





"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


a__   :       Tuple   						=			logging.get_logger(__name__)

a__   :       int   						=			{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Tuple										=       """rembert"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=25_0300				,   __SCREAMING_SNAKE_CASE=1152				,   __SCREAMING_SNAKE_CASE=32				,   __SCREAMING_SNAKE_CASE=18				,   __SCREAMING_SNAKE_CASE=256				,   __SCREAMING_SNAKE_CASE=1664				,   __SCREAMING_SNAKE_CASE=4608				,   __SCREAMING_SNAKE_CASE="gelu"				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=0.1				,   __SCREAMING_SNAKE_CASE=512				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=0.02				,   __SCREAMING_SNAKE_CASE=1e-12				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=0				,   __SCREAMING_SNAKE_CASE=312				,   __SCREAMING_SNAKE_CASE=313				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE				,   bos_token_id=__SCREAMING_SNAKE_CASE				,   eos_token_id=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			vocab_size
        snake_case__      							=			input_embedding_size
        snake_case__      							=			output_embedding_size
        snake_case__      							=			max_position_embeddings
        snake_case__      							=			hidden_size
        snake_case__      							=			num_hidden_layers
        snake_case__      							=			num_attention_heads
        snake_case__      							=			intermediate_size
        snake_case__      							=			hidden_act
        snake_case__      							=			hidden_dropout_prob
        snake_case__      							=			attention_probs_dropout_prob
        snake_case__      							=			classifier_dropout_prob
        snake_case__      							=			initializer_range
        snake_case__      							=			type_vocab_size
        snake_case__      							=			layer_norm_eps
        snake_case__      							=			use_cache
        snake_case__      							=			False



class        lowercase_			(       __lowerCAmelCase							):

       @property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        if self.task == "multiple-choice":
         snake_case__      							=			{0: "batch", 1: "choice", 2: "sequence"}
        else:
         snake_case__      							=			{0: "batch", 1: "sequence"}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
                ("token_type_ids", dynamic_axis),
            ]       )

       @property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return 1e-4








"""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_			(       __lowerCAmelCase			,unittest.TestCase							):
       snake_case__			:							Tuple										=       PaintByExamplePipeline
       snake_case__			:							List[str]										=       IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       snake_case__			:							List[str]										=       IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       snake_case__			:							List[str]										=       frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        torch.manual_seed(0       )
        snake_case__      							=			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				,   )
        snake_case__      							=			PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE       )
        torch.manual_seed(0       )
        snake_case__      							=			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       )
        snake_case__      							=			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				,   )
        snake_case__      							=			PaintByExampleImageEncoder(__SCREAMING_SNAKE_CASE				,   proj_size=32       )
        snake_case__      							=			CLIPImageProcessor(crop_size=32				,   size=32       )

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

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			np.array(image.convert("RGB"       )       )
        snake_case__      							=			image[None].transpose(0				,   3				,   1				,   2       )
        snake_case__      							=			torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(dtype=torch.floataa       ) / 127.5 - 1.0
        return image

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE="cpu"				,   __SCREAMING_SNAKE_CASE=0       ):






        '''simple docstring'''

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

        if str(__SCREAMING_SNAKE_CASE       ).startswith("mps"       ):
         snake_case__      							=			torch.manual_seed(__SCREAMING_SNAKE_CASE       )
        else:
         snake_case__      							=			torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(__SCREAMING_SNAKE_CASE       )
        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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_dummy_components()

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

        snake_case__      							=			self.get_dummy_inputs()
        snake_case__      							=			pipe(**__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			output.images

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

        assert image.shape == (1, 64, 64, 3)
        snake_case__      							=			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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

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

        snake_case__      							=			pipe(image=__SCREAMING_SNAKE_CASE				,   mask_image=mask_image[:, 0]				,   **__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			output.images

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

        snake_case__      							=			Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE       )       ).convert("RGB"       )
        snake_case__      							=			Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE       )       ).convert("RGB"       )

        snake_case__      							=			pipe(**self.get_dummy_inputs()       )
        snake_case__      							=			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       ):






        '''simple docstring'''

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



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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

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

        snake_case__      							=			torch.manual_seed(321       )
        snake_case__      							=			pipe(
            image=__SCREAMING_SNAKE_CASE				,   mask_image=__SCREAMING_SNAKE_CASE				,   example_image=__SCREAMING_SNAKE_CASE				,   generator=__SCREAMING_SNAKE_CASE				,   guidance_scale=5.0				,   num_inference_steps=50				,   output_type="np"				,   )

        snake_case__      							=			output.images

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

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

