



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

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE__      					=     []
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        lowerCamelCase  (				_snake_case  :						Dict     ,_snake_case  :						Dict     ,_snake_case  :						Optional[int]       ):





				'''simple docstring'''


				lowercase__		       =						state_dict.pop(lowercase_       )
				lowercase__		       =						val




def        lowerCamelCase  (				_snake_case  :						Dict       ):





				'''simple docstring'''


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

				return new_state_dict




def        lowerCamelCase  (				_snake_case  :						Optional[int]     ,_snake_case  :						Dict=False       ):





				'''simple docstring'''


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




def        lowerCamelCase  (				):





				'''simple docstring'''


				lowercase__		       =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowercase__		       =						Image.open(requests.get(lowercase_     ,stream=lowercase_       ).raw       )

				return im




@torch.no_grad()
def        lowerCamelCase  (				_snake_case  :						str     ,_snake_case  :						int       ):





				'''simple docstring'''


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

				# load image processor
				lowercase__		       =						"""coco_panoptic""" if is_panoptic else """coco_detection"""
				lowercase__		       =						ConditionalDetrImageProcessor(format=lowercase_       )

				# prepare image
				lowercase__		       =						prepare_img()
				lowercase__		       =						image_processor(images=lowercase_     ,return_tensors="pt"       )
				lowercase__		       =						encoding["""pixel_values"""]

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

				# load original model from torch hub
				lowercase__		       =						torch.hub.load("DeppMeng/ConditionalDETR"     ,lowercase_     ,pretrained=lowercase_       ).eval()
				lowercase__		       =						conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												lowercase__		       =						"""conditional_detr.""" + src
								rename_key(lowercase_     ,lowercase_     ,lowercase_       )
				lowercase__		       =						rename_backbone_keys(lowercase_       )
				# query, key and value matrices need special treatment
				read_in_q_k_v(lowercase_     ,is_panoptic=lowercase_       )
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				lowercase__		       =						"""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"       )
												):
																lowercase__		       =						state_dict.pop(lowercase_       )
																lowercase__		       =						val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																lowercase__		       =						state_dict.pop(lowercase_       )
																lowercase__		       =						val
												elif key.startswith("bbox_attention"       ) or key.startswith("mask_head"       ):
																continue
												else:
																lowercase__		       =						state_dict.pop(lowercase_       )
																lowercase__		       =						val
								else:
												if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
																lowercase__		       =						state_dict.pop(lowercase_       )
																lowercase__		       =						val
    # finally, create HuggingFace model and load state dict
				lowercase__		       =						ConditionalDetrForSegmentation(lowercase_       ) if is_panoptic else ConditionalDetrForObjectDetection(lowercase_       )
				model.load_state_dict(lowercase_       )
				model.eval()
				model.push_to_hub(repo_id=lowercase_     ,organization="DepuMeng"     ,commit_message="Add model"       )
				# verify our conversion
				lowercase__		       =						conditional_detr(lowercase_       )
				lowercase__		       =						model(lowercase_       )
				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(lowercase_       ).mkdir(exist_ok=lowercase_       )
				model.save_pretrained(lowercase_       )
				image_processor.save_pretrained(lowercase_       )


if __name__ == "__main__":
	SCREAMING_SNAKE_CASE__      					=     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."
	)
	SCREAMING_SNAKE_CASE__      					=     parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









'''simple docstring'''





print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))




