
"""simple docstring"""







import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowerCamelCase__           =	logging.get_logger(__name__)  # pylint: disable=invalid-name
def 					__lowerCAmelCase       (_UpperCamelCase	):
	warnings.warn(
	    'The preprocess method is deprecated and will be removed in a future version. Please'
	    ' use VaeImageProcessor.preprocess instead' ,						_UpperCamelCase ,						)
	if isinstance(_UpperCamelCase ,						torch.Tensor	):
		return image
	elif isinstance(_UpperCamelCase ,						PIL.Image.Image	):
		__lowerCAmelCase    :		Union[str, Any]		    =   [image]

	if isinstance(image[0] ,						PIL.Image.Image	):
		__lowerCAmelCase    ,     __lowerCAmelCase    :		Dict		    =   image[0].size
		__lowerCAmelCase    ,     __lowerCAmelCase    :		Tuple		    =   (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

		__lowerCAmelCase    :		Tuple		    =   [np.array(i.resize((w, h) ,						resample=PIL_INTERPOLATION['lanczos']	)	)[None, :] for i in image]
		__lowerCAmelCase    :		str		    =   np.concatenate(_UpperCamelCase ,						axis=0	)
		__lowerCAmelCase    :		List[str]		    =   np.array(_UpperCamelCase	).astype(np.floataa	) / 255.0
		__lowerCAmelCase    :		Tuple		    =   image.transpose(0 ,						3 ,						1 ,						2	)
		__lowerCAmelCase    :		Any		    =   2.0 * image - 1.0
		__lowerCAmelCase    :		List[str]		    =   torch.from_numpy(_UpperCamelCase	)
	elif isinstance(image[0] ,						torch.Tensor	):
		__lowerCAmelCase    :		Union[str, Any]		    =   torch.cat(_UpperCamelCase ,						dim=0	)
	return image
def 					__lowerCAmelCase       (_UpperCamelCase	):
	if isinstance(_UpperCamelCase ,						torch.Tensor	):
		return mask
	elif isinstance(_UpperCamelCase ,						PIL.Image.Image	):
		__lowerCAmelCase    :		List[Any]		    =   [mask]

	if isinstance(mask[0] ,						PIL.Image.Image	):
		__lowerCAmelCase    ,     __lowerCAmelCase    :		Union[str, Any]		    =   mask[0].size
		__lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[Any]		    =   (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		__lowerCAmelCase    :		List[Any]		    =   [np.array(m.convert('L'	).resize((w, h) ,						resample=PIL_INTERPOLATION['nearest']	)	)[None, :] for m in mask]
		__lowerCAmelCase    :		Dict		    =   np.concatenate(_UpperCamelCase ,						axis=0	)
		__lowerCAmelCase    :		Dict		    =   mask.astype(np.floataa	) / 255.0
		__lowerCAmelCase    :		Optional[int]		    =   0
		__lowerCAmelCase    :		List[Any]		    =   1
		__lowerCAmelCase    :		int		    =   torch.from_numpy(_UpperCamelCase	)
	elif isinstance(mask[0] ,						torch.Tensor	):
		__lowerCAmelCase    :		int		    =   torch.cat(_UpperCamelCase ,						dim=0	)
	return mask




class 				A__ (			_lowerCamelCase):
		A_		: UNetaDModel
		A_		: RePaintScheduler

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			super().__init__()
			self.register_modules(unet=_SCREAMING_SNAKE_CASE ,	scheduler=_SCREAMING_SNAKE_CASE				)







		@torch.no_grad()
		def __call__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = 2_50 ,	_SCREAMING_SNAKE_CASE = 0.0 ,	_SCREAMING_SNAKE_CASE = 10 ,	_SCREAMING_SNAKE_CASE = 10 ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = "pil" ,	_SCREAMING_SNAKE_CASE = True ,	):
			__lowerCAmelCase    :		Tuple		    =   image

			__lowerCAmelCase    :		str		    =   _preprocess_image(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Any		    =   original_image.to(device=self.device ,	dtype=self.unet.dtype				)
			__lowerCAmelCase    :		List[Any]		    =   _preprocess_mask(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Any		    =   mask_image.to(device=self.device ,	dtype=self.unet.dtype				)

			__lowerCAmelCase    :		Dict		    =   original_image.shape[0]

			# sample gaussian noise to begin the loop
			if isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) and len(_SCREAMING_SNAKE_CASE				) != batch_size:
				raise ValueError(
				    f"You have passed a list of generators of length {len(_SCREAMING_SNAKE_CASE				)}, but requested an effective batch"
				    f" size of {batch_size}. Make sure the batch size matches the length of the generators."				)

			__lowerCAmelCase    :		Any		    =   original_image.shape
			__lowerCAmelCase    :		Dict		    =   randn_tensor(_SCREAMING_SNAKE_CASE ,	generator=_SCREAMING_SNAKE_CASE ,	device=self.device ,	dtype=self.unet.dtype				)

			# set step values
			self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	self.device				)
			__lowerCAmelCase    :		Tuple		    =   eta

			__lowerCAmelCase    :		Optional[Any]		    =   self.scheduler.timesteps[0] + 1
			__lowerCAmelCase    :		Optional[int]		    =   generator[0] if isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) else generator
			for i, t in enumerate(self.progress_bar(self.scheduler.timesteps				)				):
				if t < t_last:
					# predict the noise residual
					__lowerCAmelCase    :		Dict		    =   self.unet(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				).sample
					# compute previous image: x_t -> x_t-1
					__lowerCAmelCase    :		Tuple		    =   self.scheduler.step(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				).prev_sample

				else:
					# compute the reverse: x_t-1 -> x_t
					__lowerCAmelCase    :		str		    =   self.scheduler.undo_step(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		Optional[Any]		    =   t

			__lowerCAmelCase    :		Optional[int]		    =   (image / 2 + 0.5).clamp(0 ,	1				)
			__lowerCAmelCase    :		Tuple		    =   image.cpu().permute(0 ,	2 ,	3 ,	1				).numpy()
			if output_type == "pil":
				__lowerCAmelCase    :		Dict		    =   self.numpy_to_pil(_SCREAMING_SNAKE_CASE				)

			if not return_dict:
				return (image,)

			return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE				)

"""simple docstring"""







import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__           =	logging.get_logger(__name__)
def 					__lowerCAmelCase       (_UpperCamelCase	):
	# initialize config
	if "resnet-50" in model_name:
		__lowerCAmelCase    :		int		    =   ResNetConfig.from_pretrained('microsoft/resnet-50'	)
	elif "resnet-101" in model_name:
		__lowerCAmelCase    :		Union[str, Any]		    =   ResNetConfig.from_pretrained('microsoft/resnet-101'	)
	else:
		raise ValueError('Model name should include either resnet50 or resnet101'	)

	__lowerCAmelCase    :		Union[str, Any]		    =   DetrConfig(use_timm_backbone=_UpperCamelCase ,						backbone_config=_UpperCamelCase	)

	# set label attributes
	__lowerCAmelCase    :		Optional[int]		    =   'panoptic' in model_name
	if is_panoptic:
		__lowerCAmelCase    :		Dict		    =   250
	else:
		__lowerCAmelCase    :		List[str]		    =   91
		__lowerCAmelCase    :		Tuple		    =   'huggingface/label-files'
		__lowerCAmelCase    :		str		    =   'coco-detection-id2label.json'
		__lowerCAmelCase    :		Optional[int]		    =   json.load(open(hf_hub_download(_UpperCamelCase ,						_UpperCamelCase ,						repo_type='dataset'	) ,						'r'	)	)
		__lowerCAmelCase    :		List[Any]		    =   {int(_UpperCamelCase	): v for k, v in idalabel.items()}
		__lowerCAmelCase    :		List[Any]		    =   idalabel
		__lowerCAmelCase    :		Dict		    =   {v: k for k, v in idalabel.items()}

	return config, is_panoptic
def 					__lowerCAmelCase       (_UpperCamelCase	):
	# here we list all keys to be renamed (original name on the left, our name on the right)
	__lowerCAmelCase    :		Tuple		    =   []

	# stem
	# fmt: off
	rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')	)
	rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')	)
	rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')	)
	rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')	)
	rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
				    )	)
    # fmt: on

	for i in range(config.encoder_layers	):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (
		        F"transformer.encoder.layers.{i}.self_attn.out_proj.weight",
		        F"encoder.layers.{i}.self_attn.out_proj.weight",
		    )	)
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")	)
		rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight")	)
		rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias")	)
		rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight")	)
		rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias")	)
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")	)
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias")	)
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight")	)
		rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias")	)
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (
		        F"transformer.decoder.layers.{i}.self_attn.out_proj.weight",
		        F"decoder.layers.{i}.self_attn.out_proj.weight",
		    )	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")	)
		rename_keys.append(
		    (
		        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
		        F"decoder.layers.{i}.encoder_attn.out_proj.weight",
		    )	)
		rename_keys.append(
		    (
		        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
		        F"decoder.layers.{i}.encoder_attn.out_proj.bias",
		    )	)
		rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight")	)
		rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias")	)
		rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight")	)
		rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias")	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias")	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")	)
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight")	)
		rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias")	)

	# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
	rename_keys.extend(
	    [
	        ('input_proj.weight', 'input_projection.weight'),
	        ('input_proj.bias', 'input_projection.bias'),
	        ('query_embed.weight', 'query_position_embeddings.weight'),
	        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
	        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
	        ('class_embed.weight', 'class_labels_classifier.weight'),
	        ('class_embed.bias', 'class_labels_classifier.bias'),
	        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
	        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
	        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
	        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
	        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
	        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
	    ]	)

	return rename_keys
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		str		    =   state_dict.pop(_UpperCamelCase	)
	__lowerCAmelCase    :		Dict		    =   val
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=False	):
	__lowerCAmelCase    :		Tuple		    =   ''
	if is_panoptic:
		__lowerCAmelCase    :		Union[str, Any]		    =   'detr.'

	# first: transformer encoder
	for i in range(6	):
		# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
		__lowerCAmelCase    :		Optional[int]		    =   state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"	)
		__lowerCAmelCase    :		List[Any]		    =   state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"	)
		# next, add query, keys and values (in that order) to the state dict
		__lowerCAmelCase    :		int		    =   in_proj_weight[:256, :]
		__lowerCAmelCase    :		Tuple		    =   in_proj_bias[:256]
		__lowerCAmelCase    :		Dict		    =   in_proj_weight[256:512, :]
		__lowerCAmelCase    :		Optional[int]		    =   in_proj_bias[256:512]
		__lowerCAmelCase    :		int		    =   in_proj_weight[-256:, :]
		__lowerCAmelCase    :		int		    =   in_proj_bias[-256:]
	# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
	for i in range(6	):
		# read in weights + bias of input projection layer of self-attention
		__lowerCAmelCase    :		int		    =   state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"	)
		__lowerCAmelCase    :		List[str]		    =   state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"	)
		# next, add query, keys and values (in that order) to the state dict
		__lowerCAmelCase    :		List[str]		    =   in_proj_weight[:256, :]
		__lowerCAmelCase    :		int		    =   in_proj_bias[:256]
		__lowerCAmelCase    :		Any		    =   in_proj_weight[256:512, :]
		__lowerCAmelCase    :		Any		    =   in_proj_bias[256:512]
		__lowerCAmelCase    :		Any		    =   in_proj_weight[-256:, :]
		__lowerCAmelCase    :		Optional[int]		    =   in_proj_bias[-256:]
		# read in weights + bias of input projection layer of cross-attention
		__lowerCAmelCase    :		Union[str, Any]		    =   state_dict.pop(
		    F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"	)
		__lowerCAmelCase    :		Optional[Any]		    =   state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"	)
		# next, add query, keys and values (in that order) of cross-attention to the state dict
		__lowerCAmelCase    :		List[Any]		    =   in_proj_weight_cross_attn[:256, :]
		__lowerCAmelCase    :		Union[str, Any]		    =   in_proj_bias_cross_attn[:256]
		__lowerCAmelCase    :		Optional[Any]		    =   in_proj_weight_cross_attn[256:512, :]
		__lowerCAmelCase    :		Any		    =   in_proj_bias_cross_attn[256:512]
		__lowerCAmelCase    :		str		    =   in_proj_weight_cross_attn[-256:, :]
		__lowerCAmelCase    :		Optional[int]		    =   in_proj_bias_cross_attn[-256:]
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		List[str]		    =   'http://images.cocodataset.org/val2017/000000039769.jpg'
	__lowerCAmelCase    :		Any		    =   Image.open(requests.get(_UpperCamelCase ,						stream=_UpperCamelCase	).raw	)

	return im
@torch.no_grad()
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=None ,						_UpperCamelCase=False	):
	__lowerCAmelCase    ,     __lowerCAmelCase    :		int		    =   get_detr_config(_UpperCamelCase	)

	# load original model from torch hub
	__lowerCAmelCase    :		List[str]		    =   {
	    'detr-resnet-50': 'detr_resnet50',
	    'detr-resnet-101': 'detr_resnet101',
	}
	logger.info(F"Converting model {model_name}..."	)
	__lowerCAmelCase    :		List[Any]		    =   torch.hub.load('facebookresearch/detr' ,						model_name_to_original_name[model_name] ,						pretrained=_UpperCamelCase	).eval()
	__lowerCAmelCase    :		str		    =   detr.state_dict()
	# rename keys
	for src, dest in create_rename_keys(_UpperCamelCase	):
		if is_panoptic:
			__lowerCAmelCase    :		List[str]		    =   'detr.' + src
		rename_key(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)
	# query, key and value matrices need special treatment
	read_in_q_k_v(_UpperCamelCase ,						is_panoptic=_UpperCamelCase	)
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	__lowerCAmelCase    :		Optional[Any]		    =   'detr.model.' if is_panoptic else 'model.'
	for key in state_dict.copy().keys():
		if is_panoptic:
			if (
			    key.startswith('detr'	)
			    and not key.startswith('class_labels_classifier'	)
			    and not key.startswith('bbox_predictor'	)
			):
				__lowerCAmelCase    :		Optional[Any]		    =   state_dict.pop(_UpperCamelCase	)
				__lowerCAmelCase    :		Tuple		    =   val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				__lowerCAmelCase    :		List[Any]		    =   state_dict.pop(_UpperCamelCase	)
				__lowerCAmelCase    :		Union[str, Any]		    =   val
			elif key.startswith('bbox_attention'	) or key.startswith('mask_head'	):
				continue
			else:
				__lowerCAmelCase    :		Optional[Any]		    =   state_dict.pop(_UpperCamelCase	)
				__lowerCAmelCase    :		Union[str, Any]		    =   val
		else:
			if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
				__lowerCAmelCase    :		Dict		    =   state_dict.pop(_UpperCamelCase	)
				__lowerCAmelCase    :		Optional[int]		    =   val

    # finally, create HuggingFace model and load state dict
	__lowerCAmelCase    :		Tuple		    =   DetrForSegmentation(_UpperCamelCase	) if is_panoptic else DetrForObjectDetection(_UpperCamelCase	)
	model.load_state_dict(_UpperCamelCase	)
	model.eval()

	# verify our conversion on an image
	__lowerCAmelCase    :		Optional[Any]		    =   'coco_panoptic' if is_panoptic else 'coco_detection'
	__lowerCAmelCase    :		List[str]		    =   DetrImageProcessor(format=_UpperCamelCase	)

	__lowerCAmelCase    :		Union[str, Any]		    =   processor(images=prepare_img() ,						return_tensors='pt'	)
	__lowerCAmelCase    :		Optional[int]		    =   encoding['pixel_values']

	__lowerCAmelCase    :		Optional[Any]		    =   detr(_UpperCamelCase	)
	__lowerCAmelCase    :		Union[str, Any]		    =   model(_UpperCamelCase	)

	assert torch.allclose(outputs.logits ,						original_outputs['pred_logits'] ,						atol=1e-3	)
	assert torch.allclose(outputs.pred_boxes ,						original_outputs['pred_boxes'] ,						atol=1e-3	)
	if is_panoptic:
		assert torch.allclose(outputs.pred_masks ,						original_outputs['pred_masks'] ,						atol=1e-4	)
	print('Looks ok!'	)

	if pytorch_dump_folder_path is not None:
		# Save model and image processor
		logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."	)
		Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
		model.save_pretrained(_UpperCamelCase	)
		processor.save_pretrained(_UpperCamelCase	)

	if push_to_hub:
		# Upload model and image processor to the hub
		logger.info('Uploading PyTorch model and image processor to the hub...'	)
		model.push_to_hub(F"nielsr/{model_name}"	)
		processor.push_to_hub(F"nielsr/{model_name}"	)


if __name__ == "__main__":
							lowerCamelCase__           =	argparse.ArgumentParser()

							parser.add_argument(
							    """--model_name""",
							    default="""detr-resnet-50""",
							    type=str,
							    choices=["""detr-resnet-50""", """detr-resnet-101"""],
							    help="""Name of the DETR 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 to push the model to the hub or not.""")
							lowerCamelCase__           =	parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)