



"""simple docstring"""
import math
import sys
class 							lowerCamelCase    :






					'''simple docstring'''





					def __init__(			self:				Dict		)  ->      str:
											snake_case_					:Optional[Any]		=     0
											snake_case_					:Dict		=     []
											snake_case_					:List[Any]		=     {}  # To store the pos of node in array






					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      Union[str, Any]:
											return self.cur_size == 0






					def   lowerCAmelCase_							(			self:				Optional[Any]		,	snake_case:				Optional[Any]		)  ->      str:
											snake_case_					:Optional[int]		=     self.left(snake_case		)
											snake_case_					:List[str]		=     self.right(snake_case		)
											if lc < self.cur_size and self.array(snake_case		)[0] < self.array(snake_case		)[0]:
																	snake_case_					:Any		=     lc
											else:
																	snake_case_					:List[str]		=     idx
											if rc < self.cur_size and self.array(snake_case		)[0] < self.array(snake_case		)[0]:
																	snake_case_					:Any		=     rc
											if smallest != idx:
																	self.swap(snake_case		,	snake_case		)
																	self.min_heapify(snake_case		)






					def   lowerCAmelCase_							(			self:				Any		,	snake_case:				int		)  ->      Tuple:
											# Inserts a node into the Priority Queue
											snake_case_					:List[Any]		=     self.cur_size
											self.cur_size += 1
											self.array.append((sys.maxsize, tup[1])		)
											self.decrease_key((sys.maxsize, tup[1])		,	tup[0]		)






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Any:
											# Removes and returns the min element at top of priority queue
											snake_case_					:int		=     self.array[0][1]
											snake_case_					:str		=     self.array[self.cur_size - 1]
											self.cur_size -= 1
											self.min_heapify(1		)
											del self.pos[min_node]
											return min_node






					def   lowerCAmelCase_							(			self:				Any		,	snake_case:				str		)  ->      Union[str, Any]:
											# returns the index of left child
											return 2 * i + 1






					def   lowerCAmelCase_							(			self:				Tuple		,	snake_case:				Union[str, Any]		)  ->      str:
											# returns the index of right child
											return 2 * i + 2






					def   lowerCAmelCase_							(			self:				Tuple		,	snake_case:				List[Any]		)  ->      Tuple:
											# returns the index of parent
											return math.floor(i / 2		)






					def   lowerCAmelCase_							(			self:				Tuple		,	snake_case:				List[Any]		,	snake_case:				Optional[Any]		)  ->      Any:
											# swaps array elements at indices i and j
											# update the pos{}
											snake_case_					:List[str]		=     j
											snake_case_					:Optional[int]		=     i
											snake_case_					:Optional[int]		=     self.array[i]
											snake_case_					:int		=     self.array[j]
											snake_case_					:Optional[int]		=     temp






					def   lowerCAmelCase_							(			self:				int		,	snake_case:				Tuple		,	snake_case:				Tuple		)  ->      List[Any]:
											snake_case_					:Optional[Any]		=     self.pos[tup[1]]
											# assuming the new_d is atmost old_d
											snake_case_					:List[Any]		=     (new_d, tup[1])
											while idx > 0 and self.array[self.par(snake_case		)][0] > self.array[idx][0]:
																	self.swap(snake_case		,	self.par(snake_case		)		)
																	snake_case_					:List[Any]		=     self.par(snake_case		)

class 							lowerCamelCase    :






					'''simple docstring'''





					def __init__(			self:				int		,	snake_case:				str		)  ->      List[Any]:
											snake_case_					:Optional[int]		=     {}  # To store graph: u -> (v,w)
											snake_case_					:Any		=     num  # Number of nodes in graph
											# To store the distance from source vertex
											snake_case_					:List[str]		=     [0] * self.num_nodes
											snake_case_					:Dict		=     [-1] * self.num_nodes  # To store the path






					def   lowerCAmelCase_							(			self:				Union[str, Any]		,	snake_case:				List[Any]		,	snake_case:				Dict		,	snake_case:				Any		)  ->      str:
											#  Edge going from node u to v and v to u with weight w
											# u (w)-> v, v (w) -> u
											# Check if u already in graph
											if u in self.adjList:
																	self.adjList[u].append((v, w)		)
											else:
																	snake_case_					:int		=     [(v, w)]

											# Assuming undirected graph
											if v in self.adjList:
																	self.adjList[v].append((u, w)		)
											else:
																	snake_case_					:Any		=     [(u, w)]






					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      List[Any]:
											# u -> v(w)
											for u in self.adjList:
																	print(snake_case		,	"""->"""		,	""" -> """.join(str(f"""{v}({w})"""		) for v, w in self.adjList[u]		)		)






					def   lowerCAmelCase_							(			self:				Any		,	snake_case:				str		)  ->      Optional[int]:
											# Flush old junk values in par[]
											snake_case_					:List[str]		=     [-1] * self.num_nodes
											# src is the source node
											snake_case_					:List[Any]		=     0
											snake_case_					:int		=     PriorityQueue()
											q.insert((0, src)		)  # (dist from src, node)
											for u in self.adjList:
																	if u != src:
																							snake_case_					:int		=     sys.maxsize  # Infinity
																							snake_case_					:List[Any]		=     -1

											while not q.is_empty():
																	snake_case_					:str		=     q.extract_min()  # Returns node with the min dist from source
																	# Update the distance of all the neighbours of u and
																	# if their prev dist was INFINITY then push them in Q
																	for v, w in self.adjList[u]:
																							snake_case_					:List[str]		=     self.dist[u] + w
																							if self.dist[v] > new_dist:
																													if self.dist[v] == sys.maxsize:
																																			q.insert((new_dist, v)		)
																													else:
																																			q.decrease_key((self.dist[v], v)		,	snake_case		)
																													snake_case_					:Any		=     new_dist
																													snake_case_					:Union[str, Any]		=     u

        # Show the shortest distances from src
											self.show_distances(snake_case		)






					def   lowerCAmelCase_							(			self:				str		,	snake_case:				str		)  ->      Tuple:
											print(f"""Distance from node: {src}"""		)
											for u in range(self.num_nodes		):
																	print(f"""Node {u} has distance: {self.dist[u]}"""		)






					def   lowerCAmelCase_							(			self:				Optional[Any]		,	snake_case:				Union[str, Any]		,	snake_case:				str		)  ->      Any:
											# To show the shortest path from src to dest
											# WARNING: Use it *after* calling dijkstra
											snake_case_					:List[str]		=     []
											snake_case_					:Optional[int]		=     0
											snake_case_					:Dict		=     dest
											# Backtracking from dest to src
											while self.par[temp] != -1:
																	path.append(snake_case		)
																	if temp != src:
																							for v, w in self.adjList[temp]:
																													if v == self.par[temp]:
																																			cost += w
																																			break
																	snake_case_					:int		=     self.par[temp]
											path.append(snake_case		)
											path.reverse()

											print(f"""----Path to reach {dest} from {src}----"""		)
											for u in path:
																	print(f"""{u}"""		,	end=""" """		)
																	if u != dest:
																							print("""-> """		,	end=""""""		)

											print("""\nTotal cost of path: """		,	snake_case		)


if __name__ == "__main__":
						__a           =							Graph(9)
						graph.add_edge(0, 1, 4)
						graph.add_edge(0, 7, 8)
						graph.add_edge(1, 2, 8)
						graph.add_edge(1, 7, 11)
						graph.add_edge(2, 3, 7)
						graph.add_edge(2, 8, 2)
						graph.add_edge(2, 5, 4)
						graph.add_edge(3, 4, 9)
						graph.add_edge(3, 5, 14)
						graph.add_edge(4, 5, 10)
						graph.add_edge(5, 6, 2)
						graph.add_edge(6, 7, 1)
						graph.add_edge(6, 8, 6)
						graph.add_edge(7, 8, 7)
						graph.show_graph()
						graph.dijkstra(0)
						graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21

"""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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase       :    Union[str, Any]         =		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase       :    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}.multihead_attn.out_proj.weight''',
          f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
      )
  )
  rename_keys.append(
      (
          f'''transformer.decoder.layers.{i}.multihead_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'''))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
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"),
    ]
)


def   __UpperCAmelCase     (	_snake_case		:       List[str],				_snake_case		:       Tuple,				_snake_case		:       Any       ):
   _lowercase							     =       state_dict.pop(_snake_case       )
   _lowercase							     =       val


def   __UpperCAmelCase     (	_snake_case		:       Optional[int]       ):
   _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   __UpperCAmelCase     (	_snake_case		:       Optional[Any],				_snake_case		:       int=False       ):
   _lowercase							     =       ""
   if is_panoptic:
      _lowercase							     =       "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[:2_5_6, :]
      _lowercase							     =       in_proj_bias[:2_5_6]
      _lowercase							     =       in_proj_weight[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight[-2_5_6:, :]
      _lowercase							     =       in_proj_bias[-2_5_6:]
   # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
   for i in range(6       ):
      # read in weights + bias of input projection layer of self-attention
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""       )
      # next, add query, keys and values (in that order) to the state dict
      _lowercase							     =       in_proj_weight[:2_5_6, :]
      _lowercase							     =       in_proj_bias[:2_5_6]
      _lowercase							     =       in_proj_weight[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight[-2_5_6:, :]
      _lowercase							     =       in_proj_bias[-2_5_6:]
      # read in weights + bias of input projection layer of cross-attention
      _lowercase							     =       state_dict.pop(
          f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""       )
      # next, add query, keys and values (in that order) of cross-attention to the state dict
      _lowercase							     =       in_proj_weight_cross_attn[:2_5_6, :]
      _lowercase							     =       in_proj_bias_cross_attn[:2_5_6]
      _lowercase							     =       in_proj_weight_cross_attn[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias_cross_attn[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight_cross_attn[-2_5_6:, :]
      _lowercase							     =       in_proj_bias_cross_attn[-2_5_6:]


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

   return im


@torch.no_grad()
def   __UpperCAmelCase     (	_snake_case		:       Tuple,				_snake_case		:       str       ):
   _lowercase							     =       DetrConfig()
   # 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							     =       2_5_0
   else:
      _lowercase							     =       9_1
      _lowercase							     =       "huggingface/label-files"
      _lowercase							     =       "coco-detection-id2label.json"
      _lowercase							     =       json.load(open(hf_hub_download(_snake_case,				_snake_case,				repo_type="dataset"       ),				"r"       )       )
      _lowercase							     =       {int(_snake_case       ): 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							     =       DetrImageProcessor(format=_snake_case       )

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

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

   # load original model from torch hub
   _lowercase							     =       torch.hub.load("facebookresearch/detr",				_snake_case,				pretrained=_snake_case       ).eval()
   _lowercase							     =       detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         _lowercase							     =       "detr." + src
      rename_key(_snake_case,				_snake_case,				_snake_case       )
   _lowercase							     =       rename_backbone_keys(_snake_case       )
   # query, key and value matrices need special treatment
   read_in_q_k_v(_snake_case,				is_panoptic=_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
   _lowercase							     =       "detr.model." if is_panoptic else "model."
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith("detr"       )
             and not key.startswith("class_labels_classifier"       )
             and not key.startswith("bbox_predictor"       )
         ):
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
         elif key.startswith("bbox_attention"       ) or key.startswith("mask_head"       ):
            continue
         else:
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
      else:
         if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
    # finally, create HuggingFace model and load state dict
   _lowercase							     =       DetrForSegmentation(_snake_case       ) if is_panoptic else DetrForObjectDetection(_snake_case       )
   model.load_state_dict(_snake_case       )
   model.eval()
   # verify our conversion
   _lowercase							     =       detr(_snake_case       )
   _lowercase							     =       model(_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(_snake_case       ).mkdir(exist_ok=_snake_case       )
   model.save_pretrained(_snake_case       )
   image_processor.save_pretrained(_snake_case       )


if __name__ == "__main__":
  __UpperCamelCase       :    int         =		argparse.ArgumentParser()

  parser.add_argument(
      "--model_name", default="detr_resnet50", type=str, help="Name of the 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       :    str         =		parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)