code: 
  
  
  
  
  """simple docstring"""
  
  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 		=			logging.get_logger(__name__)
  
  
  
  def   UpperCamelCase__     (		):
  		snake_case		:						Any								=				DetaConfig(
  		    num_queries=900       ,     encoder_ffn_dim=2048       ,     decoder_ffn_dim=2048       ,     num_feature_levels=5       ,     assign_first_stage=lowercase__       ,     with_box_refine=lowercase__       ,     two_stage=lowercase__       ,     )
  
  		# set labels
  		snake_case		:						Tuple								=				91
  		snake_case		:						str								=				"huggingface/label-files"
  		snake_case		:						str								=				"coco-detection-id2label.json"
  		snake_case		:						List[Any]								=				json.load(open(cached_download(hf_hub_url(lowercase__       ,     lowercase__       ,     repo_type="dataset"   )   )       ,     "r"   )   )
  		snake_case		:						Any								=				{int(lowercase__   ): v for k, v in idalabel.items()}
  		snake_case		:						Optional[int]								=				idalabel
  		snake_case		:						List[Any]								=				{v: k for k, v in idalabel.items()}
  
  		return config
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Tuple   ):
  		snake_case		:						Optional[int]								=				[]
  
  		# 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__     (		lowercase__  :     str       ,     lowercase__  :     List[str]       ,     lowercase__  :     Optional[Any]   ):
  		snake_case		:						Union[str, Any]								=				dct.pop(lowercase__   )
  		snake_case		:						int								=				val
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     List[str]       ,     lowercase__  :     Tuple   ):
  		# transformer decoder self-attention layers
  		snake_case		:						List[Any]								=				config.d_model
  		for i in range(config.decoder_layers   ):
  				# read in weights + bias of input projection layer of self-attention
  				snake_case		:						Tuple								=				state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''   )
  				snake_case		:						Tuple								=				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
  				snake_case		:						Optional[int]								=				in_proj_weight[:hidden_size, :]
  				snake_case		:						str								=				in_proj_bias[:hidden_size]
  				snake_case		:						Optional[Any]								=				in_proj_weight[
  				    hidden_size : hidden_size * 2, :
  				]
  				snake_case		:						Optional[Any]								=				in_proj_bias[hidden_size : hidden_size * 2]
  				snake_case		:						List[str]								=				in_proj_weight[-hidden_size:, :]
  				snake_case		:						Optional[Any]								=				in_proj_bias[-hidden_size:]
  
  
  
  def   UpperCamelCase__     (		):
  		snake_case		:						List[Any]								=				"http://images.cocodataset.org/val2017/000000039769.jpg"
  		snake_case		:						Union[str, Any]								=				Image.open(requests.get(lowercase__       ,     stream=lowercase__   ).raw   )
  
  		return im
  
  
  
  @torch.no_grad()
  def   UpperCamelCase__     (		lowercase__  :     Dict       ,     lowercase__  :     Tuple       ,     lowercase__  :     Optional[int]   ):
  		snake_case		:						Tuple								=				get_deta_config()
  
  		# load original state dict
  		if model_name == "deta-resnet-50":
  				snake_case		:						Tuple								=				"adet_checkpoint0011.pth"
  		elif model_name == "deta-resnet-50-24-epochs":
  				snake_case		:						Optional[Any]								=				"adet_2x_checkpoint0023.pth"
  		else:
  				raise ValueError(F'''Model name {model_name} not supported'''   )
  		snake_case		:						Union[str, Any]								=				hf_hub_download(repo_id="nielsr/deta-checkpoints"       ,     filename=lowercase__   )
  		snake_case		:						Dict								=				torch.load(lowercase__       ,     map_location="cpu"   )["model"]
  
  		# rename keys
  		snake_case		:						List[Any]								=				create_rename_keys(lowercase__   )
  		for src, dest in rename_keys:
  				rename_key(lowercase__       ,     lowercase__       ,     lowercase__   )
  		read_in_decoder_q_k_v(lowercase__       ,     lowercase__   )
  
  		# fix some prefixes
  		for key in state_dict.copy().keys():
  				if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
  						snake_case		:						Any								=				state_dict.pop(lowercase__   )
  						snake_case		:						Union[str, Any]								=				val
  				if "input_proj" in key:
  						snake_case		:						Optional[int]								=				state_dict.pop(lowercase__   )
  						snake_case		:						List[Any]								=				val
  				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
  						snake_case		:						List[Any]								=				state_dict.pop(lowercase__   )
  						snake_case		:						int								=				val
  
      # finally, create HuggingFace model and load state dict
  		snake_case		:						List[Any]								=				DetaForObjectDetection(lowercase__   )
  		model.load_state_dict(lowercase__   )
  		model.eval()
  
  		snake_case		:						Optional[Any]								=				"cuda" if torch.cuda.is_available() else "cpu"
  		model.to(lowercase__   )
  
  		# load image processor
  		snake_case		:						Any								=				DetaImageProcessor(format="coco_detection"   )
  
  		# verify our conversion on image
  		snake_case		:						List[Any]								=				prepare_img()
  		snake_case		:						str								=				processor(images=lowercase__       ,     return_tensors="pt"   )
  		snake_case		:						Union[str, Any]								=				encoding["pixel_values"]
  		snake_case		:						Union[str, Any]								=				model(pixel_values.to(lowercase__   )   )
  
  		# verify logits
  		if model_name == "deta-resnet-50":
  				snake_case		:						str								=				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]]   )
  				snake_case		:						Union[str, Any]								=				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":
  				snake_case		:						int								=				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]]   )
  				snake_case		:						int								=				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(lowercase__   )       ,     atol=1E-4   )
  		assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,     expected_boxes.to(lowercase__   )       ,     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(lowercase__   ).mkdir(exist_ok=lowercase__   )
  				model.save_pretrained(lowercase__   )
  				processor.save_pretrained(lowercase__   )
  
  		# 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 		=			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 		=			parser.parse_args()
  							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import unittest
  
  import numpy as np
  import PIL
  import torch
  
  from diffusers.image_processor import VaeImageProcessor
  class 			lowerCamelCase__    (  unittest.TestCase			):
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				1
  					snake_case		:						Dict								=				3
  					snake_case		:						int								=				8
  					snake_case		:						int								=				8
  
  					snake_case		:						List[str]								=				torch.rand((batch_size, num_channels, height, width)							)
  
  					return sample
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if isinstance(image[0]				, PIL.Image.Image							):
  							return np.stack([np.array(SCREAMING_SNAKE_CASE							) for i in image]				, axis=0							)
  					elif isinstance(SCREAMING_SNAKE_CASE				, torch.Tensor							):
  							return image.cpu().numpy().transpose(0				, 2				, 3				, 1							)
  					return image
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[int]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Any								=				self.dummy_sample
  					snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
  
  					for output_type in ["pt", "np", "pil"]:
  							snake_case		:						Optional[Any]								=				image_processor.postprocess(
  							    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE				, )
  							snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
  							snake_case		:						Any								=				(input_np * 255).round() if output_type == "pil" else input_np
  							assert (
  							    np.abs(in_np - out_np							).max() < 1E-6
  							), F'''decoded output does not match input for output_type {output_type}'''
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  					snake_case		:						Tuple								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)
  
  					for output_type in ["pt", "np", "pil"]:
  							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)
  
  							snake_case		:						Union[str, Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
  							snake_case		:						Tuple								=				(input_np * 255).round() if output_type == "pil" else input_np
  							assert (
  							    np.abs(in_np - out_np							).max() < 1E-6
  							), F'''decoded output does not match input for output_type {output_type}'''
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)
  					snake_case		:						int								=				image_processor.numpy_to_pil(SCREAMING_SNAKE_CASE							)
  
  					for output_type in ["pt", "np", "pil"]:
  							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)
  							for i, o in zip(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  									snake_case		:						Dict								=				np.array(SCREAMING_SNAKE_CASE							)
  									snake_case		:						Tuple								=				self.to_np(SCREAMING_SNAKE_CASE							) if output_type == "pil" else (self.to_np(SCREAMING_SNAKE_CASE							) * 255).round()
  									assert (
  									    np.abs(in_np - out_np							).max() < 1E-6
  									), F'''decoded output does not match input for output_type {output_type}'''
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Tuple								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						List[str]								=				self.dummy_sample
  					snake_case		:						Optional[int]								=				input_pt_ad.squeeze(0							)
  
  					snake_case		:						Any								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					snake_case		:						Union[str, Any]								=				self.to_np(self.dummy_sample							)
  					snake_case		:						Tuple								=				input_np_ad.squeeze(0							)
  
  					snake_case		:						str								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					assert np.abs(out_pt_ad - out_pt_ad							).max() < 1E-6
  					assert np.abs(out_np_ad - out_np_ad							).max() < 1E-6
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Union[str, Any]								=				self.dummy_sample
  					snake_case		:						List[str]								=				list(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					snake_case		:						Any								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					snake_case		:						Dict								=				self.to_np(self.dummy_sample							)
  					list(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					assert np.abs(out_pt_ad - out_pt_list							).max() < 1E-6
  					assert np.abs(out_np_ad - out_np_list							).max() < 1E-6
  
  