

'''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					:			List[str]       =	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase					:			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   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = state_dict.pop(a__		)
     __SCREAMING_SNAKE_CASE      = val







def   a__			(    a__		):




     """simple docstring"""


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

     return new_state_dict







def   a__			(    a__ ,       a__=False		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = """"""
     if is_panoptic:
          __SCREAMING_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)
          __SCREAMING_SNAKE_CASE      = state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'		)
          __SCREAMING_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
          __SCREAMING_SNAKE_CASE      = in_proj_weight[:2_56, :]
          __SCREAMING_SNAKE_CASE      = in_proj_bias[:2_56]
          __SCREAMING_SNAKE_CASE      = in_proj_weight[2_56:5_12, :]
          __SCREAMING_SNAKE_CASE      = in_proj_bias[2_56:5_12]
          __SCREAMING_SNAKE_CASE      = in_proj_weight[-2_56:, :]
          __SCREAMING_SNAKE_CASE      = in_proj_bias[-2_56:]







def   a__			(    ):




     """simple docstring"""


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

     return im







@torch.no_grad()
def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = ConditionalDetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          __SCREAMING_SNAKE_CASE      = """resnet101"""
     if "dc5" in model_name:
          __SCREAMING_SNAKE_CASE      = True
     __SCREAMING_SNAKE_CASE      = """panoptic""" in model_name
     if is_panoptic:
          __SCREAMING_SNAKE_CASE      = 2_50
     else:
          __SCREAMING_SNAKE_CASE      = 91
          __SCREAMING_SNAKE_CASE      = """huggingface/label-files"""
          __SCREAMING_SNAKE_CASE      = """coco-detection-id2label.json"""
          __SCREAMING_SNAKE_CASE      = json.load(open(hf_hub_download(a__ ,       a__ ,       repo_type="""dataset"""		) ,       """r"""		)		)
          __SCREAMING_SNAKE_CASE      = {int(a__		): v for k, v in idalabel.items()}
          __SCREAMING_SNAKE_CASE      = idalabel
          __SCREAMING_SNAKE_CASE      = {v: k for k, v in idalabel.items()}

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

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

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

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








"""simple docstring"""

from math import ceil, sqrt




def      lowercase__    (       lowerCAmelCase					:       int = 1_000_000			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       0

    for outer_width in range(3			,					(limit // 4) + 2			):
        if outer_width**2 > limit:
            UpperCAmelCase          =       max(ceil(sqrt(outer_width**2 - limit			)			)			,					1			)
        else:
            UpperCAmelCase          =       1
        if (outer_width - hole_width_lower_bound) % 2:
            hole_width_lower_bound += 1

        answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

    return answer


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



