


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


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__a      			= []
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__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     state_dict.pop(_UpperCamelCase			)
						UpperCAmelCase__												=     val






def        a__			(							_UpperCamelCase			):





						"""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        a__			(							_UpperCamelCase						,      _UpperCamelCase=False			):





						"""simple docstring"""

						UpperCAmelCase__												=     ''''''
						if is_panoptic:
												UpperCAmelCase__												=     '''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:]
						# 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
												UpperCAmelCase__												=     state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""			)
												UpperCAmelCase__												=     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
												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:]
												# read in weights + bias of input projection layer of cross-attention
												UpperCAmelCase__												=     state_dict.pop(
												    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""			)
												UpperCAmelCase__												=     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
												UpperCAmelCase__												=     in_proj_weight_cross_attn[:256, :]
												UpperCAmelCase__												=     in_proj_bias_cross_attn[:256]
												UpperCAmelCase__												=     in_proj_weight_cross_attn[256:512, :]
												UpperCAmelCase__												=     in_proj_bias_cross_attn[256:512]
												UpperCAmelCase__												=     in_proj_weight_cross_attn[-256:, :]
												UpperCAmelCase__												=     in_proj_bias_cross_attn[-256:]






def        a__			(							):





						"""simple docstring"""

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

						return im






@torch.no_grad()
def        a__			(							_UpperCamelCase						,      _UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     DetrConfig()
						# 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(_UpperCamelCase						,      _UpperCamelCase						,      repo_type='''dataset'''			)						,      '''r'''			)			)
												UpperCAmelCase__												=     {int(_UpperCamelCase			): 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__												=     DetrImageProcessor(format=_UpperCamelCase			)

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

						logger.info(F"""Converting model {model_name}..."""			)

						# load original model from torch hub
						UpperCAmelCase__												=     torch.hub.load('''facebookresearch/detr'''						,      _UpperCamelCase						,      pretrained=_UpperCamelCase			).eval()
						UpperCAmelCase__												=     detr.state_dict()
						# rename keys
						for src, dest in rename_keys:
												if is_panoptic:
																		UpperCAmelCase__												=     '''detr.''' + src
												rename_key(_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase			)
						UpperCAmelCase__												=     rename_backbone_keys(_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
						UpperCAmelCase__												=     '''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'''			)
																		):
																								UpperCAmelCase__												=     state_dict.pop(_UpperCamelCase			)
																								UpperCAmelCase__												=     val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								UpperCAmelCase__												=     state_dict.pop(_UpperCamelCase			)
																								UpperCAmelCase__												=     val
																		elif key.startswith('''bbox_attention'''			) or key.startswith('''mask_head'''			):
																								continue
																		else:
																								UpperCAmelCase__												=     state_dict.pop(_UpperCamelCase			)
																								UpperCAmelCase__												=     val
												else:
																		if not key.startswith('''class_labels_classifier'''			) and not key.startswith('''bbox_predictor'''			):
																								UpperCAmelCase__												=     state_dict.pop(_UpperCamelCase			)
																								UpperCAmelCase__												=     val
    # finally, create HuggingFace model and load state dict
						UpperCAmelCase__												=     DetrForSegmentation(_UpperCamelCase			) if is_panoptic else DetrForObjectDetection(_UpperCamelCase			)
						model.load_state_dict(_UpperCamelCase			)
						model.eval()
						# verify our conversion
						UpperCAmelCase__												=     detr(_UpperCamelCase			)
						UpperCAmelCase__												=     model(_UpperCamelCase			)
						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(_UpperCamelCase			).mkdir(exist_ok=_UpperCamelCase			)
						model.save_pretrained(_UpperCamelCase			)
						image_processor.save_pretrained(_UpperCamelCase			)


if __name__ == "__main__":
						__a      			= 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."
						)
						__a      			= parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)



"""simple docstring"""







from __future__ import annotations






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     sorted(_UpperCamelCase			)
						UpperCAmelCase__												=     len(_UpperCamelCase			)
						UpperCAmelCase__												=     length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)






def        a__			(							):





						"""simple docstring"""

						import doctest

						doctest.testmod()


if __name__ == "__main__":
						main()