
import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE			:   Dict         =     logging.get_logger(__name__)
def     UpperCAmelCase__							(       __magic_name__						:     Tuple							,  __magic_name__						:     Optional[Any]=False							,  __magic_name__						:     Optional[Any]=False  ):


					'''simple docstring'''

					lowerCAmelCase		:					Union[str, Any]  	=     '''backbone.''' if is_semantic else ''''''

					lowerCAmelCase		:					List[str]  	=     []
					for i in range(config.num_hidden_layers  ):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''')  )
										rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''')  )
										rename_keys.append(
										    (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''')  )
										rename_keys.append(
										    (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''')  )
										rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''')  )
										rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''')  )
										rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''')  )
										rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''')  )
										rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''')  )
										rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''')  )

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        (f'''{prefix}cls_token''', '''beit.embeddings.cls_token'''),
					        (f'''{prefix}patch_embed.proj.weight''', '''beit.embeddings.patch_embeddings.projection.weight'''),
					        (f'''{prefix}patch_embed.proj.bias''', '''beit.embeddings.patch_embeddings.projection.bias'''),
					    ]  )

					if has_lm_head:
										# mask token + shared relative position bias + layernorm
										rename_keys.extend(
										    [
										        ('''mask_token''', '''beit.embeddings.mask_token'''),
										        (
										            '''rel_pos_bias.relative_position_bias_table''',
										            '''beit.encoder.relative_position_bias.relative_position_bias_table''',
										        ),
										        (
										            '''rel_pos_bias.relative_position_index''',
										            '''beit.encoder.relative_position_bias.relative_position_index''',
										        ),
										        ('''norm.weight''', '''layernorm.weight'''),
										        ('''norm.bias''', '''layernorm.bias'''),
										    ]  )
					elif is_semantic:
										# semantic segmentation classification heads
										rename_keys.extend(
										    [
										        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
										        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
										        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
										        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
										    ]  )
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
										        ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
										        ('''head.weight''', '''classifier.weight'''),
										        ('''head.bias''', '''classifier.bias'''),
										    ]  )

					return rename_keys
def     UpperCAmelCase__							(       __magic_name__						:     Tuple							,  __magic_name__						:     Optional[Any]							,  __magic_name__						:     Tuple=False							,  __magic_name__						:     int=False  ):


					'''simple docstring'''

					for i in range(config.num_hidden_layers  ):
										lowerCAmelCase		:					Union[str, Any]  	=     '''backbone.''' if is_semantic else ''''''
										# queries, keys and values
										lowerCAmelCase		:					Tuple  	=     state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''  )
										lowerCAmelCase		:					List[Any]  	=     state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''  )
										lowerCAmelCase		:					Dict  	=     state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''  )

										lowerCAmelCase		:					Optional[Any]  	=     in_proj_weight[
										    : config.hidden_size, :
										]
										lowerCAmelCase		:					Tuple  	=     q_bias
										lowerCAmelCase		:					Union[str, Any]  	=     in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										lowerCAmelCase		:					Union[str, Any]  	=     in_proj_weight[
										    -config.hidden_size :, :
										]
										lowerCAmelCase		:					List[Any]  	=     v_bias

										# gamma_1 and gamma_2
										# we call them lambda because otherwise they are renamed when using .from_pretrained
										lowerCAmelCase		:					str  	=     state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''  )
										lowerCAmelCase		:					Union[str, Any]  	=     state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''  )

										lowerCAmelCase		:					Dict  	=     gamma_a
										lowerCAmelCase		:					Tuple  	=     gamma_a

										# relative_position bias table + index
										if not has_lm_head:
															# each layer has its own relative position bias
															lowerCAmelCase		:					Optional[Any]  	=     state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_bias_table'''  )
															lowerCAmelCase		:					Dict  	=     state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_index'''  )

															lowerCAmelCase		:					str  	=     table
															lowerCAmelCase		:					int  	=     index
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int							,  __magic_name__						:     Tuple  ):


					'''simple docstring'''

					lowerCAmelCase		:					Tuple  	=     dct.pop(__magic_name__  )
					lowerCAmelCase		:					Tuple  	=     val
def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					Optional[Any]  	=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					lowerCAmelCase		:					str  	=     Image.open(requests.get(__magic_name__							,  stream=__magic_name__  ).raw  )
					return im
@torch.no_grad()
def     UpperCAmelCase__							(       __magic_name__						:     Any							,  __magic_name__						:     Union[str, Any]  ):


					'''simple docstring'''

					lowerCAmelCase		:					Any  	=     BeitConfig()
					lowerCAmelCase		:					Tuple  	=     False
					lowerCAmelCase		:					str  	=     False
					lowerCAmelCase		:					List[Any]  	=     '''huggingface/label-files'''
					# set config parameters based on URL
					if checkpoint_url[-9:-4] == "pt22k":
										# masked image modeling
										lowerCAmelCase		:					str  	=     True
										lowerCAmelCase		:					Optional[int]  	=     True
										lowerCAmelCase		:					Tuple  	=     True
					elif checkpoint_url[-9:-4] == "ft22k":
										# intermediate fine-tuning on ImageNet-22k
										lowerCAmelCase		:					str  	=     True
										lowerCAmelCase		:					Optional[int]  	=     2_18_41
										lowerCAmelCase		:					Dict  	=     '''imagenet-22k-id2label.json'''
										lowerCAmelCase		:					int  	=     json.load(open(hf_hub_download(__magic_name__							,  __magic_name__							,  repo_type='''dataset'''  )							,  '''r'''  )  )
										lowerCAmelCase		:					List[str]  	=     {int(__magic_name__  ): v for k, v in idalabel.items()}
										# this dataset contains 21843 labels but the model only has 21841
										# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
										del idalabel[92_05]
										del idalabel[1_50_27]
										lowerCAmelCase		:					Optional[int]  	=     idalabel
										lowerCAmelCase		:					Any  	=     {v: k for k, v in idalabel.items()}
					elif checkpoint_url[-8:-4] == "to1k":
										# fine-tuning on ImageNet-1k
										lowerCAmelCase		:					Tuple  	=     True
										lowerCAmelCase		:					List[Any]  	=     10_00
										lowerCAmelCase		:					int  	=     '''imagenet-1k-id2label.json'''
										lowerCAmelCase		:					List[Any]  	=     json.load(open(hf_hub_download(__magic_name__							,  __magic_name__							,  repo_type='''dataset'''  )							,  '''r'''  )  )
										lowerCAmelCase		:					Any  	=     {int(__magic_name__  ): v for k, v in idalabel.items()}
										lowerCAmelCase		:					Dict  	=     idalabel
										lowerCAmelCase		:					List[Any]  	=     {v: k for k, v in idalabel.items()}
										if "384" in checkpoint_url:
															lowerCAmelCase		:					Tuple  	=     3_84
										if "512" in checkpoint_url:
															lowerCAmelCase		:					List[Any]  	=     5_12
					elif "ade20k" in checkpoint_url:
										# fine-tuning
										lowerCAmelCase		:					Any  	=     True
										lowerCAmelCase		:					Union[str, Any]  	=     1_50
										lowerCAmelCase		:					Any  	=     '''ade20k-id2label.json'''
										lowerCAmelCase		:					Optional[Any]  	=     json.load(open(hf_hub_download(__magic_name__							,  __magic_name__							,  repo_type='''dataset'''  )							,  '''r'''  )  )
										lowerCAmelCase		:					Tuple  	=     {int(__magic_name__  ): v for k, v in idalabel.items()}
										lowerCAmelCase		:					List[str]  	=     idalabel
										lowerCAmelCase		:					Optional[Any]  	=     {v: k for k, v in idalabel.items()}
										lowerCAmelCase		:					Any  	=     6_40
										lowerCAmelCase		:					Tuple  	=     True
					else:
										raise ValueError('''Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''''  )

					# size of the architecture
					if "base" in checkpoint_url:
										pass
					elif "large" in checkpoint_url:
										lowerCAmelCase		:					Optional[int]  	=     10_24
										lowerCAmelCase		:					Tuple  	=     40_96
										lowerCAmelCase		:					Dict  	=     24
										lowerCAmelCase		:					Dict  	=     16
										if "ade20k" in checkpoint_url:
															lowerCAmelCase		:					Dict  	=     6_40
															lowerCAmelCase		:					str  	=     [7, 11, 15, 23]
					else:
										raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''  )

					# load state_dict of original model, remove and rename some keys
					lowerCAmelCase		:					Any  	=     torch.hub.load_state_dict_from_url(__magic_name__							,  map_location='''cpu'''							,  check_hash=__magic_name__  )
					lowerCAmelCase		:					Optional[int]  	=     state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

					lowerCAmelCase		:					Any  	=     create_rename_keys(__magic_name__							,  has_lm_head=__magic_name__							,  is_semantic=__magic_name__  )
					for src, dest in rename_keys:
										rename_key(__magic_name__							,  __magic_name__							,  __magic_name__  )
					read_in_q_k_v(__magic_name__							,  __magic_name__							,  has_lm_head=__magic_name__							,  is_semantic=__magic_name__  )
					if is_semantic:
										# add prefix to decoder keys
										for key, val in state_dict.copy().items():
															lowerCAmelCase		:					Any  	=     state_dict.pop(__magic_name__  )
															if key.startswith('''backbone.fpn'''  ):
																				lowerCAmelCase		:					str  	=     key.replace('''backbone.fpn'''							,  '''fpn'''  )
															lowerCAmelCase		:					Optional[int]  	=     val

    # load HuggingFace model
					if checkpoint_url[-9:-4] == "pt22k":
										lowerCAmelCase		:					str  	=     BeitForMaskedImageModeling(__magic_name__  )
					elif "ade20k" in checkpoint_url:
										lowerCAmelCase		:					Optional[int]  	=     BeitForSemanticSegmentation(__magic_name__  )
					else:
										lowerCAmelCase		:					Optional[Any]  	=     BeitForImageClassification(__magic_name__  )
					model.eval()
					model.load_state_dict(__magic_name__  )

					# Check outputs on an image
					if is_semantic:
										lowerCAmelCase		:					Any  	=     BeitImageProcessor(size=config.image_size							,  do_center_crop=__magic_name__  )
										lowerCAmelCase		:					Optional[Any]  	=     load_dataset('''hf-internal-testing/fixtures_ade20k'''							,  split='''test'''  )
										lowerCAmelCase		:					Union[str, Any]  	=     Image.open(ds[0]['''file''']  )
					else:
										lowerCAmelCase		:					Optional[int]  	=     BeitImageProcessor(
										    size=config.image_size							,  resample=PILImageResampling.BILINEAR							,  do_center_crop=__magic_name__  )
										lowerCAmelCase		:					Optional[int]  	=     prepare_img()

					lowerCAmelCase		:					int  	=     image_processor(images=__magic_name__							,  return_tensors='''pt'''  )
					lowerCAmelCase		:					Optional[int]  	=     encoding['''pixel_values''']

					lowerCAmelCase		:					Any  	=     model(__magic_name__  )
					lowerCAmelCase		:					Tuple  	=     outputs.logits

					# verify logits
					lowerCAmelCase		:					List[str]  	=     torch.Size([1, 10_00]  )
					if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''  ):
										lowerCAmelCase		:					Dict  	=     torch.Size([1, 1_96, 81_92]  )
					elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''  ):
										lowerCAmelCase		:					str  	=     torch.Size([1, 1_96, 81_92]  )
					elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''  ):
										lowerCAmelCase		:					Dict  	=     torch.Size([1, 2_18_41]  )
										lowerCAmelCase		:					str  	=     torch.tensor([2.2288, 2.4671, 0.7395]  )
										lowerCAmelCase		:					Dict  	=     23_97
					elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''  ):
										lowerCAmelCase		:					Tuple  	=     torch.Size([1, 2_18_41]  )
										lowerCAmelCase		:					int  	=     torch.tensor([1.6881, -0.2787, 0.5901]  )
										lowerCAmelCase		:					Tuple  	=     23_96
					elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''  ):
										lowerCAmelCase		:					Optional[int]  	=     torch.tensor([0.1241, 0.0798, -0.6569]  )
										lowerCAmelCase		:					List[str]  	=     2_85
					elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''  ):
										lowerCAmelCase		:					List[Any]  	=     torch.tensor([-1.2385, -1.0987, -1.0108]  )
										lowerCAmelCase		:					List[Any]  	=     2_81
					elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''  ):
										lowerCAmelCase		:					Optional[int]  	=     torch.tensor([-1.5303, -0.9484, -0.3147]  )
										lowerCAmelCase		:					List[Any]  	=     7_61
					elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''  ):
										lowerCAmelCase		:					Any  	=     torch.tensor([0.4610, -0.0928, 0.2086]  )
										lowerCAmelCase		:					Optional[int]  	=     7_61
					elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''  ):
										lowerCAmelCase		:					str  	=     torch.tensor([-0.4804, 0.6257, -0.1837]  )
										lowerCAmelCase		:					Union[str, Any]  	=     7_61
					elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''  ):
										lowerCAmelCase		:					Dict  	=     torch.tensor([[-0.5122, 0.5117, -0.2113]]  )
										lowerCAmelCase		:					Optional[int]  	=     7_61
					elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''  ):
										lowerCAmelCase		:					Optional[Any]  	=     torch.tensor([-0.3062, 0.7261, 0.4852]  )
										lowerCAmelCase		:					Any  	=     7_61
					elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''  ):
										lowerCAmelCase		:					str  	=     (1, 1_50, 1_60, 1_60)
										lowerCAmelCase		:					Dict  	=     torch.tensor(
										    [
										        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
										        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
										        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
										    ]  )
					elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''  ):
										lowerCAmelCase		:					str  	=     (1, 1_50, 1_60, 1_60)
										lowerCAmelCase		:					Any  	=     torch.tensor(
										    [
										        [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
										        [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
										        [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
										    ]  )
					else:
										raise ValueError('''Can\'t verify logits as model is not supported'''  )

					assert logits.shape == expected_shape, "Shape of logits not as expected"
					if not has_lm_head:
										if is_semantic:
															assert torch.allclose(
															    logits[0, :3, :3, :3]							,  __magic_name__							,  atol=1e-3  ), "First elements of logits not as expected"
										else:
															print('''Predicted class idx:'''							,  logits.argmax(-1  ).item()  )
															assert torch.allclose(
															    logits[0, :3]							,  __magic_name__							,  atol=1e-3  ), "First elements of logits not as expected"
															assert logits.argmax(-1  ).item() == expected_class_idx, "Predicted class index not as expected"

					Path(__magic_name__  ).mkdir(exist_ok=__magic_name__  )
					print(f'''Saving model to {pytorch_dump_folder_path}'''  )
					model.save_pretrained(__magic_name__  )
					print(f'''Saving image processor to {pytorch_dump_folder_path}'''  )
					image_processor.save_pretrained(__magic_name__  )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE			:   List[Any]         =     argparse.ArgumentParser()

		parser.add_argument(
		    '--checkpoint_url',
		    default='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth',
		    type=str,
		    help='URL to the original PyTorch checkpoint (.pth file).',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
		)
		__SCREAMING_SNAKE_CASE			:   int         =     parser.parse_args()
		convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


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 		__magic_name__   (						snake_case,				unittest.TestCase    ):
		_lowerCAmelCase		=	PaintByExamplePipeline
		_lowerCAmelCase		=	IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
		_lowerCAmelCase		=	IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		_lowerCAmelCase		=	frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess





		def       _A							(							self    :	Dict				):
							torch.manual_seed(0				)
							lowerCAmelCase		:					Optional[int]  	=     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		:					List[str]  	=     PNDMScheduler(skip_prk_steps=lowerCamelCase__				)
							torch.manual_seed(0				)
							lowerCAmelCase		:					List[str]  	=     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		:					Union[str, Any]  	=     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		:					List[Any]  	=     PaintByExampleImageEncoder(lowerCamelCase__						,  proj_size=3_2				)
							lowerCAmelCase		:					Union[str, Any]  	=     CLIPImageProcessor(crop_size=3_2						,  size=3_2				)

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





		def       _A							(							self    :	str						,  lowerCamelCase__    :	List[Any]				):
							lowerCAmelCase		:					List[Any]  	=     np.array(image.convert('''RGB'''				)				)
							lowerCAmelCase		:					int  	=     image[None].transpose(0						,  3						,  1						,  2				)
							lowerCAmelCase		:					Any  	=     torch.from_numpy(lowerCamelCase__				).to(dtype=torch.floataa				) / 1_2_7.5 - 1.0
							return image





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

							if str(lowerCamelCase__				).startswith('''mps'''				):
												lowerCAmelCase		:					Dict  	=     torch.manual_seed(lowerCamelCase__				)
							else:
												lowerCAmelCase		:					str  	=     torch.Generator(device=lowerCamelCase__				).manual_seed(lowerCamelCase__				)
							lowerCAmelCase		:					Union[str, Any]  	=     {
							    '''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       _A							(							self    :	List[Any]				):
							lowerCAmelCase		:					Tuple  	=     self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase		:					Union[str, Any]  	=     PaintByExamplePipeline(**lowerCamelCase__				)
							lowerCAmelCase		:					List[Any]  	=     pipe.to('''cpu'''				)
							pipe.set_progress_bar_config(disable=lowerCamelCase__				)

							lowerCAmelCase		:					Tuple  	=     self.get_dummy_inputs()
							lowerCAmelCase		:					List[str]  	=     pipe(**lowerCamelCase__				)
							lowerCAmelCase		:					Optional[Any]  	=     output.images

							lowerCAmelCase		:					Optional[int]  	=     image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							lowerCAmelCase		:					List[Any]  	=     np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]				)

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





		def       _A							(							self    :	List[str]				):
							lowerCAmelCase		:					List[str]  	=     '''cpu'''
							lowerCAmelCase		:					Union[str, Any]  	=     self.get_dummy_inputs()
							inputs.pop('''mask_image'''				)
							lowerCAmelCase		:					Union[str, Any]  	=     self.convert_to_pt(inputs.pop('''image'''				)				)
							lowerCAmelCase		:					Dict  	=     image.clamp(0						,  1				) / 2

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase		:					str  	=     PaintByExamplePipeline(**self.get_dummy_components()				)
							lowerCAmelCase		:					Tuple  	=     pipe.to(lowerCamelCase__				)
							pipe.set_progress_bar_config(disable=lowerCamelCase__				)

							lowerCAmelCase		:					Optional[int]  	=     pipe(image=lowerCamelCase__						,  mask_image=mask_image[:, 0]						,  **lowerCamelCase__				)
							lowerCAmelCase		:					Optional[int]  	=     output.images

							lowerCAmelCase		:					int  	=     image.cpu().permute(0						,  2						,  3						,  1				)[0]
							lowerCAmelCase		:					int  	=     mask_image.cpu().permute(0						,  2						,  3						,  1				)[0]

							lowerCAmelCase		:					List[str]  	=     Image.fromarray(np.uinta(lowerCamelCase__				)				).convert('''RGB'''				)
							lowerCAmelCase		:					str  	=     Image.fromarray(np.uinta(lowerCamelCase__				)				).convert('''RGB'''				)

							lowerCAmelCase		:					List[Any]  	=     pipe(**self.get_dummy_inputs()				)
							lowerCAmelCase		:					Any  	=     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       _A							(							self    :	Optional[Any]				):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3				)



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





		def       _A							(							self    :	Tuple				):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





		def       _A							(							self    :	Optional[int]				):
							# make sure here that pndm scheduler skips prk
							lowerCAmelCase		:					Union[str, Any]  	=     load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/paint_by_example/dog_in_bucket.png'''				)
							lowerCAmelCase		:					Any  	=     load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/paint_by_example/mask.png'''				)
							lowerCAmelCase		:					Any  	=     load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/paint_by_example/panda.jpg'''				)

							lowerCAmelCase		:					Union[str, Any]  	=     PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''				)
							lowerCAmelCase		:					str  	=     pipe.to(lowerCamelCase__				)
							pipe.set_progress_bar_config(disable=lowerCamelCase__				)

							lowerCAmelCase		:					int  	=     torch.manual_seed(3_2_1				)
							lowerCAmelCase		:					List[str]  	=     pipe(
							    image=lowerCamelCase__						,  mask_image=lowerCamelCase__						,  example_image=lowerCamelCase__						,  generator=lowerCamelCase__						,  guidance_scale=5.0						,  num_inference_steps=5_0						,  output_type='''np'''						,  )

							lowerCAmelCase		:					Any  	=     output.images

							lowerCAmelCase		:					Tuple  	=     image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							lowerCAmelCase		:					Dict  	=     np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]				)

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