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 :     Dict          =  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase :     int          =  []
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 				_snake_case     (       lowerCAmelCase							:       str       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       Dict     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int    				=   state_dict.pop(lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Any    				=   val




def 				_snake_case     (       lowerCAmelCase							:       Any     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]    				=   OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						SCREAMING_SNAKE_CASE_  :		Any    				=   key.replace("backbone.0.body"       ,			"backbone.conv_encoder.model"     )
						SCREAMING_SNAKE_CASE_  :		str    				=   value
				else:
						SCREAMING_SNAKE_CASE_  :		str    				=   value

		return new_state_dict




def 				_snake_case     (       lowerCAmelCase							:       int       ,			lowerCAmelCase							:       Optional[Any]=False     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   ""
		if is_panoptic:
				SCREAMING_SNAKE_CASE_  :		Optional[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)
				SCREAMING_SNAKE_CASE_  :		str    				=   state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'     )
				SCREAMING_SNAKE_CASE_  :		Union[str, 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
				SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   in_proj_weight[:2_5_6, :]
				SCREAMING_SNAKE_CASE_  :		int    				=   in_proj_bias[:2_5_6]
				SCREAMING_SNAKE_CASE_  :		List[str]    				=   in_proj_weight[2_5_6:5_1_2, :]
				SCREAMING_SNAKE_CASE_  :		Dict    				=   in_proj_bias[2_5_6:5_1_2]
				SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   in_proj_weight[-2_5_6:, :]
				SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   in_proj_bias[-2_5_6:]
		# 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
				SCREAMING_SNAKE_CASE_  :		str    				=   state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'     )
				SCREAMING_SNAKE_CASE_  :		List[Any]    				=   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
				SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   in_proj_weight[:2_5_6, :]
				SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   in_proj_bias[:2_5_6]
				SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   in_proj_weight[2_5_6:5_1_2, :]
				SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   in_proj_bias[2_5_6:5_1_2]
				SCREAMING_SNAKE_CASE_  :		Dict    				=   in_proj_weight[-2_5_6:, :]
				SCREAMING_SNAKE_CASE_  :		List[str]    				=   in_proj_bias[-2_5_6:]
				# read in weights + bias of input projection layer of cross-attention
				SCREAMING_SNAKE_CASE_  :		Any    				=   state_dict.pop(
				    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'     )
				SCREAMING_SNAKE_CASE_  :		Dict    				=   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
				SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   in_proj_weight_cross_attn[:2_5_6, :]
				SCREAMING_SNAKE_CASE_  :		List[Any]    				=   in_proj_bias_cross_attn[:2_5_6]
				SCREAMING_SNAKE_CASE_  :		List[Any]    				=   in_proj_weight_cross_attn[2_5_6:5_1_2, :]
				SCREAMING_SNAKE_CASE_  :		Any    				=   in_proj_bias_cross_attn[2_5_6:5_1_2]
				SCREAMING_SNAKE_CASE_  :		Any    				=   in_proj_weight_cross_attn[-2_5_6:, :]
				SCREAMING_SNAKE_CASE_  :		Tuple    				=   in_proj_bias_cross_attn[-2_5_6:]




def 				_snake_case     (       ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Tuple    				=   "http://images.cocodataset.org/val2017/000000039769.jpg"
		SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   Image.open(requests.get(lowerCAmelCase       ,			stream=lowerCAmelCase     ).raw     )

		return im




@torch.no_grad()
def 				_snake_case     (       lowerCAmelCase							:       Union[str, Any]       ,			lowerCAmelCase							:       List[Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int    				=   DetrConfig()
		# set backbone and dilation attributes
		if "resnet101" in model_name:
				SCREAMING_SNAKE_CASE_  :		List[Any]    				=   "resnet101"
		if "dc5" in model_name:
				SCREAMING_SNAKE_CASE_  :		List[Any]    				=   True
		SCREAMING_SNAKE_CASE_  :		Any    				=   "panoptic" in model_name
		if is_panoptic:
				SCREAMING_SNAKE_CASE_  :		List[Any]    				=   2_5_0
		else:
				SCREAMING_SNAKE_CASE_  :		Dict    				=   9_1
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   "huggingface/label-files"
				SCREAMING_SNAKE_CASE_  :		str    				=   "coco-detection-id2label.json"
				SCREAMING_SNAKE_CASE_  :		str    				=   json.load(open(hf_hub_download(lowerCAmelCase       ,			lowerCAmelCase       ,			repo_type="dataset"     )       ,			"r"     )     )
				SCREAMING_SNAKE_CASE_  :		Any    				=   {int(lowerCAmelCase     ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE_  :		Dict    				=   idalabel
				SCREAMING_SNAKE_CASE_  :		str    				=   {v: k for k, v in idalabel.items()}

		# load image processor
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   "coco_panoptic" if is_panoptic else "coco_detection"
		SCREAMING_SNAKE_CASE_  :		Tuple    				=   DetrImageProcessor(format=lowerCAmelCase     )

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

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

		# load original model from torch hub
		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   torch.hub.load("facebookresearch/detr"       ,			lowerCAmelCase       ,			pretrained=lowerCAmelCase     ).eval()
		SCREAMING_SNAKE_CASE_  :		int    				=   detr.state_dict()
		# rename keys
		for src, dest in rename_keys:
				if is_panoptic:
						SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   "detr." + src
				rename_key(lowerCAmelCase       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   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
		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   "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"     )
						):
								SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   state_dict.pop(lowerCAmelCase     )
								SCREAMING_SNAKE_CASE_  :		Dict    				=   val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								SCREAMING_SNAKE_CASE_  :		Any    				=   state_dict.pop(lowerCAmelCase     )
								SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   val
						elif key.startswith("bbox_attention"     ) or key.startswith("mask_head"     ):
								continue
						else:
								SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   state_dict.pop(lowerCAmelCase     )
								SCREAMING_SNAKE_CASE_  :		Any    				=   val
				else:
						if not key.startswith("class_labels_classifier"     ) and not key.startswith("bbox_predictor"     ):
								SCREAMING_SNAKE_CASE_  :		Any    				=   state_dict.pop(lowerCAmelCase     )
								SCREAMING_SNAKE_CASE_  :		List[Any]    				=   val
    # finally, create HuggingFace model and load state dict
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   DetrForSegmentation(lowerCAmelCase     ) if is_panoptic else DetrForObjectDetection(lowerCAmelCase     )
		model.load_state_dict(lowerCAmelCase     )
		model.eval()
		# verify our conversion
		SCREAMING_SNAKE_CASE_  :		Any    				=   detr(lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		List[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 :     List[str]          =  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 :     Dict          =  parser.parse_args()
		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

from __future__ import annotations






class 		a__						:

					def __init__(   self				:	Union[str, Any],_A				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   value
							SCREAMING_SNAKE_CASE_  :		Node | None    				=   None
							SCREAMING_SNAKE_CASE_  :		Node | None    				=   None







class 		a__						:
					A     	=							42

					def __init__(   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]    				=   0

					def     __UpperCamelCase       (   self				:	List[Any],_A				:	Node | None,_A				:	int   ):







							"""simple docstring"""






							if node is None:
									return

							if path_sum == self.target:
									self.paths += 1

							if node.left:
									self.depth_first_search(node.left,path_sum + node.left.value   )
							if node.right:
									self.depth_first_search(node.right,path_sum + node.right.value   )






					def     __UpperCamelCase       (   self				:	List[str],_A				:	Node | None,_A				:	int | None = None   ):







							"""simple docstring"""






							if node is None:
									return 0
							if target is not None:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   target

							self.depth_first_search(_A,node.value   )
							self.path_sum(node.left   )
							self.path_sum(node.right   )

							return self.paths


if __name__ == "__main__":
		import doctest

		doctest.testmod()
