






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


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase   :						Dict							=  []
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}.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"""),
    ]
)
def        a_	( _A ,				_A ,				_A					)     ->     Any:







				"""simple docstring"""
				snake_case__													=       state_dict.pop(_A					)
				snake_case__													=       val
def        a_	( _A					)     ->     Union[str, Any]:







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

				return new_state_dict
def        a_	( _A ,				_A=False					)     ->     int:







				"""simple docstring"""
				snake_case__													=       ''
				if is_panoptic:
								snake_case__													=       '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)
								snake_case__													=       state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''					)
								snake_case__													=       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
								snake_case__													=       in_proj_weight[:256, :]
								snake_case__													=       in_proj_bias[:256]
								snake_case__													=       in_proj_weight[256:512, :]
								snake_case__													=       in_proj_bias[256:512]
								snake_case__													=       in_proj_weight[-256:, :]
								snake_case__													=       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
								snake_case__													=       state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''					)
								snake_case__													=       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
								snake_case__													=       in_proj_weight[:256, :]
								snake_case__													=       in_proj_bias[:256]
								snake_case__													=       in_proj_weight[256:512, :]
								snake_case__													=       in_proj_bias[256:512]
								snake_case__													=       in_proj_weight[-256:, :]
								snake_case__													=       in_proj_bias[-256:]
								# read in weights + bias of input projection layer of cross-attention
								snake_case__													=       state_dict.pop(
								    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''					)
								snake_case__													=       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
								snake_case__													=       in_proj_weight_cross_attn[:256, :]
								snake_case__													=       in_proj_bias_cross_attn[:256]
								snake_case__													=       in_proj_weight_cross_attn[256:512, :]
								snake_case__													=       in_proj_bias_cross_attn[256:512]
								snake_case__													=       in_proj_weight_cross_attn[-256:, :]
								snake_case__													=       in_proj_bias_cross_attn[-256:]
def        a_	( )     ->     Union[str, Any]:







				"""simple docstring"""
				snake_case__													=       'http://images.cocodataset.org/val2017/000000039769.jpg'
				snake_case__													=       Image.open(requests.get(_A ,				stream=_A					).raw					)

				return im
@torch.no_grad()
def        a_	( _A ,				_A					)     ->     List[Any]:







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

				# load image processor
				snake_case__													=       'coco_panoptic' if is_panoptic else 'coco_detection'
				snake_case__													=       DetrImageProcessor(format=_A					)

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

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

				# load original model from torch hub
				snake_case__													=       torch.hub.load('facebookresearch/detr' ,				_A ,				pretrained=_A					).eval()
				snake_case__													=       detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												snake_case__													=       'detr.' + src
								rename_key(_A ,				_A ,				_A					)
				snake_case__													=       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
				snake_case__													=       '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'					)
												):
																snake_case__													=       state_dict.pop(_A					)
																snake_case__													=       val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																snake_case__													=       state_dict.pop(_A					)
																snake_case__													=       val
												elif key.startswith('bbox_attention'					) or key.startswith('mask_head'					):
																continue
												else:
																snake_case__													=       state_dict.pop(_A					)
																snake_case__													=       val
								else:
												if not key.startswith('class_labels_classifier'					) and not key.startswith('bbox_predictor'					):
																snake_case__													=       state_dict.pop(_A					)
																snake_case__													=       val
    # finally, create HuggingFace model and load state dict
				snake_case__													=       DetrForSegmentation(_A					) if is_panoptic else DetrForObjectDetection(_A					)
				model.load_state_dict(_A					)
				model.eval()
				# verify our conversion
				snake_case__													=       detr(_A					)
				snake_case__													=       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__":
							__UpperCamelCase   :						Tuple							=  argparse.ArgumentParser()

							parser.add_argument(
							    """--model_name""", default="""detr_resnet50""", type=str, 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."""
							)
							__UpperCamelCase   :						int							=  parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)












def        a_	( _A ,				_A					)     ->     Union[str, Any]:







				"""simple docstring"""
				snake_case__													=       1
				while b > 0:
								if b & 1:
												res *= a

								a *= a
								b >>= 1

				return res
def        a_	( _A ,				_A ,				_A					)     ->     Dict:







				"""simple docstring"""
				snake_case__													=       1
				while b > 0:
								if b & 1:
												snake_case__													=       ((res % c) * (a % c)) % c

								a *= a
								b >>= 1

				return res




