

'''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()
lowercase_             =     logging.get_logger(__name__)

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





      """simple docstring"""



      _lowercase			 =state_dict.pop(A__  )
      _lowercase			 =val
def   a   (    A__			:     Dict  )					->   int:





      """simple docstring"""



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

      return new_state_dict
def   a   (    A__			:     Union[str, Any]   ,     A__			:     Optional[Any]=False  )					->   List[str]:





      """simple docstring"""



      _lowercase			 =''
      if is_panoptic:
            _lowercase			 ='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)
            _lowercase			 =state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''  )
            _lowercase			 =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
            _lowercase			 =in_proj_weight[:256, :]
            _lowercase			 =in_proj_bias[:256]
            _lowercase			 =in_proj_weight[256:512, :]
            _lowercase			 =in_proj_bias[256:512]
            _lowercase			 =in_proj_weight[-256:, :]
            _lowercase			 =in_proj_bias[-256:]
def   a   (    )					->   str:





      """simple docstring"""



      _lowercase			 ='http://images.cocodataset.org/val2017/000000039769.jpg'
      _lowercase			 =Image.open(requests.get(A__   ,     stream=A__  ).raw  )

      return im
@torch.no_grad()
def   a   (    A__			:     Optional[int]   ,     A__			:     Dict  )					->   Dict:





      """simple docstring"""



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

      # load image processor
      _lowercase			 ='coco_panoptic' if is_panoptic else 'coco_detection'
      _lowercase			 =ConditionalDetrImageProcessor(format=A__  )

      # prepare image
      _lowercase			 =prepare_img()
      _lowercase			 =image_processor(images=A__   ,     return_tensors='pt'  )
      _lowercase			 =encoding['pixel_values']

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

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


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





import os
def   a   (    )					->   Optional[int]:





      """simple docstring"""



      with open(os.path.dirname(A__  ) + '/grid.txt'  ) as f:
            _lowercase			 =[]  # noqa: E741
            for _ in range(20  ):
                  l.append([int(A__  ) for x in f.readline().split()]  )

            _lowercase			 =0

            # right
            for i in range(20  ):
                  for j in range(17  ):
                        _lowercase			 =l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
                        if temp > maximum:
                              _lowercase			 =temp

        # down
            for i in range(17  ):
                  for j in range(20  ):
                        _lowercase			 =l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
                        if temp > maximum:
                              _lowercase			 =temp

        # diagonal 1
            for i in range(17  ):
                  for j in range(17  ):
                        _lowercase			 =l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
                        if temp > maximum:
                              _lowercase			 =temp

        # diagonal 2
            for i in range(17  ):
                  for j in range(3   ,     20  ):
                        _lowercase			 =l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
                        if temp > maximum:
                              _lowercase			 =temp
            return maximum


if __name__ == "__main__":
  print(solution())


