

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				(    __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					:  Dict     )   -> Tuple:







        '''simple docstring'''

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

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

       def   lowerCamelCase__						(	self					:  str						,	__snake_case					:  Tuple     )   -> List[Any]:







        '''simple docstring'''

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

       def   lowerCamelCase__						(	self					:  Optional[Any]						,	__snake_case					:  int="cpu"						,	__snake_case					:  List[str]=0     )   -> List[str]:







        '''simple docstring'''

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

        if str(__snake_case     ).startswith('mps'     ):
         lowerCamelCase      						=    torch.manual_seed(__snake_case     )
        else:
         lowerCamelCase      						=    torch.Generator(device=__snake_case     ).manual_seed(__snake_case     )
        lowerCamelCase      						=    {
            '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					:  Dict     )   -> Optional[Any]:







        '''simple docstring'''

        lowerCamelCase      						=    self.get_dummy_components()

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

        lowerCamelCase      						=    self.get_dummy_inputs()
        lowerCamelCase      						=    pipe(**__snake_case     )
        lowerCamelCase      						=    output.images

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

        assert image.shape == (1, 64, 64, 3)
        lowerCamelCase      						=    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   lowerCamelCase__						(	self					:  Union[str, Any]     )   -> int:







        '''simple docstring'''

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

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

        lowerCamelCase      						=    pipe(image=__snake_case						,	mask_image=mask_image[:, 0]						,	**__snake_case     )
        lowerCamelCase      						=    output.images

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

        lowerCamelCase      						=    Image.fromarray(np.uinta(__snake_case     )     ).convert('RGB'     )
        lowerCamelCase      						=    Image.fromarray(np.uinta(__snake_case     )     ).convert('RGB'     )

        lowerCamelCase      						=    pipe(**self.get_dummy_inputs()     )
        lowerCamelCase      						=    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     )   -> Union[str, Any]:







        '''simple docstring'''

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


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


       '''simple docstring'''







       def   lowerCamelCase__						(	self					:  Dict     )   -> str:







        '''simple docstring'''

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





       def   lowerCamelCase__						(	self					:  int     )   -> str:







        '''simple docstring'''

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

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

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

        lowerCamelCase      						=    output.images

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

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_lowerCAmelCase :	Optional[Any]     				=    {
    'configuration_xlm_prophetnet': ['XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMProphetNetConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCAmelCase :	Optional[Any]     				=    ['XLMProphetNetTokenizer']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCAmelCase :	List[str]     				=    [
         'XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'XLMProphetNetDecoder',
         'XLMProphetNetEncoder',
         'XLMProphetNetForCausalLM',
         'XLMProphetNetForConditionalGeneration',
         'XLMProphetNetModel',
         'XLMProphetNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlm_prophetnet import (
              XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLMProphetNetDecoder,
              XLMProphetNetEncoder,
              XLMProphetNetForCausalLM,
              XLMProphetNetForConditionalGeneration,
              XLMProphetNetModel,
              XLMProphetNetPreTrainedModel,
          )

else:
     import sys

     _lowerCAmelCase :	List[Any]     				=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




