



'''simple docstring'''

class __snake_case:


						'''simple docstring'''



						def __init__(   self      ,			A_      ,			A_ )      ->      str:
							lowerCAmelCase       =      name
							lowerCAmelCase       =      val


						def __str__(   self )      ->      Tuple:
							return f'{self.__class__.__name__}({self.name}, {self.val})'




						def __lt__(   self      ,			A_ )      ->      Any:
							return self.val < other.val







class __snake_case:


						'''simple docstring'''



						def __init__(   self      ,			A_ )      ->      Any:
							lowerCAmelCase       =      {}
							lowerCAmelCase       =      {}
							lowerCAmelCase       =      self.build_heap(lowercase_ )


						def __getitem__(   self      ,			A_ )      ->      List[str]:
							return self.get_value(lowercase_ )


						def 							__snake_case       (   self      ,			A_ )      ->      Optional[int]:
							return (idx - 1) // 2


						def 							__snake_case       (   self      ,			A_ )      ->      int:
							return idx * 2 + 1


						def 							__snake_case       (   self      ,			A_ )      ->      List[Any]:
							return idx * 2 + 2


						def 							__snake_case       (   self      ,			A_ )      ->      Any:
							return self.heap_dict[key]


						def 							__snake_case       (   self      ,			A_ )      ->      str:
							lowerCAmelCase       =      len(lowercase_ ) - 1
							lowerCAmelCase       =      self.get_parent_idx(lowercase_ )

							for idx, i in enumerate(lowercase_ ):
								lowerCAmelCase       =      idx
								lowerCAmelCase       =      i.val

							for i in range(lowercase_      ,			-1      ,			-1 ):
								self.sift_down(lowercase_      ,			lowercase_ )
							return array


						def 							__snake_case       (   self      ,			A_      ,			A_ )      ->      List[str]:
							while True:
								lowerCAmelCase       =      self.get_left_child_idx(lowercase_ )  # noqa: E741
								lowerCAmelCase       =      self.get_right_child_idx(lowercase_ )

								lowerCAmelCase       =      idx
								if l < len(lowercase_ ) and array[l] < array[idx]:
									lowerCAmelCase       =      l
								if r < len(lowercase_ ) and array[r] < array[smallest]:
									lowerCAmelCase       =      r

								if smallest != idx:
									lowerCAmelCase,  lowerCAmelCase       =      array[smallest], array[idx]
									(
									    (
									    lowerCAmelCase     
									),  (
									    lowerCAmelCase     
									),       
									)       =      (
									    self.idx_of_element[array[smallest]],
									    self.idx_of_element[array[idx]],
									)
									lowerCAmelCase       =      smallest
								else:
									break


						def 							__snake_case       (   self      ,			A_ )      ->      Dict:
							lowerCAmelCase       =      self.get_parent_idx(lowercase_ )
							while p >= 0 and self.heap[p] > self.heap[idx]:
								lowerCAmelCase,  lowerCAmelCase       =      self.heap[idx], self.heap[p]
								lowerCAmelCase,  lowerCAmelCase       =      (
								    self.idx_of_element[self.heap[idx]],
								    self.idx_of_element[self.heap[p]],
								)
								lowerCAmelCase       =      p
								lowerCAmelCase       =      self.get_parent_idx(lowercase_ )


						def 							__snake_case       (   self )      ->      List[str]:
							return self.heap[0]


						def 							__snake_case       (   self )      ->      Optional[int]:
							lowerCAmelCase,  lowerCAmelCase       =      self.heap[-1], self.heap[0]
							lowerCAmelCase,  lowerCAmelCase       =      (
							    self.idx_of_element[self.heap[-1]],
							    self.idx_of_element[self.heap[0]],
							)

							lowerCAmelCase       =      self.heap.pop()
							del self.idx_of_element[x]
							self.sift_down(0      ,			self.heap )
							return x


						def 							__snake_case       (   self      ,			A_ )      ->      Tuple:
							self.heap.append(lowercase_ )
							lowerCAmelCase       =      len(self.heap ) - 1
							lowerCAmelCase       =      node.val
							self.sift_up(len(self.heap ) - 1 )


						def 							__snake_case       (   self )      ->      Union[str, Any]:
							return len(self.heap ) == 0




						def 							__snake_case       (   self      ,			A_      ,			A_ )      ->      int:
							assert (
							    self.heap[self.idx_of_element[node]].val > new_value
							), "newValue must be less that current value"
							lowerCAmelCase       =      new_value
							lowerCAmelCase       =      new_value
							self.sift_up(self.idx_of_element[node] )







UpperCAmelCase      						=       Node('R', -1)
UpperCAmelCase      						=       Node('B', 6)
UpperCAmelCase      						=       Node('A', 3)
UpperCAmelCase      						=       Node('X', 1)
UpperCAmelCase      						=       Node('E', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
UpperCAmelCase      						=       MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
				print(i)

print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
				print(i)

if __name__ == "__main__":
				import doctest

				doctest.testmod()



import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase:						List[str]   				=       logging.get_logger()






def   _lowercase(					__a				:						int    ,     __a				:						str    ,     __a				:						LevitConfig    ,     __a				:						Path    ,     __a				:						bool = True     ):
							print(f"""Converting {name}..."""     )

							with torch.no_grad():
														if hidden_sizes == 128:
																					if name[-1] == "S":
																												a__      =timm.create_model('levit_128s'    ,     pretrained=__a     )
																					else:
																												a__      =timm.create_model('levit_128'    ,     pretrained=__a     )
														if hidden_sizes == 192:
																					a__      =timm.create_model('levit_192'    ,     pretrained=__a     )
														if hidden_sizes == 256:
																					a__      =timm.create_model('levit_256'    ,     pretrained=__a     )
														if hidden_sizes == 384:
																					a__      =timm.create_model('levit_384'    ,     pretrained=__a     )

														from_model.eval()
														a__      =LevitForImageClassificationWithTeacher(__a     ).eval()
														a__      =OrderedDict()

														a__      =from_model.state_dict()
														a__      =list(from_model.state_dict().keys()     )
														a__      =list(our_model.state_dict().keys()     )
														print(len(__a     )    ,     len(__a     )     )
														for i in range(len(__a     )     ):
																					a__      =weights[og_keys[i]]
														our_model.load_state_dict(__a     )

														a__      =torch.randn((2, 3, 224, 224)     )
														a__      =from_model(__a     )
														a__      =our_model(__a     ).logits

							assert torch.allclose(__a    ,     __a     ), "The model logits don't match the original one."

							a__      =name
							print(__a     )

							if push_to_hub:
														our_model.save_pretrained(save_directory / checkpoint_name     )
														a__      =LevitImageProcessor()
														image_processor.save_pretrained(save_directory / checkpoint_name     )

														print(f"""Pushed {checkpoint_name}"""     )






def   _lowercase(					__a				:						Path    ,     __a				:						str = None    ,     __a				:						bool = True     ):
							a__      ='imagenet-1k-id2label.json'
							a__      =1000
							a__      =(1, num_labels)

							a__      ='huggingface/label-files'
							a__      =num_labels
							a__      =json.load(open(hf_hub_download(__a    ,     __a    ,     repo_type='dataset'     )    ,     'r'     )     )
							a__      ={int(__a     ): v for k, v in idalabel.items()}

							a__      =idalabel
							a__      ={v: k for k, v in idalabel.items()}

							a__      =partial(__a    ,     num_labels=__a    ,     idalabel=__a    ,     labelaid=__a     )

							a__      ={
							    'levit-128S': 128,
							    'levit-128': 128,
							    'levit-192': 192,
							    'levit-256': 256,
							    'levit-384': 384,
							}

							a__      ={
							    'levit-128S': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]    ,     num_attention_heads=[4, 6, 8]    ,     depths=[2, 3, 4]    ,     key_dim=[16, 16, 16]    ,     drop_path_rate=0    ,     ),
							    'levit-128': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]    ,     num_attention_heads=[4, 8, 12]    ,     depths=[4, 4, 4]    ,     key_dim=[16, 16, 16]    ,     drop_path_rate=0    ,     ),
							    'levit-192': ImageNetPreTrainedConfig(
							        hidden_sizes=[192, 288, 384]    ,     num_attention_heads=[3, 5, 6]    ,     depths=[4, 4, 4]    ,     key_dim=[32, 32, 32]    ,     drop_path_rate=0    ,     ),
							    'levit-256': ImageNetPreTrainedConfig(
							        hidden_sizes=[256, 384, 512]    ,     num_attention_heads=[4, 6, 8]    ,     depths=[4, 4, 4]    ,     key_dim=[32, 32, 32]    ,     drop_path_rate=0    ,     ),
							    'levit-384': ImageNetPreTrainedConfig(
							        hidden_sizes=[384, 512, 768]    ,     num_attention_heads=[6, 9, 12]    ,     depths=[4, 4, 4]    ,     key_dim=[32, 32, 32]    ,     drop_path_rate=0.1    ,     ),
							}

							if model_name:
														convert_weight_and_push(
														    names_to_hidden_sizes[model_name]    ,     __a    ,     names_to_config[model_name]    ,     __a    ,     __a     )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]    ,     __a    ,     __a    ,     __a    ,     __a     )
							return config, expected_shape


if __name__ == "__main__":
	_lowerCAmelCase:						Any   				=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default=None,
	    type=str,
	    help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default='levit-dump-folder/',
	    type=Path,
	    required=False,
	    help='Path to the output PyTorch model directory.',
	)
	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',
	)

	_lowerCAmelCase:						Union[str, Any]   				=       parser.parse_args()
	_lowerCAmelCase:						Path   				=       args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)

