


'''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()
lowerCamelCase__     						=  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase__     						=  []
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 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							List[Any]             =   state_dict.pop(__lowerCAmelCase )
		_UpperCAmelCase		:							Optional[int]             =   val






def 			__lowerCAmelCase			(__lowerCAmelCase ):
		_UpperCAmelCase		:							str             =   OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						_UpperCAmelCase		:							Optional[int]             =   key.replace("backbone.0.body"   ,			"backbone.conv_encoder.model" )
						_UpperCAmelCase		:							Union[str, Any]             =   value
				else:
						_UpperCAmelCase		:							Tuple             =   value

		return new_state_dict






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase=False ):
		_UpperCAmelCase		:							Dict             =   ""
		if is_panoptic:
				_UpperCAmelCase		:							List[str]             =   "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		:							List[str]             =   state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
				_UpperCAmelCase		:							Tuple             =   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		:							Union[str, Any]             =   in_proj_weight[:256, :]
				_UpperCAmelCase		:							Dict             =   in_proj_bias[:256]
				_UpperCAmelCase		:							int             =   in_proj_weight[256:512, :]
				_UpperCAmelCase		:							Tuple             =   in_proj_bias[256:512]
				_UpperCAmelCase		:							int             =   in_proj_weight[-256:, :]
				_UpperCAmelCase		:							List[Any]             =   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		:							Tuple             =   state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
				_UpperCAmelCase		:							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
				_UpperCAmelCase		:							int             =   in_proj_weight[:256, :]
				_UpperCAmelCase		:							Dict             =   in_proj_bias[:256]
				_UpperCAmelCase		:							Union[str, Any]             =   in_proj_weight[256:512, :]
				_UpperCAmelCase		:							Tuple             =   in_proj_bias[256:512]
				_UpperCAmelCase		:							List[Any]             =   in_proj_weight[-256:, :]
				_UpperCAmelCase		:							Optional[int]             =   in_proj_bias[-256:]
				# read in weights + bias of input projection layer of cross-attention
				_UpperCAmelCase		:							List[str]             =   state_dict.pop(
				    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""" )
				_UpperCAmelCase		:							List[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
				_UpperCAmelCase		:							Any             =   in_proj_weight_cross_attn[:256, :]
				_UpperCAmelCase		:							Tuple             =   in_proj_bias_cross_attn[:256]
				_UpperCAmelCase		:							str             =   in_proj_weight_cross_attn[256:512, :]
				_UpperCAmelCase		:							Optional[int]             =   in_proj_bias_cross_attn[256:512]
				_UpperCAmelCase		:							str             =   in_proj_weight_cross_attn[-256:, :]
				_UpperCAmelCase		:							List[Any]             =   in_proj_bias_cross_attn[-256:]






def 			__lowerCAmelCase			():
		_UpperCAmelCase		:							Dict             =   "http://images.cocodataset.org/val2017/000000039769.jpg"
		_UpperCAmelCase		:							Dict             =   Image.open(requests.get(__lowerCAmelCase   ,			stream=__lowerCAmelCase ).raw )

		return im






@torch.no_grad()
def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							Union[str, Any]             =   DetrConfig()
		# set backbone and dilation attributes
		if "resnet101" in model_name:
				_UpperCAmelCase		:							Tuple             =   "resnet101"
		if "dc5" in model_name:
				_UpperCAmelCase		:							Optional[int]             =   True
		_UpperCAmelCase		:							List[Any]             =   "panoptic" in model_name
		if is_panoptic:
				_UpperCAmelCase		:							Optional[Any]             =   250
		else:
				_UpperCAmelCase		:							int             =   91
				_UpperCAmelCase		:							Optional[Any]             =   "huggingface/label-files"
				_UpperCAmelCase		:							int             =   "coco-detection-id2label.json"
				_UpperCAmelCase		:							Union[str, Any]             =   json.load(open(hf_hub_download(__lowerCAmelCase   ,			__lowerCAmelCase   ,			repo_type="dataset" )   ,			"r" ) )
				_UpperCAmelCase		:							str             =   {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
				_UpperCAmelCase		:							Union[str, Any]             =   idalabel
				_UpperCAmelCase		:							Optional[Any]             =   {v: k for k, v in idalabel.items()}

		# load image processor
		_UpperCAmelCase		:							Union[str, Any]             =   "coco_panoptic" if is_panoptic else "coco_detection"
		_UpperCAmelCase		:							Dict             =   DetrImageProcessor(format=__lowerCAmelCase )

		# prepare image
		_UpperCAmelCase		:							Tuple             =   prepare_img()
		_UpperCAmelCase		:							Union[str, Any]             =   image_processor(images=__lowerCAmelCase   ,			return_tensors="pt" )
		_UpperCAmelCase		:							Tuple             =   encoding["pixel_values"]

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

		# load original model from torch hub
		_UpperCAmelCase		:							List[str]             =   torch.hub.load("facebookresearch/detr"   ,			__lowerCAmelCase   ,			pretrained=__lowerCAmelCase ).eval()
		_UpperCAmelCase		:							Optional[Any]             =   detr.state_dict()
		# rename keys
		for src, dest in rename_keys:
				if is_panoptic:
						_UpperCAmelCase		:							List[Any]             =   "detr." + src
				rename_key(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase )
		_UpperCAmelCase		:							Dict             =   rename_backbone_keys(__lowerCAmelCase )
		# query, key and value matrices need special treatment
		read_in_q_k_v(__lowerCAmelCase   ,			is_panoptic=__lowerCAmelCase )
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		_UpperCAmelCase		:							Union[str, 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" )
						):
								_UpperCAmelCase		:							str             =   state_dict.pop(__lowerCAmelCase )
								_UpperCAmelCase		:							Union[str, Any]             =   val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								_UpperCAmelCase		:							List[Any]             =   state_dict.pop(__lowerCAmelCase )
								_UpperCAmelCase		:							List[Any]             =   val
						elif key.startswith("bbox_attention" ) or key.startswith("mask_head" ):
								continue
						else:
								_UpperCAmelCase		:							int             =   state_dict.pop(__lowerCAmelCase )
								_UpperCAmelCase		:							str             =   val
				else:
						if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
								_UpperCAmelCase		:							Any             =   state_dict.pop(__lowerCAmelCase )
								_UpperCAmelCase		:							Optional[Any]             =   val
    # finally, create HuggingFace model and load state dict
		_UpperCAmelCase		:							List[Any]             =   DetrForSegmentation(__lowerCAmelCase ) if is_panoptic else DetrForObjectDetection(__lowerCAmelCase )
		model.load_state_dict(__lowerCAmelCase )
		model.eval()
		# verify our conversion
		_UpperCAmelCase		:							Tuple             =   detr(__lowerCAmelCase )
		_UpperCAmelCase		:							Union[str, Any]             =   model(__lowerCAmelCase )
		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(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
		model.save_pretrained(__lowerCAmelCase )
		image_processor.save_pretrained(__lowerCAmelCase )


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







'''simple docstring'''


def 			__lowerCAmelCase			(__lowerCAmelCase ):
		return "".join(chr(ord(__lowerCAmelCase ) + 32 ) if "A" <= char <= "Z" else char for char in word )


if __name__ == "__main__":
						from doctest import testmod

						testmod()



