





import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling







def       UpperCAmelCase							(     _lowerCamelCase      :  int			,   _lowerCamelCase      :  Dict     ):





      '''simple docstring'''


      SCREAMING_SNAKE_CASE__     :				Optional[int]   =					old_name

      if "patch_embed" in old_name:
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					old_name.split("."     )

            if layer == "0":
                  SCREAMING_SNAKE_CASE__     :				Tuple   =					old_name.replace("0"			,   "convolution1"     )
            elif layer == "1":
                  SCREAMING_SNAKE_CASE__     :				int   =					old_name.replace("1"			,   "batchnorm_before"     )
            elif layer == "3":
                  SCREAMING_SNAKE_CASE__     :				List[str]   =					old_name.replace("3"			,   "convolution2"     )
            else:
                  SCREAMING_SNAKE_CASE__     :				List[str]   =					old_name.replace("4"			,   "batchnorm_after"     )

      if "network" in old_name and re.search(r"\d\.\d"			,   _lowerCamelCase     ):
            SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					r"\b\d{2}\b"
            if bool(re.search(_lowerCamelCase			,   _lowerCamelCase     )     ):
                  SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					re.search(r"\d\.\d\d."			,   _lowerCamelCase     ).group()
            else:
                  SCREAMING_SNAKE_CASE__     :				str   =					re.search(r"\d\.\d."			,   _lowerCamelCase     ).group()
            if int(match[0]     ) < 6:
                  SCREAMING_SNAKE_CASE__     :				Tuple   =					old_name.replace(_lowerCamelCase			,   ""     )
                  SCREAMING_SNAKE_CASE__     :				List[Any]   =					trimmed_name.replace("network"			,   match[0] + ".meta4D_layers.blocks." + match[2:-1]     )
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					"intermediate_stages." + trimmed_name
            else:
                  SCREAMING_SNAKE_CASE__     :				str   =					old_name.replace(_lowerCamelCase			,   ""     )
                  if int(match[2]     ) < num_meta4D_last_stage:
                        SCREAMING_SNAKE_CASE__     :				str   =					trimmed_name.replace("network"			,   "meta4D_layers.blocks." + match[2]     )
                  else:
                        SCREAMING_SNAKE_CASE__     :				Optional[int]   =					str(int(match[2]     ) - num_meta4D_last_stage     )
                        SCREAMING_SNAKE_CASE__     :				List[str]   =					trimmed_name.replace("network"			,   "meta3D_layers.blocks." + layer_index     )
                        if "norm1" in old_name:
                              SCREAMING_SNAKE_CASE__     :				Optional[int]   =					trimmed_name.replace("norm1"			,   "layernorm1"     )
                        elif "norm2" in old_name:
                              SCREAMING_SNAKE_CASE__     :				List[Any]   =					trimmed_name.replace("norm2"			,   "layernorm2"     )
                        elif "fc1" in old_name:
                              SCREAMING_SNAKE_CASE__     :				Dict   =					trimmed_name.replace("fc1"			,   "linear_in"     )
                        elif "fc2" in old_name:
                              SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					trimmed_name.replace("fc2"			,   "linear_out"     )

                  SCREAMING_SNAKE_CASE__     :				Any   =					"last_stage." + trimmed_name

      elif "network" in old_name and re.search(r".\d."			,   _lowerCamelCase     ):
            SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					old_name.replace("network"			,   "intermediate_stages"     )

      if "fc" in new_name:
            SCREAMING_SNAKE_CASE__     :				Dict   =					new_name.replace("fc"			,   "convolution"     )
      elif ("norm1" in new_name) and ("layernorm1" not in new_name):
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					new_name.replace("norm1"			,   "batchnorm_before"     )
      elif ("norm2" in new_name) and ("layernorm2" not in new_name):
            SCREAMING_SNAKE_CASE__     :				Optional[int]   =					new_name.replace("norm2"			,   "batchnorm_after"     )
      if "proj" in new_name:
            SCREAMING_SNAKE_CASE__     :				Dict   =					new_name.replace("proj"			,   "projection"     )
      if "dist_head" in new_name:
            SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					new_name.replace("dist_head"			,   "distillation_classifier"     )
      elif "head" in new_name:
            SCREAMING_SNAKE_CASE__     :				int   =					new_name.replace("head"			,   "classifier"     )
      elif "patch_embed" in new_name:
            SCREAMING_SNAKE_CASE__     :				List[Any]   =					"efficientformer." + new_name
      elif new_name == "norm.weight" or new_name == "norm.bias":
            SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					new_name.replace("norm"			,   "layernorm"     )
            SCREAMING_SNAKE_CASE__     :				Dict   =					"efficientformer." + new_name
      else:
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					"efficientformer.encoder." + new_name

      return new_name







def       UpperCAmelCase							(     _lowerCamelCase      :  Dict			,   _lowerCamelCase      :  Optional[int]     ):





      '''simple docstring'''


      for key in checkpoint.copy().keys():
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					checkpoint.pop(_lowerCamelCase     )
            SCREAMING_SNAKE_CASE__     :				List[str]   =					val

      return checkpoint







def       UpperCAmelCase							(     ):





      '''simple docstring'''


      SCREAMING_SNAKE_CASE__     :				Tuple   =					"http://images.cocodataset.org/val2017/000000039769.jpg"
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					Image.open(requests.get(_lowerCamelCase			,   stream=_lowerCamelCase     ).raw     )

      return image







def       UpperCAmelCase							(     _lowerCamelCase      :  Path			,   _lowerCamelCase      :  Path			,   _lowerCamelCase      :  Path			,   _lowerCamelCase      :  bool     ):





      '''simple docstring'''


      SCREAMING_SNAKE_CASE__     :				Tuple   =					torch.load(_lowerCamelCase			,   map_location="cpu"     )["model"]
      SCREAMING_SNAKE_CASE__     :				Tuple   =					EfficientFormerConfig.from_json_file(_lowerCamelCase     )
      SCREAMING_SNAKE_CASE__     :				Tuple   =					EfficientFormerForImageClassificationWithTeacher(_lowerCamelCase     )
      SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					"_".join(checkpoint_path.split("/"     )[-1].split("."     )[0].split("_"     )[:-1]     )

      SCREAMING_SNAKE_CASE__     :				Dict   =					config.depths[-1] - config.num_metaad_blocks + 1
      SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					convert_torch_checkpoint(_lowerCamelCase			,   _lowerCamelCase     )

      model.load_state_dict(_lowerCamelCase     )
      model.eval()

      SCREAMING_SNAKE_CASE__     :				str   =					{
          "bilinear": PILImageResampling.BILINEAR,
          "bicubic": PILImageResampling.BICUBIC,
          "nearest": PILImageResampling.NEAREST,
      }

      # prepare image
      SCREAMING_SNAKE_CASE__     :				int   =					prepare_img()
      SCREAMING_SNAKE_CASE__     :				List[Any]   =					256
      SCREAMING_SNAKE_CASE__     :				int   =					224
      SCREAMING_SNAKE_CASE__     :				Dict   =					EfficientFormerImageProcessor(
          size={"shortest_edge": image_size}			,   crop_size={"height": crop_size, "width": crop_size}			,   resample=pillow_resamplings["bicubic"]			,   )
      SCREAMING_SNAKE_CASE__     :				List[str]   =					processor(images=_lowerCamelCase			,   return_tensors="pt"     ).pixel_values

      # original processing pipeline
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					Compose(
          [
              Resize(_lowerCamelCase			,   interpolation=pillow_resamplings["bicubic"]     ),
              CenterCrop(_lowerCamelCase     ),
              ToTensor(),
              Normalize(_lowerCamelCase			,   _lowerCamelCase     ),
          ]     )
      SCREAMING_SNAKE_CASE__     :				str   =					image_transforms(_lowerCamelCase     ).unsqueeze(0     )

      assert torch.allclose(_lowerCamelCase			,   _lowerCamelCase     )

      SCREAMING_SNAKE_CASE__     :				str   =					model(_lowerCamelCase     )
      SCREAMING_SNAKE_CASE__     :				int   =					outputs.logits

      SCREAMING_SNAKE_CASE__     :				Tuple   =					(1, 1_000)

      if "l1" in model_name:
            SCREAMING_SNAKE_CASE__     :				Dict   =					torch.Tensor(
                [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]     )
            assert torch.allclose(logits[0, :10]			,   _lowerCamelCase			,   atol=1E-3     )
            assert logits.shape == expected_shape
      elif "l3" in model_name:
            SCREAMING_SNAKE_CASE__     :				int   =					torch.Tensor(
                [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]     )
            assert torch.allclose(logits[0, :10]			,   _lowerCamelCase			,   atol=1E-3     )
            assert logits.shape == expected_shape
      elif "l7" in model_name:
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					torch.Tensor(
                [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]     )
            assert logits.shape == expected_shape
      else:
            raise ValueError(
                f"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""     )

      # Save Checkpoints
      Path(_lowerCamelCase     ).mkdir(exist_ok=_lowerCamelCase     )
      model.save_pretrained(_lowerCamelCase     )
      print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""     )
      processor.save_pretrained(_lowerCamelCase     )
      print(f"""Processor successfuly saved at {pytorch_dump_path}"""     )

      if push_to_hub:
            print("Pushing model to the hub..."     )

            model.push_to_hub(
                repo_id=f"""Bearnardd/{pytorch_dump_path}"""			,   commit_message="Add model"			,   use_temp_dir=_lowerCamelCase			,   )
            processor.push_to_hub(
                repo_id=f"""Bearnardd/{pytorch_dump_path}"""			,   commit_message="Add image processor"			,   use_temp_dir=_lowerCamelCase			,   )


if __name__ == "__main__":
    __lowercase       :int       =  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--pytorch_model_path",
        default=None,
        type=str,
        required=True,
        help="Path to EfficientFormer pytorch checkpoint.",
    )
    parser.add_argument(
        "--config_file",
        default=None,
        type=str,
        required=True,
        help="The json file for EfficientFormer model config.",
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )

    parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
    parser.add_argument(
        "--no-push_to_hub",
        dest="push_to_hub",
        action="store_false",
        help="Do not push model and image processor to the hub",
    )
    parser.set_defaults(push_to_hub=True)

    __lowercase       :List[Any]       =  parser.parse_args()
    convert_efficientformer_checkpoint(
        checkpoint_path=args.pytorch_model_path,
        efficientformer_config_file=args.config_file,
        pytorch_dump_path=args.pytorch_dump_path,
        push_to_hub=args.push_to_hub,
    )






import sys
from collections import defaultdict



class    _a     :






							"""simple docstring"""

							def __init__(      self				:     Any    )	->Dict:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					[]

							def      A_  (      self				:     int							,	a				:     List[str]    )	->Dict:
													return self.node_position[vertex]

							def      A_  (      self				:     Optional[Any]							,	a				:     Any							,	a				:     List[str]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				str   =					pos

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

																									SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					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      A_  (      self				:     Union[str, Any]							,	a				:     Tuple							,	a				:     Tuple							,	a				:     Union[str, Any]							,	a				:     List[Any]    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					position[index]

													while index != 0:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					int((index - 2) / 2    ) if index % 2 == 0 else int((index - 1) / 2    )

																			if val < heap[parent]:
																									SCREAMING_SNAKE_CASE__     :				List[Any]   =					heap[parent]
																									SCREAMING_SNAKE_CASE__     :				str   =					position[parent]
																									self.set_position(position[parent]							,	a    )
																			else:
																									SCREAMING_SNAKE_CASE__     :				int   =					val
																									SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					temp
																									self.set_position(a							,	a    )
																									break
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					parent
													else:
																			SCREAMING_SNAKE_CASE__     :				int   =					val
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					temp
																			self.set_position(a							,	0    )

							def      A_  (      self				:     Union[str, Any]							,	a				:     int							,	a				:     List[str]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					len(a    ) // 2 - 1
													for i in range(a							,	-1							,	-1    ):
																			self.top_to_bottom(a							,	a							,	len(a    )							,	a    )






							def      A_  (      self				:     Dict							,	a				:     List[Any]							,	a				:     Dict    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				Any   =					positions[0]
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					sys.maxsize
													self.top_to_bottom(a							,	0							,	len(a    )							,	a    )
													return temp







def       UpperCAmelCase							(     _lowerCamelCase      :  str     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					Heap()

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

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

						SCREAMING_SNAKE_CASE__     :				List[Any]   =					[]
						SCREAMING_SNAKE_CASE__     :				int   =					1
						SCREAMING_SNAKE_CASE__     :				int   =					sys.maxsize
						for neighbor, distance in adjacency_list[0]:
												SCREAMING_SNAKE_CASE__     :				int   =					0
												SCREAMING_SNAKE_CASE__     :				List[str]   =					distance
						heap.heapify(_lowerCamelCase			,   _lowerCamelCase     )

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


if __name__ == "__main__":  # pragma: no cover
				# < --------- Prims Algorithm --------- >
				__lowercase       :Union[str, Any]       =  int(input("Enter number of edges: ").strip())
				__lowercase       :Dict       =  defaultdict(list)
				for _ in range(edges_number):
								__lowercase       :Any       =  [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))