

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()
lowerCAmelCase							:  Optional[int]            =						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}.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       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			int  		=	state_dict.pop(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	val







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Any  		=	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_:			Optional[Any]  		=	value
												else:
																		SCREAMING_SNAKE_CASE_:			List[str]  		=	value

						return new_state_dict







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase=False   ):
						SCREAMING_SNAKE_CASE_:			Any  		=	""
						if is_panoptic:
												SCREAMING_SNAKE_CASE_:			List[str]  		=	"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)
												SCREAMING_SNAKE_CASE_:			Any  		=	state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"   )
												SCREAMING_SNAKE_CASE_:			Optional[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_:			List[str]  		=	in_proj_weight[:2_56, :]
												SCREAMING_SNAKE_CASE_:			Tuple  		=	in_proj_bias[:2_56]
												SCREAMING_SNAKE_CASE_:			int  		=	in_proj_weight[2_56:5_12, :]
												SCREAMING_SNAKE_CASE_:			int  		=	in_proj_bias[2_56:5_12]
												SCREAMING_SNAKE_CASE_:			int  		=	in_proj_weight[-2_56:, :]
												SCREAMING_SNAKE_CASE_:			Any  		=	in_proj_bias[-2_56:]







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			Tuple  		=	"http://images.cocodataset.org/val2017/000000039769.jpg"
						SCREAMING_SNAKE_CASE_:			Any  		=	Image.open(requests.get(_UpperCAmelCase    ,     stream=_UpperCAmelCase   ).raw   )

						return im







@torch.no_grad()
def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			str  		=	ConditionalDetrConfig()
						# set backbone and dilation attributes
						if "resnet101" in model_name:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	"resnet101"
						if "dc5" in model_name:
												SCREAMING_SNAKE_CASE_:			Dict  		=	True
						SCREAMING_SNAKE_CASE_:			int  		=	"panoptic" in model_name
						if is_panoptic:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	2_50
						else:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	91
												SCREAMING_SNAKE_CASE_:			str  		=	"huggingface/label-files"
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"coco-detection-id2label.json"
												SCREAMING_SNAKE_CASE_:			Any  		=	json.load(open(hf_hub_download(_UpperCAmelCase    ,     _UpperCAmelCase    ,     repo_type="dataset"   )    ,     "r"   )   )
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{int(_UpperCAmelCase   ): v for k, v in idalabel.items()}
												SCREAMING_SNAKE_CASE_:			List[str]  		=	idalabel
												SCREAMING_SNAKE_CASE_:			int  		=	{v: k for k, v in idalabel.items()}

						# load image processor
						SCREAMING_SNAKE_CASE_:			Dict  		=	"coco_panoptic" if is_panoptic else "coco_detection"
						SCREAMING_SNAKE_CASE_:			int  		=	ConditionalDetrImageProcessor(format=_UpperCAmelCase   )

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

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

						# load original model from torch hub
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.hub.load("DeppMeng/ConditionalDETR"    ,     _UpperCAmelCase    ,     pretrained=_UpperCAmelCase   ).eval()
						SCREAMING_SNAKE_CASE_:			Tuple  		=	conditional_detr.state_dict()
						# rename keys
						for src, dest in rename_keys:
												if is_panoptic:
																		SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"conditional_detr." + src
												rename_key(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Dict  		=	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
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	"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"   )
																		):
																								SCREAMING_SNAKE_CASE_:			Dict  		=	state_dict.pop(_UpperCAmelCase   )
																								SCREAMING_SNAKE_CASE_:			Dict  		=	val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								SCREAMING_SNAKE_CASE_:			List[Any]  		=	state_dict.pop(_UpperCAmelCase   )
																								SCREAMING_SNAKE_CASE_:			Any  		=	val
																		elif key.startswith("bbox_attention"   ) or key.startswith("mask_head"   ):
																								continue
																		else:
																								SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	state_dict.pop(_UpperCAmelCase   )
																								SCREAMING_SNAKE_CASE_:			Optional[int]  		=	val
												else:
																		if not key.startswith("class_labels_classifier"   ) and not key.startswith("bbox_predictor"   ):
																								SCREAMING_SNAKE_CASE_:			str  		=	state_dict.pop(_UpperCAmelCase   )
																								SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	val
    # finally, create HuggingFace model and load state dict
						SCREAMING_SNAKE_CASE_:			Any  		=	ConditionalDetrForSegmentation(_UpperCAmelCase   ) if is_panoptic else ConditionalDetrForObjectDetection(_UpperCAmelCase   )
						model.load_state_dict(_UpperCAmelCase   )
						model.eval()
						model.push_to_hub(repo_id=_UpperCAmelCase    ,     organization="DepuMeng"    ,     commit_message="Add model"   )
						# verify our conversion
						SCREAMING_SNAKE_CASE_:			int  		=	conditional_detr(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	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__":
						lowerCAmelCase							:  Any            =						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."""
						)
						lowerCAmelCase							:  List[Any]            =						parser.parse_args()
						convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









def       A_				(   _UpperCAmelCase = 10**9   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	1
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	2

						SCREAMING_SNAKE_CASE_:			int  		=	0
						SCREAMING_SNAKE_CASE_:			Dict  		=	0
						SCREAMING_SNAKE_CASE_:			List[str]  		=	0
						while perimeter <= max_perimeter:
												perimeters_sum += perimeter

												prev_value += 2 * value
												value += prev_value

												SCREAMING_SNAKE_CASE_:			Any  		=	2 * value + 2 if i % 2 == 0 else 2 * value - 2
												i += 1

						return perimeters_sum


if __name__ == "__main__":
						print(f'''{solution() = }''')






