

import sys
from collections import defaultdict






class  UpperCAmelCase__	:






  """simple docstring"""


  def __init__(							self		:	Any      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					[]






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	Any      )	->							Optional[Any]:
       return self.node_position[vertex]






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	int      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					pos






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	int      )	->							Tuple:
       if start > size // 2 - 1:
            return
       else:
            if 2 * start + 2 >= size:
                 SCREAMING_SNAKE_CASE__					=					2 * start + 1
            else:
                 if heap[2 * start + 1] < heap[2 * start + 2]:
                      SCREAMING_SNAKE_CASE__					=					2 * start + 1
                 else:
                      SCREAMING_SNAKE_CASE__					=					2 * start + 2
            if heap[smallest_child] < heap[start]:
                 SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					heap[smallest_child], positions[smallest_child]
                 SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					(
                     heap[start],
                     positions[start],
                 )
                 SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					temp, tempa

                 SCREAMING_SNAKE_CASE__					=					self.get_position(positions[smallest_child]      )
                 self.set_position(
                     positions[smallest_child]			,     self.get_position(positions[start]      )      )
                 self.set_position(positions[start]			,     __a      )

                 self.top_to_bottom(__a			,     __a			,     __a			,     __a      )






  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	List[Any]      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					position[index]

       while index != 0:
            SCREAMING_SNAKE_CASE__					=					int((index - 2) / 2      ) if index % 2 == 0 else int((index - 1) / 2      )

            if val < heap[parent]:
                 SCREAMING_SNAKE_CASE__					=					heap[parent]
                 SCREAMING_SNAKE_CASE__					=					position[parent]
                 self.set_position(position[parent]			,     __a      )
            else:
                 SCREAMING_SNAKE_CASE__					=					val
                 SCREAMING_SNAKE_CASE__					=					temp
                 self.set_position(__a			,     __a      )
                 break
            SCREAMING_SNAKE_CASE__					=					parent
       else:
            SCREAMING_SNAKE_CASE__					=					val
            SCREAMING_SNAKE_CASE__					=					temp
            self.set_position(__a			,     0      )






  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	Tuple      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					len(__a      ) // 2 - 1
       for i in range(__a			,     -1			,     -1      ):
            self.top_to_bottom(__a			,     __a			,     len(__a      )			,     __a      )







  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Optional[Any]      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					positions[0]
       SCREAMING_SNAKE_CASE__					=					sys.maxsize
       self.top_to_bottom(__a			,     0			,     len(__a      )			,     __a      )
       return temp



def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					Heap()

     SCREAMING_SNAKE_CASE__					=					[0] * len(__snake_case       )
     SCREAMING_SNAKE_CASE__					=					[-1] * len(__snake_case       )  # Neighboring Tree Vertex of selected vertex
     # Minimum Distance of explored vertex with neighboring vertex of partial tree
     # formed in graph
     SCREAMING_SNAKE_CASE__					=					[]  # Heap of Distance of vertices from their neighboring vertex
     SCREAMING_SNAKE_CASE__					=					[]

     for vertex in range(len(__snake_case       )       ):
          distance_tv.append(sys.maxsize       )
          positions.append(__snake_case       )
          heap.node_position.append(__snake_case       )

     SCREAMING_SNAKE_CASE__					=					[]
     SCREAMING_SNAKE_CASE__					=					1
     SCREAMING_SNAKE_CASE__					=					sys.maxsize
     for neighbor, distance in adjacency_list[0]:
          SCREAMING_SNAKE_CASE__					=					0
          SCREAMING_SNAKE_CASE__					=					distance
     heap.heapify(__snake_case   ,	__snake_case       )

     for _ in range(1   ,	len(__snake_case       )       ):
          SCREAMING_SNAKE_CASE__					=					heap.delete_minimum(__snake_case   ,	__snake_case       )
          if visited[vertex] == 0:
               tree_edges.append((nbr_tv[vertex], vertex)       )
               SCREAMING_SNAKE_CASE__					=					1
               for neighbor, distance in adjacency_list[vertex]:
                    if (
                        visited[neighbor] == 0
                        and distance < distance_tv[heap.get_position(__snake_case       )]
                    ):
                         SCREAMING_SNAKE_CASE__					=					distance
                         heap.bottom_to_top(
                             __snake_case   ,	heap.get_position(__snake_case       )   ,	__snake_case   ,	__snake_case       )
                         SCREAMING_SNAKE_CASE__					=					vertex
     return tree_edges


if __name__ == "__main__":  # pragma: no cover
       # < --------- Prims Algorithm --------- >
       _SCREAMING_SNAKE_CASE         =    int(input('''Enter number of edges: ''').strip())
       _SCREAMING_SNAKE_CASE         =    defaultdict(list)
       for _ in range(edges_number):
              _SCREAMING_SNAKE_CASE         =    [int(x) for x in input().strip().split()]
              adjacency_list[edge[0]].append([edge[1], edge[2]])
              adjacency_list[edge[1]].append([edge[0], edge[2]])
       print(prisms_algorithm(adjacency_list))





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 PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE  :						Dict         =    logging.get_logger(__name__)
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					original_name.split('''.'''       )[0]
     SCREAMING_SNAKE_CASE__					=					key.split('''.'''       )
     SCREAMING_SNAKE_CASE__					=					int(key_list[key_list.index(_A       ) - 2]       )
     SCREAMING_SNAKE_CASE__					=					int(key_list[key_list.index(_A       ) - 1]       )
     SCREAMING_SNAKE_CASE__					=					orig_block_num - offset

     SCREAMING_SNAKE_CASE__					=					key.replace(F'''{orig_block_num}.{layer_num}.{original_name}'''   ,	F'''block.{new_block_num}.{layer_num}.{new_name}'''       )
     return key
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					OrderedDict()
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					0, 0
     for key, value in state_dict.items():
          if key.startswith('''network'''       ):
               SCREAMING_SNAKE_CASE__					=					key.replace('''network'''   ,	'''poolformer.encoder'''       )
          if "proj" in key:
               # Works for the first embedding as well as the internal embedding layers
               if key.endswith('''bias'''       ) and "patch_embed" not in key:
                    patch_emb_offset += 1
               SCREAMING_SNAKE_CASE__					=					key[: key.find('''proj'''       )]
               SCREAMING_SNAKE_CASE__					=					key.replace(_A   ,	F'''patch_embeddings.{total_embed_found}.'''       )
               SCREAMING_SNAKE_CASE__					=					key.replace('''proj'''   ,	'''projection'''       )
               if key.endswith('''bias'''       ):
                    total_embed_found += 1
          if "patch_embeddings" in key:
               SCREAMING_SNAKE_CASE__					=					'''poolformer.encoder.''' + key
          if "mlp.fc1" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''mlp.fc1'''   ,	'''output.conv1'''       )
          if "mlp.fc2" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''mlp.fc2'''   ,	'''output.conv2'''       )
          if "norm1" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''norm1'''   ,	'''before_norm'''       )
          if "norm2" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''norm2'''   ,	'''after_norm'''       )
          if "layer_scale_1" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''layer_scale_1'''   ,	'''layer_scale_1'''       )
          if "layer_scale_2" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''layer_scale_2'''   ,	'''layer_scale_2'''       )
          if "head" in key:
               SCREAMING_SNAKE_CASE__					=					key.replace('''head'''   ,	'''classifier'''       )
          SCREAMING_SNAKE_CASE__					=					value
     return new_state_dict
def        UpperCAmelCase_     (		):





     '''simple docstring'''




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

     return image
@torch.no_grad()
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					PoolFormerConfig()

     # set attributes based on model_name
     SCREAMING_SNAKE_CASE__					=					'''huggingface/label-files'''
     SCREAMING_SNAKE_CASE__					=					model_name[-3:]
     SCREAMING_SNAKE_CASE__					=					10_00
     SCREAMING_SNAKE_CASE__					=					'''imagenet-1k-id2label.json'''
     SCREAMING_SNAKE_CASE__					=					(1, 10_00)

     # set config attributes
     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()}
     if size == "s12":
          SCREAMING_SNAKE_CASE__					=					[2, 2, 6, 2]
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					0.9
     elif size == "s24":
          SCREAMING_SNAKE_CASE__					=					[4, 4, 12, 4]
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					0.9
     elif size == "s36":
          SCREAMING_SNAKE_CASE__					=					[6, 6, 18, 6]
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					1e-6
          SCREAMING_SNAKE_CASE__					=					0.9
     elif size == "m36":
          SCREAMING_SNAKE_CASE__					=					[6, 6, 18, 6]
          SCREAMING_SNAKE_CASE__					=					[96, 1_92, 3_84, 7_68]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					1e-6
          SCREAMING_SNAKE_CASE__					=					0.9_5
     elif size == "m48":
          SCREAMING_SNAKE_CASE__					=					[8, 8, 24, 8]
          SCREAMING_SNAKE_CASE__					=					[96, 1_92, 3_84, 7_68]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					1e-6
          SCREAMING_SNAKE_CASE__					=					0.9_5
     else:
          raise ValueError(F'''Size {size} not supported'''       )

     # load image processor
     SCREAMING_SNAKE_CASE__					=					PoolFormerImageProcessor(crop_pct=_A       )

     # Prepare image
     SCREAMING_SNAKE_CASE__					=					prepare_img()
     SCREAMING_SNAKE_CASE__					=					image_processor(images=_A   ,	return_tensors='''pt'''       ).pixel_values

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

     # load original state dict
     SCREAMING_SNAKE_CASE__					=					torch.load(_A   ,	map_location=torch.device('''cpu'''       )       )

     # rename keys
     SCREAMING_SNAKE_CASE__					=					rename_keys(_A       )

     # create HuggingFace model and load state dict
     SCREAMING_SNAKE_CASE__					=					PoolFormerForImageClassification(_A       )
     model.load_state_dict(_A       )
     model.eval()

     # Define image processor
     SCREAMING_SNAKE_CASE__					=					PoolFormerImageProcessor(crop_pct=_A       )
     SCREAMING_SNAKE_CASE__					=					image_processor(images=prepare_img()   ,	return_tensors='''pt'''       ).pixel_values

     # forward pass
     SCREAMING_SNAKE_CASE__					=					model(_A       )
     SCREAMING_SNAKE_CASE__					=					outputs.logits

     # define expected logit slices for different models
     if size == "s12":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]       )
     elif size == "s24":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]       )
     elif size == "s36":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]       )
     elif size == "m36":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]       )
     elif size == "m48":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3]       )
     else:
          raise ValueError(F'''Size {size} not supported'''       )

     # verify logits
     assert logits.shape == expected_shape
     assert torch.allclose(logits[0, :3]   ,	_A   ,	atol=1e-2       )

     # finally, 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       )
     print(F'''Saving image processor to {pytorch_dump_folder_path}'''       )
     image_processor.save_pretrained(_A       )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    argparse.ArgumentParser()

       parser.add_argument(
           '''--model_name''',
           default='''poolformer_s12''',
           type=str,
           help='''Name of the model you\'d like to convert.''',
       )
       parser.add_argument(
           '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
       )
       _SCREAMING_SNAKE_CASE  :						Tuple         =    parser.parse_args()
       convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)


