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


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					int           =							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__					,	)
												SCREAMING_SNAKE_CASE							:					int           =							PNDMScheduler(skip_prk_steps=UpperCamelCase__		)
												torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							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		)
												SCREAMING_SNAKE_CASE							:					Any           =							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=1000					,	hidden_act='''gelu'''					,	projection_dim=512					,	)
												SCREAMING_SNAKE_CASE							:					Dict           =							CLIPTextModel(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							{
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''vae''': vae,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''safety_checker''': None,
												    '''feature_extractor''': None,
												}
												return components


						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       Union[str, Any]=0		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							floats_tensor((1, 3, 32, 32)					,	rng=random.Random(UpperCamelCase__		)		).to(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							image.cpu().permute(0					,	2					,	3					,	1		)[0]
												SCREAMING_SNAKE_CASE							:					List[Any]           =							Image.fromarray(np.uinta(UpperCamelCase__		)		).convert('''RGB'''		).resize((64, 64)		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							Image.fromarray(np.uinta(image + 4		)		).convert('''RGB'''		).resize((64, 64)		)
												if str(UpperCamelCase__		).startswith('''mps'''		):
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.manual_seed(UpperCamelCase__		)
												else:
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.Generator(device=UpperCamelCase__		).manual_seed(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					int           =							{
												    '''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  __A ( self			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
												SCREAMING_SNAKE_CASE							:					Any           =							self.get_dummy_components()
												SCREAMING_SNAKE_CASE							:					int           =							StableDiffusionInpaintPipeline(**UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							sd_pipe.to(UpperCamelCase__		)
												sd_pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.get_dummy_inputs(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							sd_pipe(**UpperCamelCase__		).images
												SCREAMING_SNAKE_CASE							:					List[str]           =							image[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)
												SCREAMING_SNAKE_CASE							:					List[str]           =							np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]		)

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



						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





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






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


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





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


						def  __A ( self			:       Tuple		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''stabilityai/stable-diffusion-2-inpainting'''
												SCREAMING_SNAKE_CASE							:					Any           =							StableDiffusionInpaintPipeline.from_pretrained(UpperCamelCase__					,	safety_checker=UpperCamelCase__		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)
												pipe.enable_attention_slicing()

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''Face of a yellow cat, high resolution, sitting on a park bench'''

												SCREAMING_SNAKE_CASE							:					int           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							pipe(
												    prompt=UpperCamelCase__					,	image=UpperCamelCase__					,	mask_image=UpperCamelCase__					,	generator=UpperCamelCase__					,	output_type='''np'''					,	)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							output.images[0]

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


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							'''stabilityai/stable-diffusion-2-inpainting'''
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							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()

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''Face of a yellow cat, high resolution, sitting on a park bench'''

												SCREAMING_SNAKE_CASE							:					List[str]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							pipe(
												    prompt=UpperCamelCase__					,	image=UpperCamelCase__					,	mask_image=UpperCamelCase__					,	generator=UpperCamelCase__					,	output_type='''np'''					,	)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							output.images[0]

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



						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/sd2-inpaint/init_image.png'''		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''stabilityai/stable-diffusion-2-inpainting'''
												SCREAMING_SNAKE_CASE							:					Tuple           =							PNDMScheduler.from_pretrained(UpperCamelCase__					,	subfolder='''scheduler'''		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							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()

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							'''Face of a yellow cat, high resolution, sitting on a park bench'''

												SCREAMING_SNAKE_CASE							:					str           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							pipe(
												    prompt=UpperCamelCase__					,	image=UpperCamelCase__					,	mask_image=UpperCamelCase__					,	generator=UpperCamelCase__					,	num_inference_steps=2					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.cuda.max_memory_allocated()
												# make sure that less than 2.65 GB is allocated
												assert mem_bytes < 2.65 * 10**9






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

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


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


__UpperCamelCase	: str    		=  logging.get_logger(__name__)

__UpperCamelCase	: str    		=  {
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""layoutlmv3"""


						def __init__( self			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]=5_0265					,	UpperCamelCase__			:       List[Any]=768					,	UpperCamelCase__			:       Union[str, Any]=12					,	UpperCamelCase__			:       Union[str, Any]=12					,	UpperCamelCase__			:       Tuple=3072					,	UpperCamelCase__			:       List[Any]="gelu"					,	UpperCamelCase__			:       List[Any]=0.1					,	UpperCamelCase__			:       List[str]=0.1					,	UpperCamelCase__			:       Any=512					,	UpperCamelCase__			:       List[Any]=2					,	UpperCamelCase__			:       List[Any]=0.02					,	UpperCamelCase__			:       Optional[Any]=1E-5					,	UpperCamelCase__			:       List[str]=1					,	UpperCamelCase__			:       int=0					,	UpperCamelCase__			:       str=2					,	UpperCamelCase__			:       List[str]=1024					,	UpperCamelCase__			:       str=128					,	UpperCamelCase__			:       str=128					,	UpperCamelCase__			:       Tuple=True					,	UpperCamelCase__			:       Optional[int]=32					,	UpperCamelCase__			:       Any=128					,	UpperCamelCase__			:       Optional[Any]=64					,	UpperCamelCase__			:       Dict=256					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       str=True					,	UpperCamelCase__			:       Dict=True					,	UpperCamelCase__			:       Dict=224					,	UpperCamelCase__			:       Optional[Any]=3					,	UpperCamelCase__			:       Any=16					,	UpperCamelCase__			:       Any=None					,	**UpperCamelCase__			:       Optional[Any]					,	):





												'''simple docstring'''





												super().__init__(
												    vocab_size=UpperCamelCase__					,	hidden_size=UpperCamelCase__					,	num_hidden_layers=UpperCamelCase__					,	num_attention_heads=UpperCamelCase__					,	intermediate_size=UpperCamelCase__					,	hidden_act=UpperCamelCase__					,	hidden_dropout_prob=UpperCamelCase__					,	attention_probs_dropout_prob=UpperCamelCase__					,	max_position_embeddings=UpperCamelCase__					,	type_vocab_size=UpperCamelCase__					,	initializer_range=UpperCamelCase__					,	layer_norm_eps=UpperCamelCase__					,	pad_token_id=UpperCamelCase__					,	bos_token_id=UpperCamelCase__					,	eos_token_id=UpperCamelCase__					,	**UpperCamelCase__					,	)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							max_ad_position_embeddings
												SCREAMING_SNAKE_CASE							:					List[Any]           =							coordinate_size
												SCREAMING_SNAKE_CASE							:					List[str]           =							shape_size
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							has_relative_attention_bias
												SCREAMING_SNAKE_CASE							:					List[Any]           =							rel_pos_bins
												SCREAMING_SNAKE_CASE							:					str           =							max_rel_pos
												SCREAMING_SNAKE_CASE							:					Any           =							has_spatial_attention_bias
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							rel_ad_pos_bins
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							max_rel_ad_pos
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							text_embed
												SCREAMING_SNAKE_CASE							:					List[str]           =							visual_embed
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input_size
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							num_channels
												SCREAMING_SNAKE_CASE							:					List[Any]           =							patch_size
												SCREAMING_SNAKE_CASE							:					List[Any]           =							classifier_dropout






class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	version.parse("""1.12""")


						@property
						def  __A ( self			:       str		):





												'''simple docstring'''





												if self.task in ["question-answering", "sequence-classification"]:
																		return OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
																		    ]		)
												else:
																		return OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
																		    ]		)


						@property
						def  __A ( self			:       int		):





												'''simple docstring'''





												return 1E-5


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       "ProcessorMixin"					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional["TensorType"] = None					,	UpperCamelCase__			:       int = 3					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       int = 40					,	):





												'''simple docstring'''





												setattr(processor.image_processor					,	'''apply_ocr'''					,	UpperCamelCase__		)

												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												SCREAMING_SNAKE_CASE							:					Any           =							compute_effective_axis_dimension(
												    UpperCamelCase__					,	fixed_dimension=OnnxConfig.default_fixed_batch					,	num_token_to_add=0		)
												# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
												SCREAMING_SNAKE_CASE							:					List[Any]           =							processor.tokenizer.num_special_tokens_to_add(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							compute_effective_axis_dimension(
												    UpperCamelCase__					,	fixed_dimension=OnnxConfig.default_fixed_sequence					,	num_token_to_add=UpperCamelCase__		)
												# Generate dummy inputs according to compute batch and sequence
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[[''' '''.join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

												# Generate dummy bounding boxes
												SCREAMING_SNAKE_CASE							:					Any           =							[[[48, 84, 73, 128]]] * batch_size

												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self._generate_dummy_images(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							dict(
												    processor(
												        UpperCamelCase__					,	text=UpperCamelCase__					,	boxes=UpperCamelCase__					,	return_tensors=UpperCamelCase__					,	)		)

												return inputs





