
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A		:   Any         =    logging.get_logger(__name__)




def   UpperCamelCase__     (    )     ->    List[str]:
						_lowercase									=				DetaConfig(
						    num_queries=9_00		,      encoder_ffn_dim=20_48		,      decoder_ffn_dim=20_48		,      num_feature_levels=5		,      assign_first_stage=SCREAMING_SNAKE_CASE_		,      with_box_refine=SCREAMING_SNAKE_CASE_		,      two_stage=SCREAMING_SNAKE_CASE_		,      )

						# set labels
						_lowercase									=				91
						_lowercase									=				"""huggingface/label-files"""
						_lowercase									=				"""coco-detection-id2label.json"""
						_lowercase									=				json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      repo_type="""dataset"""					)					)		,      """r"""					)					)
						_lowercase									=				{int(SCREAMING_SNAKE_CASE_					): v for k, v in idalabel.items()}
						_lowercase									=				idalabel
						_lowercase									=				{v: k for k, v in idalabel.items()}

						return config




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  List[Any]					)     ->    int:
						_lowercase									=				[]

						# stem
						# fmt: off
						rename_keys.append(("""backbone.0.body.conv1.weight""", """model.backbone.model.embedder.embedder.convolution.weight""")					)
						rename_keys.append(("""backbone.0.body.bn1.weight""", """model.backbone.model.embedder.embedder.normalization.weight""")					)
						rename_keys.append(("""backbone.0.body.bn1.bias""", """model.backbone.model.embedder.embedder.normalization.bias""")					)
						rename_keys.append(("""backbone.0.body.bn1.running_mean""", """model.backbone.model.embedder.embedder.normalization.running_mean""")					)
						rename_keys.append(("""backbone.0.body.bn1.running_var""", """model.backbone.model.embedder.embedder.normalization.running_var""")					)
						# stages
						for stage_idx in range(len(config.backbone_config.depths					)					):
												for layer_idx in range(config.backbone_config.depths[stage_idx]					):
																		# shortcut
																		if layer_idx == 0:
																								rename_keys.append(
																								    (
																								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
																								        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
																								    )					)
																								rename_keys.append(
																								    (
																								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
																								        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
																								    )					)
																								rename_keys.append(
																								    (
																								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
																								        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
																								    )					)
																								rename_keys.append(
																								    (
																								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
																								        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
																								    )					)
																								rename_keys.append(
																								    (
																								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
																								        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
																								    )					)
																		# 3 convs
																		for i in range(3					):
																								rename_keys.append(
																								    (
																								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
																								        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
																								    )					)
																								rename_keys.append(
																								    (
																								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
																								        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
																								    )					)
																								rename_keys.append(
																								    (
																								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
																								        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
																								    )					)
																								rename_keys.append(
																								    (
																								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
																								        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
																								    )					)
																								rename_keys.append(
																								    (
																								        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
																								        f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
																								    )					)
    # transformer encoder
						for i in range(config.encoder_layers					):
												rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")					)
												rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")					)
												rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", f"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")					)
												rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", f"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")					)
												rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", f"""model.encoder.layers.{i}.self_attn.value_proj.weight""")					)
												rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", f"""model.encoder.layers.{i}.self_attn.value_proj.bias""")					)
												rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", f"""model.encoder.layers.{i}.self_attn.output_proj.weight""")					)
												rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", f"""model.encoder.layers.{i}.self_attn.output_proj.bias""")					)
												rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.weight""", f"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")					)
												rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")					)
												rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""model.encoder.layers.{i}.fc1.weight""")					)
												rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""model.encoder.layers.{i}.fc1.bias""")					)
												rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""model.encoder.layers.{i}.fc2.weight""")					)
												rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""model.encoder.layers.{i}.fc2.bias""")					)
												rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""model.encoder.layers.{i}.final_layer_norm.weight""")					)
												rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""model.encoder.layers.{i}.final_layer_norm.bias""")					)

						# transformer decoder
						for i in range(config.decoder_layers					):
												rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.weight""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""model.decoder.layers.{i}.self_attn.out_proj.weight""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""model.decoder.layers.{i}.self_attn.out_proj.bias""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.weight""", f"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.bias""", f"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""model.decoder.layers.{i}.fc1.weight""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""model.decoder.layers.{i}.fc1.bias""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""model.decoder.layers.{i}.fc2.weight""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""model.decoder.layers.{i}.fc2.bias""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""model.decoder.layers.{i}.final_layer_norm.weight""")					)
												rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""model.decoder.layers.{i}.final_layer_norm.bias""")					)

						# fmt: on

						return rename_keys




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Union[str, Any]		,      SCREAMING_SNAKE_CASE_  :  Tuple		,      SCREAMING_SNAKE_CASE_  :  List[Any]					)     ->    Union[str, Any]:
						_lowercase									=				dct.pop(SCREAMING_SNAKE_CASE_					)
						_lowercase									=				val




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Optional[int]		,      SCREAMING_SNAKE_CASE_  :  int					)     ->    List[str]:
						# transformer decoder self-attention layers
						_lowercase									=				config.d_model
						for i in range(config.decoder_layers					):
												# read in weights + bias of input projection layer of self-attention
												_lowercase									=				state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
												_lowercase									=				state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""					)
												# next, add query, keys and values (in that order) to the state dict
												_lowercase									=				in_proj_weight[:hidden_size, :]
												_lowercase									=				in_proj_bias[:hidden_size]
												_lowercase									=				in_proj_weight[
												    hidden_size : hidden_size * 2, :
												]
												_lowercase									=				in_proj_bias[hidden_size : hidden_size * 2]
												_lowercase									=				in_proj_weight[-hidden_size:, :]
												_lowercase									=				in_proj_bias[-hidden_size:]




def   UpperCamelCase__     (    )     ->    Dict:
						_lowercase									=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						_lowercase									=				Image.open(requests.get(SCREAMING_SNAKE_CASE_		,      stream=SCREAMING_SNAKE_CASE_					).raw					)

						return im




@torch.no_grad()
def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Union[str, Any]		,      SCREAMING_SNAKE_CASE_  :  Union[str, Any]		,      SCREAMING_SNAKE_CASE_  :  Optional[int]					)     ->    Any:
						_lowercase									=				get_deta_config()

						# load original state dict
						if model_name == "deta-resnet-50":
												_lowercase									=				"""adet_checkpoint0011.pth"""
						elif model_name == "deta-resnet-50-24-epochs":
												_lowercase									=				"""adet_2x_checkpoint0023.pth"""
						else:
												raise ValueError(f"""Model name {model_name} not supported"""					)
						_lowercase									=				hf_hub_download(repo_id="""nielsr/deta-checkpoints"""		,      filename=SCREAMING_SNAKE_CASE_					)
						_lowercase									=				torch.load(SCREAMING_SNAKE_CASE_		,      map_location="""cpu"""					)["""model"""]

						# rename keys
						_lowercase									=				create_rename_keys(SCREAMING_SNAKE_CASE_					)
						for src, dest in rename_keys:
												rename_key(SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_					)
						read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_					)

						# fix some prefixes
						for key in state_dict.copy().keys():
												if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
																		_lowercase									=				state_dict.pop(SCREAMING_SNAKE_CASE_					)
																		_lowercase									=				val
												if "input_proj" in key:
																		_lowercase									=				state_dict.pop(SCREAMING_SNAKE_CASE_					)
																		_lowercase									=				val
												if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																		_lowercase									=				state_dict.pop(SCREAMING_SNAKE_CASE_					)
																		_lowercase									=				val

    # finally, create HuggingFace model and load state dict
						_lowercase									=				DetaForObjectDetection(SCREAMING_SNAKE_CASE_					)
						model.load_state_dict(SCREAMING_SNAKE_CASE_					)
						model.eval()

						_lowercase									=				"""cuda""" if torch.cuda.is_available() else """cpu"""
						model.to(SCREAMING_SNAKE_CASE_					)

						# load image processor
						_lowercase									=				DetaImageProcessor(format="""coco_detection"""					)

						# verify our conversion on image
						_lowercase									=				prepare_img()
						_lowercase									=				processor(images=SCREAMING_SNAKE_CASE_		,      return_tensors="""pt"""					)
						_lowercase									=				encoding["""pixel_values"""]
						_lowercase									=				model(pixel_values.to(SCREAMING_SNAKE_CASE_					)					)

						# verify logits
						if model_name == "deta-resnet-50":
												_lowercase									=				torch.tensor(
												    [[-7.39_78, -2.54_06, -4.16_68], [-8.26_84, -3.99_33, -3.80_96], [-7.05_15, -3.79_73, -5.85_16]]					)
												_lowercase									=				torch.tensor([[0.50_43, 0.49_73, 0.99_98], [0.25_42, 0.54_89, 0.47_48], [0.54_90, 0.27_65, 0.05_70]]					)
						elif model_name == "deta-resnet-50-24-epochs":
												_lowercase									=				torch.tensor(
												    [[-7.16_88, -2.48_57, -4.86_69], [-7.86_30, -3.81_54, -4.26_74], [-7.27_30, -4.18_65, -5.53_23]]					)
												_lowercase									=				torch.tensor([[0.50_21, 0.49_71, 0.99_94], [0.25_46, 0.54_86, 0.47_31], [0.16_86, 0.19_86, 0.21_42]]					)

						assert torch.allclose(outputs.logits[0, :3, :3]		,      expected_logits.to(SCREAMING_SNAKE_CASE_					)		,      atol=1E-4					)
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,      expected_boxes.to(SCREAMING_SNAKE_CASE_					)		,      atol=1E-4					)
						print("""Everything ok!"""					)

						if pytorch_dump_folder_path:
												# Save model and processor
												logger.info(f"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""					)
												Path(SCREAMING_SNAKE_CASE_					).mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)
												model.save_pretrained(SCREAMING_SNAKE_CASE_					)
												processor.save_pretrained(SCREAMING_SNAKE_CASE_					)

						# Push to hub
						if push_to_hub:
												print("""Pushing model and processor to hub..."""					)
												model.push_to_hub(f"""jozhang97/{model_name}"""					)
												processor.push_to_hub(f"""jozhang97/{model_name}"""					)


if __name__ == "__main__":
		A		:   List[str]         =    argparse.ArgumentParser()

		parser.add_argument(
		    '''--model_name''',
		    type=str,
		    default='''deta-resnet-50''',
		    choices=['''deta-resnet-50''', '''deta-resnet-50-24-epochs'''],
		    help='''Name of the model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    type=str,
		    help='''Path to the folder to output PyTorch model.''',
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)
		A		:   Tuple         =    parser.parse_args()
		convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()



class       a_   ( _a				, _a				, _a				, unittest.TestCase						):
			a  : Optional[int]				      =       StableDiffusionControlNetInpaintPipeline
			a  : Union[str, Any]				      =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			a  : List[Any]				      =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			a  : int				      =       frozenset({'''control_image'''}						)  # skip `image` and `mask` for now, only test for control_image
			a  : int				      =       TEXT_TO_IMAGE_IMAGE_PARAMS






			def       UpperCamelCase_							(			self     ):
									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							, )
									torch.manual_seed(0     )
									_lowercase									=				ControlNetModel(
									    block_out_channels=(32, 64)							, layers_per_block=2							, in_channels=4							, down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")							, cross_attention_dim=32							, conditioning_embedding_out_channels=(16, 32)							, )
									torch.manual_seed(0     )
									_lowercase									=				DDIMScheduler(
									    beta_start=0.0_00_85							, beta_end=0.0_12							, beta_schedule="""scaled_linear"""							, clip_sample=__UpperCamelCase							, set_alpha_to_one=__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							, )
									torch.manual_seed(0     )
									_lowercase									=				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							, )
									_lowercase									=				CLIPTextModel(__UpperCamelCase     )
									_lowercase									=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

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






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase=0     ):
									if str(__UpperCamelCase     ).startswith("""mps"""     ):
															_lowercase									=				torch.manual_seed(__UpperCamelCase     )
									else:
															_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(__UpperCamelCase     )

									_lowercase									=				2
									_lowercase									=				randn_tensor(
									    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)							, generator=__UpperCamelCase							, device=torch.device(__UpperCamelCase     )							, )
									_lowercase									=				floats_tensor((1, 3, 32, 32)							, rng=random.Random(__UpperCamelCase     )     ).to(__UpperCamelCase     )
									_lowercase									=				init_image.cpu().permute(0							, 2							, 3							, 1     )[0]

									_lowercase									=				Image.fromarray(np.uinta(__UpperCamelCase     )     ).convert("""RGB"""     ).resize((64, 64)     )
									_lowercase									=				Image.fromarray(np.uinta(init_image + 4     )     ).convert("""RGB"""     ).resize((64, 64)     )

									_lowercase									=				{
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									    """image""": image,
									    """mask_image""": mask_image,
									    """control_image""": control_image,
									}

									return inputs






			def       UpperCamelCase_							(			self     ):
									return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3     )






			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()							, reason="""XFormers attention is only available with CUDA and `xformers` installed"""							, )
			def       UpperCamelCase_							(			self     ):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3     )






			def       UpperCamelCase_							(			self     ):
									self._test_inference_batch_single_identical(expected_max_diff=2E-3     )







class       a_   ( _a						):
			a  : List[str]				      =       StableDiffusionControlNetInpaintPipeline
			a  : Any				      =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			a  : int				      =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			a  : Union[str, Any]				      =       frozenset([]						)






			def       UpperCamelCase_							(			self     ):
									torch.manual_seed(0     )
									_lowercase									=				UNetaDConditionModel(
									    block_out_channels=(32, 64)							, layers_per_block=2							, sample_size=32							, in_channels=4							, out_channels=4							, down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")							, up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")							, cross_attention_dim=32							, )
									torch.manual_seed(0     )
									_lowercase									=				ControlNetModel(
									    block_out_channels=(32, 64)							, layers_per_block=2							, in_channels=4							, down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")							, cross_attention_dim=32							, conditioning_embedding_out_channels=(16, 32)							, )
									torch.manual_seed(0     )
									_lowercase									=				DDIMScheduler(
									    beta_start=0.0_00_85							, beta_end=0.0_12							, beta_schedule="""scaled_linear"""							, clip_sample=__UpperCamelCase							, set_alpha_to_one=__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							, )
									torch.manual_seed(0     )
									_lowercase									=				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							, )
									_lowercase									=				CLIPTextModel(__UpperCamelCase     )
									_lowercase									=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

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







class       a_   ( _a				, _a				, unittest.TestCase						):
			a  : str				      =       StableDiffusionControlNetInpaintPipeline
			a  : Any				      =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			a  : Optional[Any]				      =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






			def       UpperCamelCase_							(			self     ):
									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							, )
									torch.manual_seed(0     )

									def init_weights(__UpperCamelCase     ):
															if isinstance(__UpperCamelCase							, torch.nn.Convad     ):
																					torch.nn.init.normal(m.weight     )
																					m.bias.data.fill_(1.0     )

									_lowercase									=				ControlNetModel(
									    block_out_channels=(32, 64)							, layers_per_block=2							, in_channels=4							, down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")							, cross_attention_dim=32							, conditioning_embedding_out_channels=(16, 32)							, )
									controlneta.controlnet_down_blocks.apply(__UpperCamelCase     )

									torch.manual_seed(0     )
									_lowercase									=				ControlNetModel(
									    block_out_channels=(32, 64)							, layers_per_block=2							, in_channels=4							, down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")							, cross_attention_dim=32							, conditioning_embedding_out_channels=(16, 32)							, )
									controlneta.controlnet_down_blocks.apply(__UpperCamelCase     )

									torch.manual_seed(0     )
									_lowercase									=				DDIMScheduler(
									    beta_start=0.0_00_85							, beta_end=0.0_12							, beta_schedule="""scaled_linear"""							, clip_sample=__UpperCamelCase							, set_alpha_to_one=__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							, )
									torch.manual_seed(0     )
									_lowercase									=				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							, )
									_lowercase									=				CLIPTextModel(__UpperCamelCase     )
									_lowercase									=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

									_lowercase									=				MultiControlNetModel([controlneta, controlneta]     )

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






			def       UpperCamelCase_							(			self							, __UpperCamelCase							, __UpperCamelCase=0     ):
									if str(__UpperCamelCase     ).startswith("""mps"""     ):
															_lowercase									=				torch.manual_seed(__UpperCamelCase     )
									else:
															_lowercase									=				torch.Generator(device=__UpperCamelCase     ).manual_seed(__UpperCamelCase     )

									_lowercase									=				2

									_lowercase									=				[
									    randn_tensor(
									        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)							, generator=__UpperCamelCase							, device=torch.device(__UpperCamelCase     )							, ),
									    randn_tensor(
									        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)							, generator=__UpperCamelCase							, device=torch.device(__UpperCamelCase     )							, ),
									]
									_lowercase									=				floats_tensor((1, 3, 32, 32)							, rng=random.Random(__UpperCamelCase     )     ).to(__UpperCamelCase     )
									_lowercase									=				init_image.cpu().permute(0							, 2							, 3							, 1     )[0]

									_lowercase									=				Image.fromarray(np.uinta(__UpperCamelCase     )     ).convert("""RGB"""     ).resize((64, 64)     )
									_lowercase									=				Image.fromarray(np.uinta(init_image + 4     )     ).convert("""RGB"""     ).resize((64, 64)     )

									_lowercase									=				{
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									    """image""": image,
									    """mask_image""": mask_image,
									    """control_image""": control_image,
									}

									return inputs






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.get_dummy_components()
									_lowercase									=				self.pipeline_class(**__UpperCamelCase     )
									pipe.to(__UpperCamelCase     )

									_lowercase									=				10.0
									_lowercase									=				4

									_lowercase									=				self.get_dummy_inputs(__UpperCamelCase     )
									_lowercase									=				steps
									_lowercase									=				scale
									_lowercase									=				pipe(**__UpperCamelCase     )[0]

									_lowercase									=				self.get_dummy_inputs(__UpperCamelCase     )
									_lowercase									=				steps
									_lowercase									=				scale
									_lowercase									=				pipe(**__UpperCamelCase							, control_guidance_start=0.1							, control_guidance_end=0.2     )[0]

									_lowercase									=				self.get_dummy_inputs(__UpperCamelCase     )
									_lowercase									=				steps
									_lowercase									=				scale
									_lowercase									=				pipe(**__UpperCamelCase							, control_guidance_start=[0.1, 0.3]							, control_guidance_end=[0.2, 0.7]     )[0]

									_lowercase									=				self.get_dummy_inputs(__UpperCamelCase     )
									_lowercase									=				steps
									_lowercase									=				scale
									_lowercase									=				pipe(**__UpperCamelCase							, control_guidance_start=0.4							, control_guidance_end=[0.5, 0.8]     )[0]

									# make sure that all outputs are different
									assert np.sum(np.abs(output_a - output_a     )     ) > 1E-3
									assert np.sum(np.abs(output_a - output_a     )     ) > 1E-3
									assert np.sum(np.abs(output_a - output_a     )     ) > 1E-3






			def       UpperCamelCase_							(			self     ):
									return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3     )






			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()							, reason="""XFormers attention is only available with CUDA and `xformers` installed"""							, )
			def       UpperCamelCase_							(			self     ):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3     )






			def       UpperCamelCase_							(			self     ):
									self._test_inference_batch_single_identical(expected_max_diff=2E-3     )






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				self.get_dummy_components()
									_lowercase									=				self.pipeline_class(**__UpperCamelCase     )
									pipe.to(__UpperCamelCase     )
									pipe.set_progress_bar_config(disable=__UpperCamelCase     )
									with tempfile.TemporaryDirectory() as tmpdir:
															try:
																					# save_pretrained is not implemented for Multi-ControlNet
																					pipe.save_pretrained(__UpperCamelCase     )
															except NotImplementedError:
																					pass







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






			def       UpperCamelCase_							(			self     ):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""     )

									_lowercase									=				StableDiffusionControlNetInpaintPipeline.from_pretrained(
									    """runwayml/stable-diffusion-inpainting"""							, safety_checker=__UpperCamelCase							, controlnet=__UpperCamelCase     )
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				torch.Generator(device="""cpu"""     ).manual_seed(0     )
									_lowercase									=				load_image(
									    """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""     ).resize((512, 512)     )

									_lowercase									=				load_image(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
									    """/stable_diffusion_inpaint/input_bench_mask.png"""     ).resize((512, 512)     )

									_lowercase									=				"""pitch black hole"""

									_lowercase									=				load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""     ).resize((512, 512)     )

									_lowercase									=				pipe(
									    __UpperCamelCase							, image=__UpperCamelCase							, mask_image=__UpperCamelCase							, control_image=__UpperCamelCase							, generator=__UpperCamelCase							, output_type="""np"""							, num_inference_steps=3							, )

									_lowercase									=				output.images[0]

									assert image.shape == (512, 512, 3)

									_lowercase									=				load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"""     )

									assert np.abs(expected_image - image     ).max() < 9E-2






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint"""     )

									_lowercase									=				StableDiffusionControlNetInpaintPipeline.from_pretrained(
									    """runwayml/stable-diffusion-v1-5"""							, safety_checker=__UpperCamelCase							, controlnet=__UpperCamelCase     )
									_lowercase									=				DDIMScheduler.from_config(pipe.scheduler.config     )
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=__UpperCamelCase     )

									_lowercase									=				torch.Generator(device="""cpu"""     ).manual_seed(33     )

									_lowercase									=				load_image(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"""     )
									_lowercase									=				init_image.resize((512, 512)     )

									_lowercase									=				load_image(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"""     )
									_lowercase									=				mask_image.resize((512, 512)     )

									_lowercase									=				"""a handsome man with ray-ban sunglasses"""

									def make_inpaint_condition(__UpperCamelCase							, __UpperCamelCase     ):
															_lowercase									=				np.array(image.convert("""RGB"""     )     ).astype(np.floataa     ) / 2_55.0
															_lowercase									=				np.array(image_mask.convert("""L"""     )     ).astype(np.floataa     ) / 2_55.0

															assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
															_lowercase									=				-1.0  # set as masked pixel
															_lowercase									=				np.expand_dims(__UpperCamelCase							, 0     ).transpose(0							, 3							, 1							, 2     )
															_lowercase									=				torch.from_numpy(__UpperCamelCase     )
															return image

									_lowercase									=				make_inpaint_condition(__UpperCamelCase							, __UpperCamelCase     )

									_lowercase									=				pipe(
									    __UpperCamelCase							, image=__UpperCamelCase							, mask_image=__UpperCamelCase							, control_image=__UpperCamelCase							, guidance_scale=9.0							, eta=1.0							, generator=__UpperCamelCase							, num_inference_steps=20							, output_type="""np"""							, )
									_lowercase									=				output.images[0]

									assert image.shape == (512, 512, 3)

									_lowercase									=				load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"""     )

									assert np.abs(expected_image - image     ).max() < 9E-2