


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A	:		Union[str, Any]    =       logging.get_logger(__name__)

A	:		int    =       {
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			'''roberta-prelayernorm'''

					def __init__(self   :						Dict				, _UpperCAmelCase   :						List[Any]=5_0265				, _UpperCAmelCase   :						List[Any]=768				, _UpperCAmelCase   :						str=12				, _UpperCAmelCase   :						Any=12				, _UpperCAmelCase   :						Any=3072				, _UpperCAmelCase   :						int="gelu"				, _UpperCAmelCase   :						Tuple=0.1				, _UpperCAmelCase   :						Tuple=0.1				, _UpperCAmelCase   :						Any=512				, _UpperCAmelCase   :						Dict=2				, _UpperCAmelCase   :						int=0.02				, _UpperCAmelCase   :						List[Any]=1E-1_2				, _UpperCAmelCase   :						Tuple=1				, _UpperCAmelCase   :						int=0				, _UpperCAmelCase   :						int=2				, _UpperCAmelCase   :						Optional[Any]="absolute"				, _UpperCAmelCase   :						int=True				, _UpperCAmelCase   :						Optional[int]=None				, **_UpperCAmelCase   :						Any				, )						->       List[str]:




							"""simple docstring"""


							super().__init__(pad_token_id=_UpperCAmelCase				, bos_token_id=_UpperCAmelCase				, eos_token_id=_UpperCAmelCase				, **_UpperCAmelCase		)

							lowercase__					      =		vocab_size
							lowercase__					      =		hidden_size
							lowercase__					      =		num_hidden_layers
							lowercase__					      =		num_attention_heads
							lowercase__					      =		hidden_act
							lowercase__					      =		intermediate_size
							lowercase__					      =		hidden_dropout_prob
							lowercase__					      =		attention_probs_dropout_prob
							lowercase__					      =		max_position_embeddings
							lowercase__					      =		type_vocab_size
							lowercase__					      =		initializer_range
							lowercase__					      =		layer_norm_eps
							lowercase__					      =		position_embedding_type
							lowercase__					      =		use_cache
							lowercase__					      =		classifier_dropout






class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					@property
					def   lowerCamelCase__					(self   :						Any		)						->       Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task == "multiple-choice":
									lowercase__					      =		{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									lowercase__					      =		{0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							    ]		)





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							):
					'''simple docstring'''






					A__      =			StableDiffusionInpaintPipeline
					A__      =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					A__      =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A__      =			frozenset(
					    []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					A__      =			frozenset([]							)

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




							"""simple docstring"""


							torch.manual_seed(0		)
							lowercase__					      =		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=_UpperCAmelCase				, )
							lowercase__					      =		PNDMScheduler(skip_prk_steps=_UpperCAmelCase		)
							torch.manual_seed(0		)
							lowercase__					      =		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		)
							lowercase__					      =		CLIPTextConfig(
							    bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1E-0_5				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1000				, hidden_act="""gelu"""				, projection_dim=512				, )
							lowercase__					      =		CLIPTextModel(_UpperCAmelCase		)
							lowercase__					      =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

							lowercase__					      =		{
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Optional[Any]=0		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		floats_tensor((1, 3, 32, 32)				, rng=random.Random(_UpperCAmelCase		)		).to(_UpperCAmelCase		)
							lowercase__					      =		image.cpu().permute(0				, 2				, 3				, 1		)[0]
							lowercase__					      =		Image.fromarray(np.uinta(_UpperCAmelCase		)		).convert("""RGB"""		).resize((64, 64)		)
							lowercase__					      =		Image.fromarray(np.uinta(image + 4		)		).convert("""RGB"""		).resize((64, 64)		)
							if str(_UpperCAmelCase		).startswith("""mps"""		):
									lowercase__					      =		torch.manual_seed(_UpperCAmelCase		)
							else:
									lowercase__					      =		torch.Generator(device=_UpperCAmelCase		).manual_seed(_UpperCAmelCase		)
							lowercase__					      =		{
							    """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   :						Optional[int]		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowercase__					      =		self.get_dummy_components()
							lowercase__					      =		StableDiffusionInpaintPipeline(**_UpperCAmelCase		)
							lowercase__					      =		sd_pipe.to(_UpperCAmelCase		)
							sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

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

							assert image.shape == (1, 64, 64, 3)
							lowercase__					      =		np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]		)

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


					def   lowerCamelCase__					(self   :						str		)						->       int:




							"""simple docstring"""


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






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






					def   lowerCamelCase__					(self   :						str		)						->       Optional[Any]:




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						Union[str, Any]		)						->       Union[str, Any]:




							"""simple docstring"""


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

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase				, safety_checker=_UpperCAmelCase		)
							pipe.to(_UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)
							pipe.enable_attention_slicing()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, output_type="""np"""				, )
							lowercase__					      =		output.images[0]

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

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       List[str]:




							"""simple docstring"""


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

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		StableDiffusionInpaintPipeline.from_pretrained(
							    _UpperCAmelCase				, torch_dtype=torch.floataa				, safety_checker=_UpperCAmelCase				, )
							pipe.to(_UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)
							pipe.enable_attention_slicing()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, output_type="""np"""				, )
							lowercase__					      =		output.images[0]

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


					def   lowerCamelCase__					(self   :						List[str]		)						->       Optional[Any]:




							"""simple docstring"""


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

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

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

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, num_inference_steps=2				, output_type="""np"""				, )

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

