





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_   :   str       =					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase_   :   Dict       =					[]
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  (__lowerCamelCase  :     Optional[Any]					,  __lowerCamelCase  :     str					,  __lowerCamelCase  :     Optional[int]     )   ->    Any:
			a__     = state_dict.pop(__lowerCamelCase     )
			a__     = val







def    _lowerCamelCase  (__lowerCamelCase  :     Optional[int]     )   ->    Dict:
			a__     = OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									a__     = key.replace("backbone.0.body"					,  "backbone.conv_encoder.model"     )
									a__     = value
						else:
									a__     = value

			return new_state_dict







def    _lowerCamelCase  (__lowerCamelCase  :     str					,  __lowerCamelCase  :     List[str]=False     )   ->    List[Any]:
			a__     = ""
			if is_panoptic:
						a__     = "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)
						a__     = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''     )
						a__     = 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
						a__     = in_proj_weight[:256, :]
						a__     = in_proj_bias[:256]
						a__     = in_proj_weight[256:512, :]
						a__     = in_proj_bias[256:512]
						a__     = in_proj_weight[-256:, :]
						a__     = in_proj_bias[-256:]







def    _lowerCamelCase  ()   ->    Any:
			a__     = "http://images.cocodataset.org/val2017/000000039769.jpg"
			a__     = Image.open(requests.get(__lowerCamelCase					,  stream=__lowerCamelCase     ).raw     )

			return im







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

			# load image processor
			a__     = "coco_panoptic" if is_panoptic else "coco_detection"
			a__     = ConditionalDetrImageProcessor(format=__lowerCamelCase     )

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

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

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










'''simple docstring'''


def    _lowerCamelCase  (__lowerCamelCase  :     int = 400_0000     )   ->    int:
			a__     = [0, 1]
			a__     = 0
			while fib[i] <= n:
						fib.append(fib[i] + fib[i + 1]     )
						if fib[i + 2] > n:
									break
						i += 1
			a__     = 0
			for j in range(len(__lowerCamelCase     ) - 1     ):
						if fib[j] % 2 == 0:
									total += fib[j]

			return total


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



