



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()
UpperCAmelCase__   =       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase__   =       []
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				:   Any							,   _UpperCAmelCase				:   List[Any]							,   _UpperCAmelCase				:   Optional[int]    )				-> Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase		       =   state_dict.pop(SCREAMING_SNAKE_CASE__    )
			_UpperCAmelCase		       =   val





def 							A	(     _UpperCAmelCase				:   Optional[Any]    )				-> str:







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

			return new_state_dict





def 							A	(     _UpperCAmelCase				:   Union[str, Any]							,   _UpperCAmelCase				:   Any=False    )				-> Optional[Any]:







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





def 							A	(     )				-> Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase		       =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
			_UpperCAmelCase		       =   Image.open(requests.get(SCREAMING_SNAKE_CASE__							,   stream=SCREAMING_SNAKE_CASE__    ).raw    )

			return im





@torch.no_grad()
def 							A	(     _UpperCAmelCase				:   Tuple							,   _UpperCAmelCase				:   str    )				-> str:







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

			# load image processor
			_UpperCAmelCase		       =   """coco_panoptic""" if is_panoptic else """coco_detection"""
			_UpperCAmelCase		       =   ConditionalDetrImageProcessor(format=SCREAMING_SNAKE_CASE__    )

			# prepare image
			_UpperCAmelCase		       =   prepare_img()
			_UpperCAmelCase		       =   image_processor(images=SCREAMING_SNAKE_CASE__							,   return_tensors='pt'    )
			_UpperCAmelCase		       =   encoding["""pixel_values"""]

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

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


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


from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class __snake_case		:
    _a		:						int
    _a		:						TreeNode | None=				None
    _a		:						TreeNode | None=				None






lowercase  :					Dict						    =							namedtuple("""CoinsDistribResult""", """moves excess""")







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    int:

       if root is None:
              return 0

       # Validation
       def count_nodes(SCREAMING_SNAKE_CASE__				) -> int:
              if node is None:
                     return 0

              return count_nodes(node.left				) + count_nodes(node.right				) + 1

       def count_coins(SCREAMING_SNAKE_CASE__				) -> int:
              if node is None:
                     return 0

              return count_coins(node.left				) + count_coins(node.right				) + node.data

       if count_nodes(SCREAMING_SNAKE_CASE__				) != count_coins(SCREAMING_SNAKE_CASE__				):
              raise ValueError("""The nodes number should be same as the number of coins"""				)

       # Main calculation
       def get_distrib(SCREAMING_SNAKE_CASE__				) -> CoinsDistribResult:

              if node is None:
                     return CoinsDistribResult(0					,	1				)

              lowercase	,			lowercase							:     int       	=      get_distrib(node.left				)
              lowercase	,			lowercase							:     List[Any]       	=      get_distrib(node.right				)

              lowercase							:     Optional[Any]       	=      1 - left_distrib_excess
              lowercase							:     Union[str, Any]       	=      1 - right_distrib_excess

              lowercase							:     List[Any]       	=      (
                  left_distrib_moves
                  + right_distrib_moves
                  + abs(SCREAMING_SNAKE_CASE__				)
                  + abs(SCREAMING_SNAKE_CASE__				)
              )
              lowercase							:     Any       	=      node.data - coins_to_left - coins_to_right

              return CoinsDistribResult(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       return get_distrib(SCREAMING_SNAKE_CASE__				)[0]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
