



"""simple docstring"""





import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase					=       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase					=       []
for i in range(6):
					# 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}.cross_attn.out_proj.weight''',
					        F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
					    )
					)
					rename_keys.append(
					    (
					        F'''transformer.decoder.layers.{i}.cross_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'''))

					# q, k, v projections in self/cross-attention in decoder for conditional DETR
					rename_keys.append(
					    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
					)
					rename_keys.append(
					    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
					)
					rename_keys.append(
					    (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
					)
					rename_keys.append(
					    (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
					)
					rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
					rename_keys.append(
					    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
					)
					# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
					rename_keys.append(
					    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
					)
					rename_keys.append(
					    (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
					)
					rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
					rename_keys.append(
					    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
					)

					rename_keys.append(
					    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
					)
					rename_keys.append(
					    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
					)
					rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
					rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
					rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
					rename_keys.append(
					    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
					)
					# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
					rename_keys.append(
					    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
					)
					rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
					rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
					rename_keys.append(
					    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
					)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)




def 						UpperCAmelCase  (		A						:    Any  ,					A						:    Union[str, Any]  ,					A						:    List[Any]							):

		'''simple docstring'''


		_UpperCAmelCase							=		state_dict.pop(A							)
		_UpperCAmelCase							=		val




def 						UpperCAmelCase  (		A						:    Optional[Any]							):

		'''simple docstring'''


		_UpperCAmelCase							=		OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						_UpperCAmelCase							=		key.replace('backbone.0.body'  ,					'backbone.conv_encoder.model'							)
						_UpperCAmelCase							=		value
				else:
						_UpperCAmelCase							=		value

		return new_state_dict




def 						UpperCAmelCase  (		A						:    List[Any]  ,					A						:    Any=False							):

		'''simple docstring'''


		_UpperCAmelCase							=		''
		if is_panoptic:
				_UpperCAmelCase							=		'conditional_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)
				_UpperCAmelCase							=		state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'							)
				_UpperCAmelCase							=		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
				_UpperCAmelCase							=		in_proj_weight[:256, :]
				_UpperCAmelCase							=		in_proj_bias[:256]
				_UpperCAmelCase							=		in_proj_weight[256:512, :]
				_UpperCAmelCase							=		in_proj_bias[256:512]
				_UpperCAmelCase							=		in_proj_weight[-256:, :]
				_UpperCAmelCase							=		in_proj_bias[-256:]




def 						UpperCAmelCase  (		):

		'''simple docstring'''


		_UpperCAmelCase							=		'http://images.cocodataset.org/val2017/000000039769.jpg'
		_UpperCAmelCase							=		Image.open(requests.get(A  ,					stream=A							).raw							)

		return im




@torch.no_grad()
def 						UpperCAmelCase  (		A						:    Dict  ,					A						:    List[str]							):

		'''simple docstring'''


		_UpperCAmelCase							=		ConditionalDetrConfig()
		# set backbone and dilation attributes
		if "resnet101" in model_name:
				_UpperCAmelCase							=		'resnet101'
		if "dc5" in model_name:
				_UpperCAmelCase							=		True
		_UpperCAmelCase							=		'panoptic' in model_name
		if is_panoptic:
				_UpperCAmelCase							=		250
		else:
				_UpperCAmelCase							=		91
				_UpperCAmelCase							=		'huggingface/label-files'
				_UpperCAmelCase							=		'coco-detection-id2label.json'
				_UpperCAmelCase							=		json.load(open(hf_hub_download(A  ,					A  ,					repo_type='dataset'							)  ,					'r'							)							)
				_UpperCAmelCase							=		{int(A							): v for k, v in idalabel.items()}
				_UpperCAmelCase							=		idalabel
				_UpperCAmelCase							=		{v: k for k, v in idalabel.items()}

		# load image processor
		_UpperCAmelCase							=		'coco_panoptic' if is_panoptic else 'coco_detection'
		_UpperCAmelCase							=		ConditionalDetrImageProcessor(format=A							)

		# prepare image
		_UpperCAmelCase							=		prepare_img()
		_UpperCAmelCase							=		image_processor(images=A  ,					return_tensors='pt'							)
		_UpperCAmelCase							=		encoding['pixel_values']

		logger.info(f'Converting model {model_name}...'							)

		# load original model from torch hub
		_UpperCAmelCase							=		torch.hub.load('DeppMeng/ConditionalDETR'  ,					A  ,					pretrained=A							).eval()
		_UpperCAmelCase							=		conditional_detr.state_dict()
		# rename keys
		for src, dest in rename_keys:
				if is_panoptic:
						_UpperCAmelCase							=		'conditional_detr.' + src
				rename_key(A  ,					A  ,					A							)
		_UpperCAmelCase							=		rename_backbone_keys(A							)
		# query, key and value matrices need special treatment
		read_in_q_k_v(A  ,					is_panoptic=A							)
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		_UpperCAmelCase							=		'conditional_detr.model.' if is_panoptic else 'model.'
		for key in state_dict.copy().keys():
				if is_panoptic:
						if (
						    key.startswith('conditional_detr'							)
						    and not key.startswith('class_labels_classifier'							)
						    and not key.startswith('bbox_predictor'							)
						):
								_UpperCAmelCase							=		state_dict.pop(A							)
								_UpperCAmelCase							=		val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								_UpperCAmelCase							=		state_dict.pop(A							)
								_UpperCAmelCase							=		val
						elif key.startswith('bbox_attention'							) or key.startswith('mask_head'							):
								continue
						else:
								_UpperCAmelCase							=		state_dict.pop(A							)
								_UpperCAmelCase							=		val
				else:
						if not key.startswith('class_labels_classifier'							) and not key.startswith('bbox_predictor'							):
								_UpperCAmelCase							=		state_dict.pop(A							)
								_UpperCAmelCase							=		val
    # finally, create HuggingFace model and load state dict
		_UpperCAmelCase							=		ConditionalDetrForSegmentation(A							) if is_panoptic else ConditionalDetrForObjectDetection(A							)
		model.load_state_dict(A							)
		model.eval()
		model.push_to_hub(repo_id=A  ,					organization='DepuMeng'  ,					commit_message='Add model'							)
		# verify our conversion
		_UpperCAmelCase							=		conditional_detr(A							)
		_UpperCAmelCase							=		model(A							)
		assert torch.allclose(outputs.logits  ,					original_outputs['pred_logits']  ,					atol=1e-4							)
		assert torch.allclose(outputs.pred_boxes  ,					original_outputs['pred_boxes']  ,					atol=1e-4							)
		if is_panoptic:
				assert torch.allclose(outputs.pred_masks  ,					original_outputs['pred_masks']  ,					atol=1e-4							)

		# Save model and image processor
		logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'							)
		Path(A							).mkdir(exist_ok=A							)
		model.save_pretrained(A							)
		image_processor.save_pretrained(A							)


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

					parser.add_argument(
					    '''--model_name''',
					    default='''conditional_detr_resnet50''',
					    type=str,
					    help='''Name of the CONDITIONAL_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.'''
					)
					lowercase					=       parser.parse_args()
					convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










"""simple docstring"""
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				return numa ^ numa < 0


if __name__ == "__main__":
			import doctest

			doctest.testmod()

