


"""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()
_snake_case        =				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_snake_case        =				[]
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 	snake_case    (					_a:  int		, _a:  int		, _a:  Dict				)->		Dict:
      '''simple docstring'''


      lowerCamelCase__							     =		state_dict.pop(a__				)
      lowerCamelCase__							     =		val



def 	snake_case    (					_a:  List[Any]				)->		Union[str, Any]:
      '''simple docstring'''


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

      return new_state_dict



def 	snake_case    (					_a:  int		, _a:  int=False				)->		Optional[Any]:
      '''simple docstring'''


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



def 	snake_case    (					)->		Dict:
      '''simple docstring'''


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

      return im



@torch.no_grad()
def 	snake_case    (					_a:  Tuple		, _a:  int				)->		Union[str, Any]:
      '''simple docstring'''


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

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

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

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

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






import math






class      lowerCamelCase_      :




	def __init__(     self						:     Union[str, Any]					,      __A						:     List[str]=0							):  # a graph with Node 0,1,...,N-1
		__A :			List[str]		    =			n
		__A :			List[str]		    =			[
		    [math.inf for j in range(0					,      __A							)] for i in range(0					,      __A							)
		]  # adjacency matrix for weight
		__A :			str		    =			[
		    [math.inf for j in range(0					,      __A							)] for i in range(0					,      __A							)
		]  # dp[i][j] stores minimum distance from i to j




	def        lowerCAmelCase_       (     self						:     str					,      __A						:     Union[str, Any]					,      __A						:     Any					,      __A						:     Optional[int]							):
		__A :			List[Any]		    =			w




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		for k in range(0					,      self.n							):
			for i in range(0					,      self.n							):
				for j in range(0					,      self.n							):
					__A :			List[Any]		    =			min(self.dp[i][j]					,      self.dp[i][k] + self.dp[k][j]							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     List[str]					,      __A						:     List[str]							):
		return self.dp[u][v]


if __name__ == "__main__":
			UpperCAmelCase_							:					Tuple		 =   Graph(5)
			graph.add_edge(0, 2, 9)
			graph.add_edge(0, 4, 10)
			graph.add_edge(1, 3, 5)
			graph.add_edge(2, 3, 7)
			graph.add_edge(3, 0, 10)
			graph.add_edge(3, 1, 2)
			graph.add_edge(3, 2, 1)
			graph.add_edge(3, 4, 6)
			graph.add_edge(4, 1, 3)
			graph.add_edge(4, 2, 4)
			graph.add_edge(4, 3, 9)
			graph.floyd_warshall()
			graph.show_min(1, 4)
			graph.show_min(0, 3)





