




'''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()
UpperCamelCase_		:     Dict          =		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase_		:     Union[str, 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      (  _UpperCamelCase:       Dict   ,		_UpperCamelCase:       List[str]   ,		_UpperCamelCase:       Dict       )		->					Dict:

     """simple docstring"""




     _snake_case				= state_dict.pop(_UpperCamelCase       )
     _snake_case				= val





def       __a      (  _UpperCamelCase:       Optional[Any]       )		->					Any:

     """simple docstring"""




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

     return new_state_dict





def       __a      (  _UpperCamelCase:       Dict   ,		_UpperCamelCase:       int=False       )		->					Optional[Any]:

     """simple docstring"""




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





def       __a      (  )		->					Optional[int]:

     """simple docstring"""




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

     return im





@torch.no_grad()
def       __a      (  _UpperCamelCase:       Optional[Any]   ,		_UpperCamelCase:       str       )		->					Any:

     """simple docstring"""




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

     # load image processor
     _snake_case				= "coco_panoptic" if is_panoptic else "coco_detection"
     _snake_case				= ConditionalDetrImageProcessor(format=_UpperCamelCase       )

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

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

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








'''simple docstring'''





from math import ceil





def       __a      (  _UpperCamelCase:       int = 1_001       )		->					int:

     """simple docstring"""




     _snake_case				= 1

     for i in range(1   ,		int(ceil(n / 2.0       )       )       ):
          _snake_case				= 2 * i + 1
          _snake_case				= 2 * i
          _snake_case				= total + 4 * odd**2 - 6 * even

     return total


if __name__ == "__main__":
   import sys

   if len(sys.argv) == 1:
      print(solution())
   else:
      try:
         UpperCamelCase_		:     int          =		int(sys.argv[1])
         print(solution(n))
      except ValueError:
         print('''Invalid entry - please enter a number''')


