







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]   ,lowerCamelCase__       :     Union[str, Any]="cpu"   ,lowerCamelCase__       :     List[Any]=0      ):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									UpperCAmelCase__					=							floats_tensor((1, 3, 32, 32)   ,rng=random.Random(lowerCamelCase__      )      ).to(lowerCamelCase__      )
									UpperCAmelCase__					=							image.cpu().permute(0   ,2   ,3   ,1      )[0]
									UpperCAmelCase__					=							Image.fromarray(np.uinta(lowerCamelCase__      )      ).convert('RGB'      ).resize((64, 64)      )
									UpperCAmelCase__					=							Image.fromarray(np.uinta(image + 4      )      ).convert('RGB'      ).resize((64, 64)      )
									UpperCAmelCase__					=							Image.fromarray(np.uinta(lowerCamelCase__      )      ).convert('RGB'      ).resize((32, 32)      )

									if str(lowerCamelCase__      ).startswith('mps'      ):
													UpperCAmelCase__					=							torch.manual_seed(lowerCamelCase__      )
									else:
													UpperCAmelCase__					=							torch.Generator(device=lowerCamelCase__      ).manual_seed(lowerCamelCase__      )
									UpperCAmelCase__					=							{
									    '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       :     Tuple      ):
									UpperCAmelCase__					=							self.get_dummy_components()

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

									UpperCAmelCase__					=							self.get_dummy_inputs()
									UpperCAmelCase__					=							pipe(**lowerCamelCase__      )
									UpperCAmelCase__					=							output.images

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

									assert image.shape == (1, 64, 64, 3)
									UpperCAmelCase__					=							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       :     Dict      ):
									UpperCAmelCase__					=							'cpu'
									UpperCAmelCase__					=							self.get_dummy_inputs()
									inputs.pop('mask_image'      )
									UpperCAmelCase__					=							self.convert_to_pt(inputs.pop('image'      )      )
									UpperCAmelCase__					=							image.clamp(0   ,1      ) / 2

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

									UpperCAmelCase__					=							pipe(image=lowerCamelCase__   ,mask_image=mask_image[:, 0]   ,**lowerCamelCase__      )
									UpperCAmelCase__					=							output.images

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

									UpperCAmelCase__					=							Image.fromarray(np.uinta(lowerCamelCase__      )      ).convert('RGB'      )
									UpperCAmelCase__					=							Image.fromarray(np.uinta(lowerCamelCase__      )      ).convert('RGB'      )

									UpperCAmelCase__					=							pipe(**self.get_dummy_inputs()      )
									UpperCAmelCase__					=							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       :     int      ):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3      )






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




					"""simple docstring"""

					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 		__lowerCAmelCase						(    self       :     Optional[Any]      ):
									# make sure here that pndm scheduler skips prk
									UpperCAmelCase__					=							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/paint_by_example/dog_in_bucket.png'      )
									UpperCAmelCase__					=							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/paint_by_example/mask.png'      )
									UpperCAmelCase__					=							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/paint_by_example/panda.jpg'      )

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

									UpperCAmelCase__					=							torch.manual_seed(321      )
									UpperCAmelCase__					=							pipe(
									    image=lowerCamelCase__   ,mask_image=lowerCamelCase__   ,example_image=lowerCamelCase__   ,generator=lowerCamelCase__   ,guidance_scale=5.0   ,num_inference_steps=50   ,output_type='np'   ,)

									UpperCAmelCase__					=							output.images

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

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__					=							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




"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase__  :  str											=     logging.get_logger(__name__)

lowerCAmelCase__  :  Union[str, Any]											=     {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"plbart"
					snake_case__       =			["past_key_values"]
					snake_case__       =			{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







					def __init__(    self       :     str   ,lowerCamelCase__       :     List[Any]=50_005   ,lowerCamelCase__       :     Optional[Any]=1_024   ,lowerCamelCase__       :     List[str]=6   ,lowerCamelCase__       :     Tuple=3_072   ,lowerCamelCase__       :     Optional[int]=12   ,lowerCamelCase__       :     Optional[int]=6   ,lowerCamelCase__       :     Optional[int]=3_072   ,lowerCamelCase__       :     Any=12   ,lowerCamelCase__       :     str=0.0   ,lowerCamelCase__       :     str=0.0   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     str=True   ,lowerCamelCase__       :     Optional[int]="gelu"   ,lowerCamelCase__       :     Optional[Any]=768   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Union[str, Any]=0.0   ,lowerCamelCase__       :     List[str]=0.0_2   ,lowerCamelCase__       :     Optional[int]=0.0   ,lowerCamelCase__       :     Dict=True   ,lowerCamelCase__       :     List[Any]=1   ,lowerCamelCase__       :     List[str]=0   ,lowerCamelCase__       :     Any=2   ,lowerCamelCase__       :     Any=2   ,**lowerCamelCase__       :     Dict   ,):
									UpperCAmelCase__					=							vocab_size
									UpperCAmelCase__					=							max_position_embeddings
									UpperCAmelCase__					=							d_model
									UpperCAmelCase__					=							encoder_ffn_dim
									UpperCAmelCase__					=							encoder_layers
									UpperCAmelCase__					=							encoder_attention_heads
									UpperCAmelCase__					=							decoder_ffn_dim
									UpperCAmelCase__					=							decoder_layers
									UpperCAmelCase__					=							decoder_attention_heads
									UpperCAmelCase__					=							dropout
									UpperCAmelCase__					=							attention_dropout
									UpperCAmelCase__					=							activation_dropout
									UpperCAmelCase__					=							activation_function
									UpperCAmelCase__					=							init_std
									UpperCAmelCase__					=							encoder_layerdrop
									UpperCAmelCase__					=							decoder_layerdrop
									UpperCAmelCase__					=							classifier_dropout
									UpperCAmelCase__					=							use_cache
									UpperCAmelCase__					=							encoder_layers
									UpperCAmelCase__					=							scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=lowerCamelCase__   ,bos_token_id=lowerCamelCase__   ,eos_token_id=lowerCamelCase__   ,is_encoder_decoder=lowerCamelCase__   ,forced_eos_token_id=lowerCamelCase__   ,**lowerCamelCase__   ,)






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					@property
					def 		__lowerCAmelCase						(    self       :     int      ):
									return OrderedDict(
									    [
									        ('input_ids', {0: 'batch', 1: 'sequence'}),
									        ('attention_mask', {0: 'batch', 1: 'sequence'}),
									    ]      )







					@property
					def 		__lowerCAmelCase						(    self       :     Any      ):
									if self.use_past:
													return OrderedDict(
													    [
													        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
													        ('past_keys', {0: 'batch', 2: 'sequence'}),
													        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
													    ]      )
									else:
													return OrderedDict(
													    [
													        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
													        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
													    ]      )



