




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__       :      Optional[Any]  =   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__       :      Optional[Any]  =   []
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      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Dict:
       lowerCamelCase   :      List[str]			 =					state_dict.pop(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Tuple			 =					val

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Optional[int]:
       lowerCamelCase   :      Optional[Any]			 =					OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     lowerCamelCase   :      Optional[int]			 =					key.replace("backbone.0.body" ,"backbone.conv_encoder.model"    )
                     lowerCamelCase   :      Optional[int]			 =					value
              else:
                     lowerCamelCase   :      List[Any]			 =					value

       return new_state_dict

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False    )	-> List[str]:
       lowerCamelCase   :      int			 =					""
       if is_panoptic:
              lowerCamelCase   :      Dict			 =					"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)
              lowerCamelCase   :      List[Any]			 =					state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''    )
              lowerCamelCase   :      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
              lowerCamelCase   :      List[Any]			 =					in_proj_weight[:256, :]
              lowerCamelCase   :      int			 =					in_proj_bias[:256]
              lowerCamelCase   :      Optional[int]			 =					in_proj_weight[256:512, :]
              lowerCamelCase   :      int			 =					in_proj_bias[256:512]
              lowerCamelCase   :      Any			 =					in_proj_weight[-256:, :]
              lowerCamelCase   :      Dict			 =					in_proj_bias[-256:]

def      A      (      )	-> List[str]:
       lowerCamelCase   :      Tuple			 =					"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowerCamelCase   :      int			 =					Image.open(requests.get(_SCREAMING_SNAKE_CASE ,stream=_SCREAMING_SNAKE_CASE    ).raw    )

       return im

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

       # load image processor
       lowerCamelCase   :      Tuple			 =					"coco_panoptic" if is_panoptic else "coco_detection"
       lowerCamelCase   :      List[str]			 =					ConditionalDetrImageProcessor(format=_SCREAMING_SNAKE_CASE    )

       # prepare image
       lowerCamelCase   :      List[str]			 =					prepare_img()
       lowerCamelCase   :      Union[str, Any]			 =					image_processor(images=_SCREAMING_SNAKE_CASE ,return_tensors="pt"    )
       lowerCamelCase   :      int			 =					encoding["pixel_values"]

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

       # load original model from torch hub
       lowerCamelCase   :      Tuple			 =					torch.hub.load("DeppMeng/ConditionalDETR" ,_SCREAMING_SNAKE_CASE ,pretrained=_SCREAMING_SNAKE_CASE    ).eval()
       lowerCamelCase   :      List[Any]			 =					conditional_detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     lowerCamelCase   :      Optional[Any]			 =					"conditional_detr." + src
              rename_key(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Dict			 =					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
       lowerCamelCase   :      Optional[int]			 =					"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"    )
                     ):
                            lowerCamelCase   :      Dict			 =					state_dict.pop(_SCREAMING_SNAKE_CASE    )
                            lowerCamelCase   :      int			 =					val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            lowerCamelCase   :      Optional[int]			 =					state_dict.pop(_SCREAMING_SNAKE_CASE    )
                            lowerCamelCase   :      Dict			 =					val
                     elif key.startswith("bbox_attention"    ) or key.startswith("mask_head"    ):
                            continue
                     else:
                            lowerCamelCase   :      str			 =					state_dict.pop(_SCREAMING_SNAKE_CASE    )
                            lowerCamelCase   :      Dict			 =					val
              else:
                     if not key.startswith("class_labels_classifier"    ) and not key.startswith("bbox_predictor"    ):
                            lowerCamelCase   :      Dict			 =					state_dict.pop(_SCREAMING_SNAKE_CASE    )
                            lowerCamelCase   :      List[str]			 =					val
    # finally, create HuggingFace model and load state dict
       lowerCamelCase   :      List[str]			 =					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
       lowerCamelCase   :      Any			 =					conditional_detr(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[Any]			 =					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__":
       SCREAMING_SNAKE_CASE__       :      List[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.'
       )
       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   parser.parse_args()
       convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)












import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

def      A      (      _SCREAMING_SNAKE_CASE    )	-> tuple:
       return (data["data"], data["target"])

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> np.ndarray:
       lowerCamelCase   :      List[str]			 =					XGBRegressor(verbosity=0 ,random_state=42    )
       xgb.fit(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # Predict target for test data
       lowerCamelCase   :      List[Any]			 =					xgb.predict(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Tuple			 =					predictions.reshape(len(_SCREAMING_SNAKE_CASE    ) ,1    )
       return predictions

def      A      (      )	-> None:
       lowerCamelCase   :      Dict			 =					fetch_california_housing()
       lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					data_handling(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Optional[Any]			 =					train_test_split(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,test_size=0.25 ,random_state=1    )
       lowerCamelCase   :      Any			 =					xgboost(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # Error printing
       print(f'''Mean Absolute Error : {mean_absolute_error(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )}'''    )
       print(f'''Mean Square Error  : {mean_squared_error(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )}'''    )


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()






