



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			(      __UpperCAmelCase ,unittest.TestCase       ):
       UpperCamelCase__    :       Dict		   =		PaintByExamplePipeline
       UpperCamelCase__    :       Tuple		   =		IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       UpperCamelCase__    :       List[str]		   =		IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       UpperCamelCase__    :       Tuple		   =		frozenset([]       )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



       def 						_lowerCamelCase     (						self				:	Union[str, Any]):


           '''simple docstring'''

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

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



       def 						_lowerCamelCase     (						self				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]):


           '''simple docstring'''

           __a					=							np.array(image.convert('''RGB'''))
           __a					=							image[None].transpose(0	,						3	,						1	,						2)
           __a					=							torch.from_numpy(__SCREAMING_SNAKE_CASE).to(dtype=torch.floataa) / 1_27.5 - 1.0
           return image



       def 						_lowerCamelCase     (						self				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	Dict="cpu"	,						__SCREAMING_SNAKE_CASE				:	List[str]=0):


           '''simple docstring'''

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

           if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
               __a					=							torch.manual_seed(__SCREAMING_SNAKE_CASE)
           else:
               __a					=							torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
           __a					=							{
               '''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				:	Union[str, Any]):


           '''simple docstring'''

           __a					=							self.get_dummy_components()

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

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

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

           assert image.shape == (1, 64, 64, 3)
           __a					=							np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2



       def 						_lowerCamelCase     (						self				:	Any):


           '''simple docstring'''

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

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

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

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

           __a					=							Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE)).convert('''RGB''')
           __a					=							Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE)).convert('''RGB''')

           __a					=							pipe(**self.get_dummy_inputs())
           __a					=							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				:	List[Any]):


           '''simple docstring'''

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


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



       def 						_lowerCamelCase     (						self				:	List[str]):


           '''simple docstring'''

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





       def 						_lowerCamelCase     (						self				:	Union[str, Any]):


           '''simple docstring'''

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

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

           __a					=							torch.manual_seed(321)
           __a					=							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'''	,						)

           __a					=							output.images

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

           assert image.shape == (1, 512, 512, 3)
           __a					=							np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2










from __future__ import annotations

from typing import Generic, TypeVar

__snake_case		:str 						=	TypeVar('''T''')

class        _A			(      __UpperCAmelCase       ):
       pass


class        _A			(      __UpperCAmelCase       ):
       pass


class        _A			(      Generic[T]       ):



       def __init__(						self				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	int = 10):


           '''simple docstring'''

           __a					=							[]
           __a					=							limit



       def __bool__(						self				:	List[Any]):


           '''simple docstring'''

           return bool(self.stack)



       def __str__(						self				:	Any):


           '''simple docstring'''

           return str(self.stack)



       def 						_lowerCamelCase     (						self				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	T):


           '''simple docstring'''

           if len(self.stack) >= self.limit:
               raise StackOverflowError
           self.stack.append(__SCREAMING_SNAKE_CASE)



       def 						_lowerCamelCase     (						self				:	str):


           '''simple docstring'''

           if not self.stack:
               raise StackUnderflowError
           return self.stack.pop()



       def 						_lowerCamelCase     (						self				:	Optional[Any]):


           '''simple docstring'''

           if not self.stack:
               raise StackUnderflowError
           return self.stack[-1]



       def 						_lowerCamelCase     (						self				:	Union[str, Any]):


           '''simple docstring'''

           return not bool(self.stack)



       def 						_lowerCamelCase     (						self				:	Tuple):


           '''simple docstring'''

           return self.size() == self.limit



       def 						_lowerCamelCase     (						self				:	Union[str, Any]):


           '''simple docstring'''

           return len(self.stack)





       def __contains__(						self				:	int	,						__SCREAMING_SNAKE_CASE				:	T):


           '''simple docstring'''

           return item in self.stack





def 	__snake_case      (     ):
    __a					=							Stack(10							)
    assert bool(_UpperCAmelCase							) is False
    assert stack.is_empty() is True
    assert stack.is_full() is False
    assert str(_UpperCAmelCase							) == "[]"

    try:
        __a					=							stack.pop()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    try:
        __a					=							stack.peek()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    for i in range(10							):
        assert stack.size() == i
        stack.push(_UpperCAmelCase							)

    assert bool(_UpperCAmelCase							)
    assert not stack.is_empty()
    assert stack.is_full()
    assert str(_UpperCAmelCase							) == str(list(range(10							)							)							)
    assert stack.pop() == 9
    assert stack.peek() == 8

    stack.push(100							)
    assert str(_UpperCAmelCase							) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]							)

    try:
        stack.push(200							)
        raise AssertionError  # This should not happen
    except StackOverflowError:
        assert True  # This should happen

    assert not stack.is_empty()
    assert stack.size() == 10

    assert 5 in stack
    assert 55 not in stack


if __name__ == "__main__":
 test_stack()





