



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class    _A	(     __UpperCAmelCase				,__UpperCAmelCase				,__UpperCAmelCase				,unittest.TestCase   ):
       UpperCamelCase__  :   Union[str, Any]						 =						StableDiffusionInpaintPipeline
       UpperCamelCase__  :   Union[str, Any]						 =						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
       UpperCamelCase__  :   Tuple						 =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       UpperCamelCase__  :   int						 =						frozenset(
           []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       UpperCamelCase__  :   int						 =						frozenset([]   )



       def 		_lowerCamelCase      (		self							:					Dict):


           '''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					,		attention_head_dim=(2, 4)					,		use_linear_projection=__SCREAMING_SNAKE_CASE					,		)
           __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					,		sample_size=128					,		)
           torch.manual_seed(0)
           __a							=    CLIPTextConfig(
               bos_token_id=0					,		eos_token_id=2					,		hidden_size=32					,		intermediate_size=37					,		layer_norm_eps=1E-05					,		num_attention_heads=4					,		num_hidden_layers=5					,		pad_token_id=1					,		vocab_size=1_000					,		hidden_act='''gelu'''					,		projection_dim=512					,		)
           __a							=    CLIPTextModel(__SCREAMING_SNAKE_CASE)
           __a							=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

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



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Tuple=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))
           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							=    {
               '''prompt''': '''A painting of a squirrel eating a burger''',
               '''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							:					List[Any]):


           '''simple docstring'''

           __a							=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           __a							=    self.get_dummy_components()
           __a							=    StableDiffusionInpaintPipeline(**__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(**__SCREAMING_SNAKE_CASE).images
           __a							=    image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           __a							=    np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76])

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





       def 		_lowerCamelCase      (		self							:					Optional[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							:					int):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-inpaint/init_image.png''')
           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')
           __a							=    load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
               '''/yellow_cat_sitting_on_a_park_bench.npy''')

           __a							=    '''stabilityai/stable-diffusion-2-inpainting'''
           __a							=    StableDiffusionInpaintPipeline.from_pretrained(__SCREAMING_SNAKE_CASE					,		safety_checker=__SCREAMING_SNAKE_CASE)
           pipe.to(__SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
           pipe.enable_attention_slicing()

           __a							=    '''Face of a yellow cat, high resolution, sitting on a park bench'''

           __a							=    torch.manual_seed(0)
           __a							=    pipe(
               prompt=__SCREAMING_SNAKE_CASE					,		image=__SCREAMING_SNAKE_CASE					,		mask_image=__SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE					,		output_type='''np'''					,		)
           __a							=    output.images[0]

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image).max() < 9E-3



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


           '''simple docstring'''

           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-inpaint/init_image.png''')
           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')
           __a							=    load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
               '''/yellow_cat_sitting_on_a_park_bench_fp16.npy''')

           __a							=    '''stabilityai/stable-diffusion-2-inpainting'''
           __a							=    StableDiffusionInpaintPipeline.from_pretrained(
               __SCREAMING_SNAKE_CASE					,		torch_dtype=torch.floataa					,		safety_checker=__SCREAMING_SNAKE_CASE					,		)
           pipe.to(__SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
           pipe.enable_attention_slicing()

           __a							=    '''Face of a yellow cat, high resolution, sitting on a park bench'''

           __a							=    torch.manual_seed(0)
           __a							=    pipe(
               prompt=__SCREAMING_SNAKE_CASE					,		image=__SCREAMING_SNAKE_CASE					,		mask_image=__SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE					,		output_type='''np'''					,		)
           __a							=    output.images[0]

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image).max() < 5E-1





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


           '''simple docstring'''

           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-inpaint/init_image.png''')
           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')

           __a							=    '''stabilityai/stable-diffusion-2-inpainting'''
           __a							=    PNDMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE					,		subfolder='''scheduler''')
           __a							=    StableDiffusionInpaintPipeline.from_pretrained(
               __SCREAMING_SNAKE_CASE					,		safety_checker=__SCREAMING_SNAKE_CASE					,		scheduler=__SCREAMING_SNAKE_CASE					,		torch_dtype=torch.floataa					,		)
           pipe.to(__SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
           pipe.enable_attention_slicing(1)
           pipe.enable_sequential_cpu_offload()

           __a							=    '''Face of a yellow cat, high resolution, sitting on a park bench'''

           __a							=    torch.manual_seed(0)
           __a							=    pipe(
               prompt=__SCREAMING_SNAKE_CASE					,		image=__SCREAMING_SNAKE_CASE					,		mask_image=__SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE					,		num_inference_steps=2					,		output_type='''np'''					,		)

           __a							=    torch.cuda.max_memory_allocated()
           # make sure that less than 2.65 GB is allocated
           assert mem_bytes < 2.65 * 10**9










# Lint as: python3
import itertools
import os
import re


__snake_case						:int						=					re.compile(r'''([A-Z]+)([A-Z][a-z])''')
__snake_case						:List[Any]						=					re.compile(r'''([a-z\d])([A-Z])''')

__snake_case						:Dict						=					re.compile(r'''(?<!_)_(?!_)''')
__snake_case						:Optional[int]						=					re.compile(r'''(_{2,})''')

__snake_case						:Optional[Any]						=					r'''^\w+(\.\w+)*$'''

__snake_case						:Union[str, Any]						=					r'''<>:/\|?*'''





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    _uppercase_uppercase_re.sub(R'''\1_\2'''       ,			_UpperCAmelCase    )
    __a							=    _lowercase_uppercase_re.sub(R'''\1_\2'''       ,			_UpperCAmelCase    )
    return name.lower()





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    _single_underscore_re.split(_UpperCAmelCase    )
    __a							=    [_multiple_underscores_re.split(_UpperCAmelCase    ) for n in name]
    return "".join(n.capitalize() for n in itertools.chain.from_iterable(_UpperCAmelCase    ) if n != ''''''    )





def 					__snake_case     (      _UpperCAmelCase    ):
    if os.path.basename(_UpperCAmelCase    ) != name:
        raise ValueError(f'Should be a dataset name, not a path: {name}'    )
    return camelcase_to_snakecase(_UpperCAmelCase    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    if os.path.basename(_UpperCAmelCase    ) != name:
        raise ValueError(f'Should be a dataset name, not a path: {name}'    )
    if not re.match(_split_re       ,			_UpperCAmelCase    ):
        raise ValueError(f'Split name should match \'{_split_re}\'\' but got \'{split}\'.'    )
    return f'{filename_prefix_for_name(_UpperCAmelCase    )}-{split}'





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None    ):
    __a							=    filename_prefix_for_split(_UpperCAmelCase       ,			_UpperCAmelCase    )
    if filetype_suffix:
        prefix += f'.{filetype_suffix}'
    __a							=    os.path.join(_UpperCAmelCase       ,			_UpperCAmelCase    )
    return f'{filepath}*'





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None    ):
    __a							=    filename_prefix_for_split(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    os.path.join(_UpperCAmelCase       ,			_UpperCAmelCase    )

    if shard_lengths:
        __a							=    len(_UpperCAmelCase    )
        __a							=    [f'{prefix}-{shard_id:05d}-of-{num_shards:05d}' for shard_id in range(_UpperCAmelCase    )]
        if filetype_suffix:
            __a							=    [filename + f'.{filetype_suffix}' for filename in filenames]
        return filenames
    else:
        __a							=    prefix
        if filetype_suffix:
            filename += f'.{filetype_suffix}'
        return [filename]





