






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()
a_     :   Optional[int]							=    logging.get_logger()


def        _a  (     UpperCamelCase_					:				int    ,   UpperCamelCase_					:				str    ,   UpperCamelCase_					:				LevitConfig    ,   UpperCamelCase_					:				Path    ,   UpperCamelCase_					:				bool = True   ) ->   Dict:
						"""simple docstring"""







						print(F"Converting {name}..."   )

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

												from_model.eval()
												lowerCAmelCase__       =						LevitForImageClassificationWithTeacher(UpperCamelCase_   ).eval()
												lowerCAmelCase__       =						OrderedDict()

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

												lowerCAmelCase__       =						torch.randn((2, 3, 224, 224)   )
												lowerCAmelCase__       =						from_model(UpperCamelCase_   )
												lowerCAmelCase__       =						our_model(UpperCamelCase_   ).logits

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

						lowerCAmelCase__       =						name
						print(UpperCamelCase_   )

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

												print(F"Pushed {checkpoint_name}"   )


def        _a  (     UpperCamelCase_					:				Path    ,   UpperCamelCase_					:				str = None    ,   UpperCamelCase_					:				bool = True   ) ->   str:
						"""simple docstring"""







						lowerCAmelCase__       =						"imagenet-1k-id2label.json"
						lowerCAmelCase__       =						1_000
						lowerCAmelCase__       =						(1, num_labels)

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

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

						lowerCAmelCase__       =						partial(UpperCamelCase_    ,   num_labels=UpperCamelCase_    ,   idalabel=UpperCamelCase_    ,   labelaid=UpperCamelCase_   )

						lowerCAmelCase__       =						{
						    "levit-128S": 128,
						    "levit-128": 128,
						    "levit-192": 192,
						    "levit-256": 256,
						    "levit-384": 384,
						}

						lowerCAmelCase__       =						{
						    "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]    ,   UpperCamelCase_    ,   names_to_config[model_name]    ,   UpperCamelCase_    ,   UpperCamelCase_   )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(names_to_hidden_sizes[model_name]    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )
						return config, expected_shape


if __name__ == "__main__":
	a_     :   Union[str, 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''',
	)

	a_     :   Any							=    parser.parse_args()
	a_     :   int							=    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)














def        _a  (     UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				List[str]   ) ->   List[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						0
						lowerCAmelCase__       =						len(UpperCamelCase_   ) - 1

						while left <= right:
												# avoid divided by 0 during interpolation
												if sorted_collection[left] == sorted_collection[right]:
																		if sorted_collection[left] == item:
																								return left
																		else:
																								return None

												lowerCAmelCase__       =						left + ((item - sorted_collection[left]) * (right - left)) // (
												    sorted_collection[right] - sorted_collection[left]
												)

												# out of range check
												if point < 0 or point >= len(UpperCamelCase_   ):
																		return None

												lowerCAmelCase__       =						sorted_collection[point]
												if current_item == item:
																		return point
												else:
																		if point < left:
																								lowerCAmelCase__       =						left
																								lowerCAmelCase__       =						point
																		elif point > right:
																								lowerCAmelCase__       =						right
																								lowerCAmelCase__       =						point
																		else:
																								if item < current_item:
																														lowerCAmelCase__       =						point - 1
																								else:
																														lowerCAmelCase__       =						point + 1
						return None


def        _a  (     UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				int    ,   UpperCamelCase_					:				int   ) ->   List[Any]:
						"""simple docstring"""







						if sorted_collection[left] == sorted_collection[right]:
												if sorted_collection[left] == item:
																		return left
												else:
																		return None

						lowerCAmelCase__       =						left + ((item - sorted_collection[left]) * (right - left)) // (
						    sorted_collection[right] - sorted_collection[left]
						)

						# out of range check
						if point < 0 or point >= len(UpperCamelCase_   ):
												return None

						if sorted_collection[point] == item:
												return point
						elif point < left:
												return interpolation_search_by_recursion(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )
						elif point > right:
												return interpolation_search_by_recursion(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )
						else:
												if sorted_collection[point] > item:
																		return interpolation_search_by_recursion(
																		    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   point - 1   )
												else:
																		return interpolation_search_by_recursion(
																		    UpperCamelCase_    ,   UpperCamelCase_    ,   point + 1    ,   UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				Optional[Any]   ) ->   Tuple:
						"""simple docstring"""







						if collection != sorted(UpperCamelCase_   ):
												raise ValueError("Collection must be ascending sorted"   )
						return True


if __name__ == "__main__":
	import sys
	a_							=    0
	if debug == 1:
		a_							=    [10, 30, 40, 45, 50, 66, 77, 93]
		try:
			__assert_sorted(collection)
		except ValueError:
			sys.exit('''Sequence must be ascending sorted to apply interpolation search''')
		a_							=    67

	a_							=    interpolation_search(collection, target)
	if result is not None:
		print(F"{target} found at positions: {result}")
	else:
		print('''Not found''')






