



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_		 =					logging.get_logger(__name__)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str				)  ->     YolosConfig:
   _A      =       YolosConfig()

   # size of the architecture
   if "yolos_ti" in yolos_name:
      _A      =       192
      _A      =       768
      _A      =       12
      _A      =       3
      _A      =       [800, 1_333]
      _A      =       False
   elif yolos_name == "yolos_s_dWr":
      _A      =       330
      _A      =       14
      _A      =       6
      _A      =       1_320
   elif "yolos_s" in yolos_name:
      _A      =       384
      _A      =       1_536
      _A      =       12
      _A      =       6
   elif "yolos_b" in yolos_name:
      _A      =       [800, 1_344]

   _A      =       91
   _A      =       '''huggingface/label-files'''
   _A      =       '''coco-detection-id2label.json'''
   _A      =       json.load(open(hf_hub_download(_snake_case	,   _snake_case	,   repo_type='''dataset'''				)	,   '''r'''				)				)
   _A      =       {int(_snake_case				): v for k, v in idalabel.items()}
   _A      =       idalabel
   _A      =       {v: k for k, v in idalabel.items()}

   return config

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:dict	,   _snake_case	:YolosConfig	,   _snake_case	:bool = False				)  ->     Optional[int]:
   for i in range(config.num_hidden_layers				):
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      _A      =       state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''				)
      _A      =       state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''				)
      # next, add query, keys and values (in that order) to the state dict
      _A      =       in_proj_weight[: config.hidden_size, :]
      _A      =       in_proj_bias[: config.hidden_size]
      _A      =       in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      _A      =       in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      _A      =       in_proj_weight[-config.hidden_size :, :]
      _A      =       in_proj_bias[-config.hidden_size :]

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str				)  ->     str:
   if "backbone" in name:
      _A      =       name.replace('''backbone'''	,   '''vit'''				)
   if "cls_token" in name:
      _A      =       name.replace('''cls_token'''	,   '''embeddings.cls_token'''				)
   if "det_token" in name:
      _A      =       name.replace('''det_token'''	,   '''embeddings.detection_tokens'''				)
   if "mid_pos_embed" in name:
      _A      =       name.replace('''mid_pos_embed'''	,   '''encoder.mid_position_embeddings'''				)
   if "pos_embed" in name:
      _A      =       name.replace('''pos_embed'''	,   '''embeddings.position_embeddings'''				)
   if "patch_embed.proj" in name:
      _A      =       name.replace('''patch_embed.proj'''	,   '''embeddings.patch_embeddings.projection'''				)
   if "blocks" in name:
      _A      =       name.replace('''blocks'''	,   '''encoder.layer'''				)
   if "attn.proj" in name:
      _A      =       name.replace('''attn.proj'''	,   '''attention.output.dense'''				)
   if "attn" in name:
      _A      =       name.replace('''attn'''	,   '''attention.self'''				)
   if "norm1" in name:
      _A      =       name.replace('''norm1'''	,   '''layernorm_before'''				)
   if "norm2" in name:
      _A      =       name.replace('''norm2'''	,   '''layernorm_after'''				)
   if "mlp.fc1" in name:
      _A      =       name.replace('''mlp.fc1'''	,   '''intermediate.dense'''				)
   if "mlp.fc2" in name:
      _A      =       name.replace('''mlp.fc2'''	,   '''output.dense'''				)
   if "class_embed" in name:
      _A      =       name.replace('''class_embed'''	,   '''class_labels_classifier'''				)
   if "bbox_embed" in name:
      _A      =       name.replace('''bbox_embed'''	,   '''bbox_predictor'''				)
   if "vit.norm" in name:
      _A      =       name.replace('''vit.norm'''	,   '''vit.layernorm'''				)

   return name

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:dict	,   _snake_case	:YolosForObjectDetection				)  ->     dict:
   for key in orig_state_dict.copy().keys():
      _A      =       orig_state_dict.pop(_snake_case				)

      if "qkv" in key:
         _A      =       key.split('''.'''				)
         _A      =       int(key_split[2]				)
         _A      =       model.vit.encoder.layer[layer_num].attention.attention.all_head_size
         if "weight" in key:
            _A      =       val[:dim, :]
            _A      =       val[
                dim : dim * 2, :
            ]
            _A      =       val[-dim:, :]
         else:
            _A      =       val[:dim]
            _A      =       val[dim : dim * 2]
            _A      =       val[-dim:]
      else:
         _A      =       val

   return orig_state_dict

def 	SCREAMING_SNAKE_CASE_							(	)  ->     torch.Tensor:
   _A      =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   _A      =       Image.open(requests.get(_snake_case	,   stream=_snake_case				).raw				)
   return im

@torch.no_grad()
def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:str	,   _snake_case	:str	,   _snake_case	:bool = False				)  ->     Dict:
   _A      =       get_yolos_config(_snake_case				)

   # load original state_dict
   _A      =       torch.load(_snake_case	,   map_location='''cpu'''				)['''model''']

   # load 🤗 model
   _A      =       YolosForObjectDetection(_snake_case				)
   model.eval()
   _A      =       convert_state_dict(_snake_case	,   _snake_case				)
   model.load_state_dict(_snake_case				)

   # Check outputs on an image, prepared by YolosImageProcessor
   _A      =       800 if yolos_name != '''yolos_ti''' else 512
   _A      =       YolosImageProcessor(format='''coco_detection'''	,   size=_snake_case				)
   _A      =       image_processor(images=prepare_img()	,   return_tensors='''pt'''				)
   _A      =       model(**_snake_case				)
   _A  ,    _A      =       outputs.logits, outputs.pred_boxes

   _A  ,    _A      =       None, None
   if yolos_name == "yolos_ti":
      _A      =       torch.tensor(
          [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]				)
      _A      =       torch.tensor(
          [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]				)
   elif yolos_name == "yolos_s_200_pre":
      _A      =       torch.tensor(
          [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]				)
      _A      =       torch.tensor(
          [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]				)
   elif yolos_name == "yolos_s_300_pre":
      _A      =       torch.tensor(
          [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]				)
      _A      =       torch.tensor(
          [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]				)
   elif yolos_name == "yolos_s_dWr":
      _A      =       torch.tensor(
          [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]				)
      _A      =       torch.tensor(
          [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]				)
   elif yolos_name == "yolos_base":
      _A      =       torch.tensor(
          [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]				)
      _A      =       torch.tensor(
          [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]				)
   else:
      raise ValueError(F'''Unknown yolos_name: {yolos_name}'''				)

   assert torch.allclose(logits[0, :3, :3]	,   _snake_case	,   atol=1E-4				)
   assert torch.allclose(pred_boxes[0, :3, :3]	,   _snake_case	,   atol=1E-4				)

   Path(_snake_case				).mkdir(exist_ok=_snake_case				)
   print(F'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''				)
   model.save_pretrained(_snake_case				)
   print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
   image_processor.save_pretrained(_snake_case				)

   if push_to_hub:
      _A      =       {
          '''yolos_ti''': '''yolos-tiny''',
          '''yolos_s_200_pre''': '''yolos-small''',
          '''yolos_s_300_pre''': '''yolos-small-300''',
          '''yolos_s_dWr''': '''yolos-small-dwr''',
          '''yolos_base''': '''yolos-base''',
      }

      print('''Pushing to the hub...'''				)
      _A      =       model_mapping[yolos_name]
      image_processor.push_to_hub(_snake_case	,   organization='''hustvl'''				)
      model.push_to_hub(_snake_case	,   organization='''hustvl'''				)


if __name__ == "__main__":
   UpperCAmelCase_		 =					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--yolos_name""",
       default="""yolos_s_200_pre""",
       type=str,
       help=(
           """Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"""
           """ 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."""
       ),
   )
   parser.add_argument(
       """--checkpoint_path""", default=None, type=str, help="""Path to the original state dict (.pth file)."""
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )
   parser.add_argument(
       """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
   )

   UpperCAmelCase_		 =					parser.parse_args()
   convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

UpperCAmelCase_		 =					TypeVar("""T""")

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (position - 1) // 2

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (2 * position) + 1

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (2 * position) + 2


class 	lowerCamelCase__	(       Generic[T]):



 """simple docstring"""

 def __init__(    self					:					Optional[int]      )   ->	None:
    _A      =       []
    _A      =       {}
    _A      =       0

 def __len__(    self					:					str      )   ->	int:
    return self.elements

 def __repr__(    self					:					Optional[int]      )   ->	str:
    return str(self.heap      )

 def     snake_case_      (    self					:					str      )   ->	bool:
    # Check if the priority queue is empty
    return self.elements == 0

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Add an element with given priority to the queue
    self.heap.append((elem, weight)      )
    _A      =       self.elements
    self.elements += 1
    self._bubble_up(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple      )   ->	T:
    # Remove and return the element with lowest weight (highest priority)
    if self.elements > 1:
       self._swap_nodes(0			,						self.elements - 1      )
    _A  ,    _A      =       self.heap.pop()
    del self.position_map[elem]
    self.elements -= 1
    if self.elements > 0:
       _A  ,    _A      =       self.heap[0]
       self._bubble_down(__lowerCAmelCase      )
    return elem

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Update the weight of the given key
    _A      =       self.position_map[elem]
    _A      =       (elem, weight)
    if position > 0:
       _A      =       get_parent_position(__lowerCAmelCase      )
       _A  ,    _A      =       self.heap[parent_position]
       if parent_weight > weight:
          self._bubble_up(__lowerCAmelCase      )
       else:
          self._bubble_down(__lowerCAmelCase      )
    else:
       self._bubble_down(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					T      )   ->	None:
    # Place a node at the proper position (upward movement) [to be used internally
    # only]
    _A      =       self.position_map[elem]
    if curr_pos == 0:
       return None
    _A      =       get_parent_position(__lowerCAmelCase      )
    _A  ,    _A      =       self.heap[curr_pos]
    _A  ,    _A      =       self.heap[parent_position]
    if parent_weight > weight:
       self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
       return self._bubble_up(__lowerCAmelCase      )
    return None

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					T      )   ->	None:
    # Place a node at the proper position (downward movement) [to be used
    # internally only]
    _A      =       self.position_map[elem]
    _A  ,    _A      =       self.heap[curr_pos]
    _A      =       get_child_left_position(__lowerCAmelCase      )
    _A      =       get_child_right_position(__lowerCAmelCase      )
    if child_left_position < self.elements and child_right_position < self.elements:
       _A  ,    _A      =       self.heap[child_left_position]
       _A  ,    _A      =       self.heap[child_right_position]
       if child_right_weight < child_left_weight and child_right_weight < weight:
          self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    if child_left_position < self.elements:
       _A  ,    _A      =       self.heap[child_left_position]
       if child_left_weight < weight:
          self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    else:
       return None
    if child_right_position < self.elements:
       _A  ,    _A      =       self.heap[child_right_position]
       if child_right_weight < weight:
          self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    return None

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					int      )   ->	None:
    # Swap the nodes at the given positions
    _A      =       self.heap[nodea_pos][0]
    _A      =       self.heap[nodea_pos][0]
    _A  ,    _A      =       (
        self.heap[nodea_pos],
        self.heap[nodea_pos],
    )
    _A      =       nodea_pos
    _A      =       nodea_pos


class 	lowerCamelCase__	(       Generic[T]):



 """simple docstring"""

 def __init__(    self					:					str      )   ->	None:
    _A      =       {}
    _A      =       0

 def __repr__(    self					:					str      )   ->	str:
    return str(self.connections      )

 def __len__(    self					:					Dict      )   ->	int:
    return self.nodes

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					T      )   ->	None:
    # Add a node in the graph if it is not in the graph
    if node not in self.connections:
       _A      =       {}
       self.nodes += 1

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Add an edge between 2 nodes in the graph
    self.add_node(__lowerCAmelCase      )
    self.add_node(__lowerCAmelCase      )
    _A      =       weight
    _A      =       weight

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:GraphUndirectedWeighted[T]	,   )  ->     tuple[dict[T, int], dict[T, T | None]]:
   _A      =       {node: maxsize for node in graph.connections}
   _A      =       {node: None for node in graph.connections}

   _A      =       MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(_snake_case	,   _snake_case				)

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   _A      =       priority_queue.extract_min()
   _A      =       0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         _A      =       dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(_snake_case	,   dist[neighbour]				)
         _A      =       node

    # running prim's algorithm
   while not priority_queue.is_empty():
      _A      =       priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            _A      =       dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(_snake_case	,   dist[neighbour]				)
            _A      =       node
   return dist, parent



