




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 			A			(		_lowerCAmelCase			,						unittest.TestCase		):
     """simple docstring"""

     _snake_case:	Any     =PaintByExamplePipeline
     _snake_case:	List[str]     =IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
     _snake_case:	Any     =IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     _snake_case:	Dict     =frozenset([]		)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




     def  __snake_case   (    self  :   Any			)				->       str:
           torch.manual_seed(0			)
           lowerCamelCase__        =				UNetaDConditionModel(
               block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  sample_size=3_2	,  in_channels=9	,  out_channels=4	,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')	,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')	,  cross_attention_dim=3_2	,  )
           lowerCamelCase__        =				PNDMScheduler(skip_prk_steps=A__			)
           torch.manual_seed(0			)
           lowerCamelCase__        =				AutoencoderKL(
               block_out_channels=[3_2, 6_4]	,  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=3_2	,  projection_dim=3_2	,  intermediate_size=3_7	,  layer_norm_eps=1E-05	,  num_attention_heads=4	,  num_hidden_layers=5	,  image_size=3_2	,  patch_size=4	,  )
           lowerCamelCase__        =				PaintByExampleImageEncoder(A__	,  proj_size=3_2			)
           lowerCamelCase__        =				CLIPImageProcessor(crop_size=3_2	,  size=3_2			)

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




     def  __snake_case   (    self  :   Optional[int]	,  A__  :   Optional[Any]			)				->       Tuple:
           lowerCamelCase__        =				np.array(image.convert('RGB'			)			)
           lowerCamelCase__        =				image[None].transpose(0	,  3	,  1	,  2			)
           lowerCamelCase__        =				torch.from_numpy(A__			).to(dtype=torch.floataa			) / 127.5 - 1.0
           return image




     def  __snake_case   (    self  :   Dict	,  A__  :   str="cpu"	,  A__  :   str=0			)				->       str:
           # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
           lowerCamelCase__        =				floats_tensor((1, 3, 3_2, 3_2)	,  rng=random.Random(A__			)			).to(A__			)
           lowerCamelCase__        =				image.cpu().permute(0	,  2	,  3	,  1			)[0]
           lowerCamelCase__        =				Image.fromarray(np.uinta(A__			)			).convert('RGB'			).resize((6_4, 6_4)			)
           lowerCamelCase__        =				Image.fromarray(np.uinta(image + 4			)			).convert('RGB'			).resize((6_4, 6_4)			)
           lowerCamelCase__        =				Image.fromarray(np.uinta(A__			)			).convert('RGB'			).resize((3_2, 3_2)			)

           if str(A__			).startswith('mps'			):
                 lowerCamelCase__        =				torch.manual_seed(A__			)
           else:
                 lowerCamelCase__        =				torch.Generator(device=A__			).manual_seed(A__			)
           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  __snake_case   (    self  :   Optional[Any]			)				->       Any:
           lowerCamelCase__        =				self.get_dummy_components()

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

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

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

           assert image.shape == (1, 6_4, 6_4, 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  __snake_case   (    self  :   int			)				->       Dict:
           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(A__			)
           pipe.set_progress_bar_config(disable=A__			)

           lowerCamelCase__        =				pipe(image=A__	,  mask_image=mask_image[:, 0]	,  **A__			)
           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(A__			)			).convert('RGB'			)
           lowerCamelCase__        =				Image.fromarray(np.uinta(A__			)			).convert('RGB'			)

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

           assert out_a.shape == (1, 6_4, 6_4, 3)
           assert np.abs(out_a.flatten() - out_a.flatten()			).max() < 5E-2




     def  __snake_case   (    self  :   int			)				->       Any:
           super().test_inference_batch_single_identical(expected_max_diff=3E-3			)






@slow
@require_torch_gpu
class 			A			(		unittest.TestCase		):
     """simple docstring"""

     def  __snake_case   (    self  :   Optional[Any]			)				->       Union[str, Any]:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()




     def  __snake_case   (    self  :   List[str]			)				->       Any:
           # make sure here that pndm scheduler skips prk
           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(A__			)
           pipe.set_progress_bar_config(disable=A__			)

           lowerCamelCase__        =				torch.manual_seed(3_2_1			)
           lowerCamelCase__        =				pipe(
               image=A__	,  mask_image=A__	,  example_image=A__	,  generator=A__	,  guidance_scale=5.0	,  num_inference_steps=5_0	,  output_type='np'	,  )

           lowerCamelCase__        =				output.images

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

           assert image.shape == (1, 5_1_2, 5_1_2, 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





import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


__SCREAMING_SNAKE_CASE    :   int       =importlib.metadata.version("""numpy""")
__SCREAMING_SNAKE_CASE    :   Optional[int]       =""".""".join([str(x) for x in sys.version_info[:3]])






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     def  __snake_case   (    self  :   List[Any]			)				->       Optional[int]:
           # lt + different version strings
           require_version_core('numpy<1000.4.5'			)
           require_version_core('numpy<1000.4'			)
           require_version_core('numpy<1000'			)

           # le
           require_version_core('numpy<=1000.4.5'			)
           require_version_core(F"""numpy<={numpy_ver}"""			)

           # eq
           require_version_core(F"""numpy=={numpy_ver}"""			)

           # ne
           require_version_core('numpy!=1000.4.5'			)

           # ge
           require_version_core('numpy>=1.0'			)
           require_version_core('numpy>=1.0.0'			)
           require_version_core(F"""numpy>={numpy_ver}"""			)

           # gt
           require_version_core('numpy>1.0.0'			)

           # mix
           require_version_core('numpy>1.0.0,<1000'			)

           # requirement w/o version
           require_version_core('numpy'			)

           # unmet requirements due to version conflict
           for req in ["numpy==1.0.0", "numpy>=1000.0.0", F"""numpy<{numpy_ver}"""]:
                 try:
                       require_version_core(A__			)
                 except ImportError as e:
                       self.assertIn(F"""{req} is required"""	,  str(A__			)			)
                       self.assertIn('but found'	,  str(A__			)			)

        # unmet requirements due to missing module
           for req in ["numpipypie>1", "numpipypie2"]:
                 try:
                       require_version_core(A__			)
                 except importlib.metadata.PackageNotFoundError as e:
                       self.assertIn(F"""The '{req}' distribution was not found and is required by this application"""	,  str(A__			)			)
                       self.assertIn('Try: pip install transformers -U'	,  str(A__			)			)

        # bogus requirements formats:
        # 1. whole thing
           for req in ["numpy??1.0.0", "numpy1.0.0"]:
                 try:
                       require_version_core(A__			)
                 except ValueError as e:
                       self.assertIn('requirement needs to be in the pip package format'	,  str(A__			)			)
        # 2. only operators
           for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
                 try:
                       require_version_core(A__			)
                 except ValueError as e:
                       self.assertIn('need one of '	,  str(A__			)			)




     def  __snake_case   (    self  :   str			)				->       Optional[int]:
           # matching requirement
           require_version('python>=3.6.0'			)

           # not matching requirements
           for req in ["python>9.9.9", "python<3.0.0"]:
                 try:
                       require_version_core(A__			)
                 except ImportError as e:
                       self.assertIn(F"""{req} is required"""	,  str(A__			)			)
                       self.assertIn(F"""but found python=={python_ver}"""	,  str(A__			)			)