



'''simple docstring'''




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()
UpperCamelCase__       :	Tuple    = logging.get_logger()


def 				lowerCAmelCase_   (	_lowerCamelCase:   int       ,      _lowerCamelCase:   str       ,      _lowerCamelCase:   LevitConfig       ,      _lowerCamelCase:   Path       ,      _lowerCamelCase:   bool = True					):
		print(F"Converting {name}..."					)

		with torch.no_grad():
				if hidden_sizes == 1_28:
						if name[-1] == "S":
								__SCREAMING_SNAKE_CASE       :		List[Any]           =		timm.create_model("""levit_128s"""       ,      pretrained=_lowerCamelCase					)
						else:
								__SCREAMING_SNAKE_CASE       :		List[str]           =		timm.create_model("""levit_128"""       ,      pretrained=_lowerCamelCase					)
				if hidden_sizes == 1_92:
						__SCREAMING_SNAKE_CASE       :		Any           =		timm.create_model("""levit_192"""       ,      pretrained=_lowerCamelCase					)
				if hidden_sizes == 2_56:
						__SCREAMING_SNAKE_CASE       :		int           =		timm.create_model("""levit_256"""       ,      pretrained=_lowerCamelCase					)
				if hidden_sizes == 3_84:
						__SCREAMING_SNAKE_CASE       :		Any           =		timm.create_model("""levit_384"""       ,      pretrained=_lowerCamelCase					)

				from_model.eval()
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		LevitForImageClassificationWithTeacher(_lowerCamelCase					).eval()
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		OrderedDict()

				__SCREAMING_SNAKE_CASE       :		Any           =		from_model.state_dict()
				__SCREAMING_SNAKE_CASE       :		Tuple           =		list(from_model.state_dict().keys()					)
				__SCREAMING_SNAKE_CASE       :		int           =		list(our_model.state_dict().keys()					)
				print(len(_lowerCamelCase					)       ,      len(_lowerCamelCase					)					)
				for i in range(len(_lowerCamelCase					)					):
						__SCREAMING_SNAKE_CASE       :		int           =		weights[og_keys[i]]
				our_model.load_state_dict(_lowerCamelCase					)

				__SCREAMING_SNAKE_CASE       :		str           =		torch.randn((2, 3, 2_24, 2_24)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		from_model(_lowerCamelCase					)
				__SCREAMING_SNAKE_CASE       :		Any           =		our_model(_lowerCamelCase					).logits

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

		__SCREAMING_SNAKE_CASE       :		Dict           =		name
		print(_lowerCamelCase					)

		if push_to_hub:
				our_model.save_pretrained(save_directory / checkpoint_name					)
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		LevitImageProcessor()
				image_processor.save_pretrained(save_directory / checkpoint_name					)

				print(F"Pushed {checkpoint_name}"					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Path       ,      _lowerCamelCase:   str = None       ,      _lowerCamelCase:   bool = True					):
		__SCREAMING_SNAKE_CASE       :		Tuple           =		"""imagenet-1k-id2label.json"""
		__SCREAMING_SNAKE_CASE       :		Tuple           =		10_00
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		(1, num_labels)

		__SCREAMING_SNAKE_CASE       :		Dict           =		"""huggingface/label-files"""
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		num_labels
		__SCREAMING_SNAKE_CASE       :		str           =		json.load(open(hf_hub_download(_lowerCamelCase       ,      _lowerCamelCase       ,      repo_type="""dataset"""					)       ,      """r"""					)					)
		__SCREAMING_SNAKE_CASE       :		int           =		{int(_lowerCamelCase					): v for k, v in idalabel.items()}

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		idalabel
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		{v: k for k, v in idalabel.items()}

		__SCREAMING_SNAKE_CASE       :		int           =		partial(_lowerCamelCase       ,      num_labels=_lowerCamelCase       ,      idalabel=_lowerCamelCase       ,      labelaid=_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		int           =		{
		    """levit-128S""": 1_28,
		    """levit-128""": 1_28,
		    """levit-192""": 1_92,
		    """levit-256""": 2_56,
		    """levit-384""": 3_84,
		}

		__SCREAMING_SNAKE_CASE       :		Dict           =		{
		    """levit-128S""": ImageNetPreTrainedConfig(
		        hidden_sizes=[1_28, 2_56, 3_84]       ,      num_attention_heads=[4, 6, 8]       ,      depths=[2, 3, 4]       ,      key_dim=[16, 16, 16]       ,      drop_path_rate=0       ,      ),
		    """levit-128""": ImageNetPreTrainedConfig(
		        hidden_sizes=[1_28, 2_56, 3_84]       ,      num_attention_heads=[4, 8, 12]       ,      depths=[4, 4, 4]       ,      key_dim=[16, 16, 16]       ,      drop_path_rate=0       ,      ),
		    """levit-192""": ImageNetPreTrainedConfig(
		        hidden_sizes=[1_92, 2_88, 3_84]       ,      num_attention_heads=[3, 5, 6]       ,      depths=[4, 4, 4]       ,      key_dim=[32, 32, 32]       ,      drop_path_rate=0       ,      ),
		    """levit-256""": ImageNetPreTrainedConfig(
		        hidden_sizes=[2_56, 3_84, 5_12]       ,      num_attention_heads=[4, 6, 8]       ,      depths=[4, 4, 4]       ,      key_dim=[32, 32, 32]       ,      drop_path_rate=0       ,      ),
		    """levit-384""": ImageNetPreTrainedConfig(
		        hidden_sizes=[3_84, 5_12, 7_68]       ,      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]       ,      _lowerCamelCase       ,      names_to_config[model_name]       ,      _lowerCamelCase       ,      _lowerCamelCase					)
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(names_to_hidden_sizes[model_name]       ,      _lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase       ,      _lowerCamelCase					)
		return config, expected_shape


if __name__ == "__main__":
						UpperCamelCase__       :	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''',
						)

						UpperCamelCase__       :	str    = parser.parse_args()
						UpperCamelCase__       :	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)




'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   list[int]       ,      _lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		str           =		int(_lowerCamelCase					)

		# Initialize Result
		__SCREAMING_SNAKE_CASE       :		Tuple           =		[]

		# Traverse through all denomination
		for denomination in reversed(_lowerCamelCase					):
				# Find denominations
				while int(_lowerCamelCase					) >= int(_lowerCamelCase					):
						total_value -= int(_lowerCamelCase					)
						answer.append(_lowerCamelCase					)  # Append the "answers" array

		return answer


# Driver Code
if __name__ == "__main__":
						UpperCamelCase__       :	int    = []
						UpperCamelCase__       :	List[Any]    = '''0'''

						if (
						    input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
						    == "y"
						):
												UpperCamelCase__       :	Tuple    = int(input('''Enter the number of denominations you want to add: ''').strip())

												for i in range(0, n):
																		denominations.append(int(input(f"Denomination {i}: ").strip()))
												UpperCamelCase__       :	str    = input('''Enter the change you want to make in Indian Currency: ''').strip()
						else:
												# All denominations of Indian Currency if user does not enter
												UpperCamelCase__       :	List[Any]    = [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
												UpperCamelCase__       :	str    = input('''Enter the change you want to make: ''').strip()

						if int(value) == 0 or int(value) < 0:
												print('''The total value cannot be zero or negative.''')

						else:
												print(f"Following is minimal change for {value}: ")
												UpperCamelCase__       :	int    = find_minimum_change(denominations, value)
												# Print result
												for i in range(len(answer)):
																		print(answer[i], end=''' ''')