

'''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	(    _a		,					unittest.TestCase  ):
   _A    :       Dict      				=   PaintByExamplePipeline
   _A    :       Any      				=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   _A    :       str      				=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _A    :       int      				=   frozenset([]  )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
   def       __UpperCamelCase  (	self     : Optional[int]							):
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:Dict   =   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       ,)
     SCREAMING_SNAKE_CASE:Optional[Any]   =   PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE__							)
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:List[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							)
     SCREAMING_SNAKE_CASE:Optional[Any]   =   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       ,)
     SCREAMING_SNAKE_CASE:Any   =   PaintByExampleImageEncoder(SCREAMING_SNAKE_CASE__       ,proj_size=32							)
     SCREAMING_SNAKE_CASE:int   =   CLIPImageProcessor(crop_size=32       ,size=32							)

     SCREAMING_SNAKE_CASE:List[str]   =   {
         "unet": unet,
         "scheduler": scheduler,
         "vae": vae,
         "image_encoder": image_encoder,
         "safety_checker": None,
         "feature_extractor": feature_extractor,
     }
     return components
   def       __UpperCamelCase  (	self     : Optional[int]       ,SCREAMING_SNAKE_CASE__     : List[Any]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   np.array(image.convert("RGB"							)							)
     SCREAMING_SNAKE_CASE:Dict   =   image[None].transpose(0       ,3       ,1       ,2							)
     SCREAMING_SNAKE_CASE:str   =   torch.from_numpy(SCREAMING_SNAKE_CASE__							).to(dtype=torch.floataa							) / 127.5 - 1.0
     return image
   def       __UpperCamelCase  (	self     : Any       ,SCREAMING_SNAKE_CASE__     : Tuple="cpu"       ,SCREAMING_SNAKE_CASE__     : str=0							):
     # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
     SCREAMING_SNAKE_CASE:str   =   floats_tensor((1, 3, 32, 32)       ,rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[str]   =   image.cpu().permute(0       ,2       ,3       ,1							)[0]
     SCREAMING_SNAKE_CASE:Dict   =   Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__							)							).convert("RGB"							).resize((64, 64)							)
     SCREAMING_SNAKE_CASE:Dict   =   Image.fromarray(np.uinta(image + 4							)							).convert("RGB"							).resize((64, 64)							)
     SCREAMING_SNAKE_CASE:int   =   Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__							)							).convert("RGB"							).resize((32, 32)							)

     if str(SCREAMING_SNAKE_CASE__							).startswith("mps"							):
       SCREAMING_SNAKE_CASE:Any   =   torch.manual_seed(SCREAMING_SNAKE_CASE__							)
     else:
       SCREAMING_SNAKE_CASE:Optional[Any]   =   torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str   =   {
         "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       __UpperCamelCase  (	self     : int							):
     SCREAMING_SNAKE_CASE:Optional[int]   =   self.get_dummy_components()

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE:Optional[int]   =   PaintByExamplePipeline(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   pipe.to("cpu"							)
     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

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

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     SCREAMING_SNAKE_CASE:Dict   =   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       __UpperCamelCase  (	self     : Optional[Any]							):
     SCREAMING_SNAKE_CASE:Optional[Any]   =   "cpu"
     SCREAMING_SNAKE_CASE:Tuple   =   self.get_dummy_inputs()
     inputs.pop("mask_image"							)
     SCREAMING_SNAKE_CASE:List[str]   =   self.convert_to_pt(inputs.pop("image"							)							)
     SCREAMING_SNAKE_CASE:List[Any]   =   image.clamp(0       ,1							) / 2

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   PaintByExamplePipeline(**self.get_dummy_components()							)
     SCREAMING_SNAKE_CASE:Tuple   =   pipe.to(SCREAMING_SNAKE_CASE__							)
     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   pipe(image=SCREAMING_SNAKE_CASE__       ,mask_image=mask_image[:, 0]       ,**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   output.images

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   image.cpu().permute(0       ,2       ,3       ,1							)[0]
     SCREAMING_SNAKE_CASE:List[str]   =   mask_image.cpu().permute(0       ,2       ,3       ,1							)[0]

     SCREAMING_SNAKE_CASE:Any   =   Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__							)							).convert("RGB"							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__							)							).convert("RGB"							)

     SCREAMING_SNAKE_CASE:int   =   pipe(**self.get_dummy_inputs()							)
     SCREAMING_SNAKE_CASE:Tuple   =   output.images

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







   def       __UpperCamelCase  (	self     : int							):
     super().test_inference_batch_single_identical(expected_max_diff=3e-3							)





@slow
@require_torch_gpu
class 							_snake_case	(    unittest.TestCase  ):
   def       __UpperCamelCase  (	self     : str							):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







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

     SCREAMING_SNAKE_CASE:int   =   PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   pipe.to(SCREAMING_SNAKE_CASE__							)
     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[str]   =   torch.manual_seed(321							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   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"       ,)

     SCREAMING_SNAKE_CASE:Optional[int]   =   output.images

     SCREAMING_SNAKE_CASE:Optional[int]   =   image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     SCREAMING_SNAKE_CASE:List[Any]   =   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









'''simple docstring'''

from typing import TYPE_CHECKING

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


A_      =							{"configuration_yoso": ["YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP", "YosoConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_      =							[
        "YOSO_PRETRAINED_MODEL_ARCHIVE_LIST",
        "YosoForMaskedLM",
        "YosoForMultipleChoice",
        "YosoForQuestionAnswering",
        "YosoForSequenceClassification",
        "YosoForTokenClassification",
        "YosoLayer",
        "YosoModel",
        "YosoPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_yoso import (
            YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
            YosoForMaskedLM,
            YosoForMultipleChoice,
            YosoForQuestionAnswering,
            YosoForSequenceClassification,
            YosoForTokenClassification,
            YosoLayer,
            YosoModel,
            YosoPreTrainedModel,
        )


else:
    import sys

    A_      =							_LazyModule(__name__, globals()["__file__"], _import_structure)






