"""simple docstring"""







import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]    ,							UpperCAmelCase_					:       Any=False						)							->    Optional[int]:

	'''simple docstring'''





	__snake_case    :							Any   	=				[]
	for i in range(config.num_hidden_layers						):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((F"module.blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight")						)
		rename_keys.append((F"module.blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")						)
		rename_keys.append(
		    (F"module.blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")						)
		rename_keys.append((F"module.blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")						)
		rename_keys.append((F"module.blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")						)
		rename_keys.append((F"module.blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")						)
		rename_keys.append((F"module.blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")						)
		rename_keys.append((F"module.blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")						)
		rename_keys.append((F"module.blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")						)
		rename_keys.append((F"module.blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")						)

	# projection layer + position embeddings
	rename_keys.extend(
	    [
	        ('module.cls_token', 'vit.embeddings.cls_token'),
	        ('module.patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
	        ('module.patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
	        ('module.pos_embed', 'vit.embeddings.position_embeddings'),
	    ]						)

	if base_model:
		# layernorm + pooler
		rename_keys.extend(
		    [
		        ('module.norm.weight', 'layernorm.weight'),
		        ('module.norm.bias', 'layernorm.bias'),
		    ]						)

		# if just the base model, we should remove "vit" from all keys that start with "vit"
		__snake_case    :							Optional[int]   	=				[(pair[0], pair[1][4:]) if pair[1].startswith('vit'						) else pair for pair in rename_keys]
	else:
		# layernorm + classification head
		rename_keys.extend(
		    [
		        ('norm.weight', 'vit.layernorm.weight'),
		        ('norm.bias', 'vit.layernorm.bias'),
		        ('head.weight', 'classifier.weight'),
		        ('head.bias', 'classifier.bias'),
		    ]						)

	return rename_keys
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]    ,							UpperCAmelCase_					:       Optional[Any]    ,							UpperCAmelCase_					:       List[Any]=False						)							->    Union[str, Any]:

	'''simple docstring'''





	for i in range(config.num_hidden_layers						):
		if base_model:
			__snake_case    :							int   	=				''
		else:
			__snake_case    :							Dict   	=				'vit.'
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		__snake_case    :							Optional[Any]   	=				state_dict.pop(F"module.blocks.{i}.attn.qkv.weight"						)
		__snake_case    :							Any   	=				state_dict.pop(F"module.blocks.{i}.attn.qkv.bias"						)
		# next, add query, keys and values (in that order) to the state dict
		__snake_case    :							Union[str, Any]   	=				in_proj_weight[
		    : config.hidden_size, :
		]
		__snake_case    :							Dict   	=				in_proj_bias[: config.hidden_size]
		__snake_case    :							Any   	=				in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		__snake_case    :							List[Any]   	=				in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		__snake_case    :							Tuple   	=				in_proj_weight[
		    -config.hidden_size :, :
		]
		__snake_case    :							Optional[Any]   	=				in_proj_bias[-config.hidden_size :]
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int						)							->    Optional[Any]:

	'''simple docstring'''





	__snake_case    :							int   	=				['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(UpperCAmelCase_    ,							UpperCAmelCase_						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]						)							->    str:

	'''simple docstring'''





	__snake_case    :							Any   	=				[
	    'module.fc.fc1.weight',
	    'module.fc.fc1.bias',
	    'module.fc.bn1.weight',
	    'module.fc.bn1.bias',
	    'module.fc.bn1.running_mean',
	    'module.fc.bn1.running_var',
	    'module.fc.bn1.num_batches_tracked',
	    'module.fc.fc2.weight',
	    'module.fc.fc2.bias',
	    'module.fc.bn2.weight',
	    'module.fc.bn2.bias',
	    'module.fc.bn2.running_mean',
	    'module.fc.bn2.running_var',
	    'module.fc.bn2.num_batches_tracked',
	    'module.fc.fc3.weight',
	    'module.fc.fc3.bias',
	]
	for k in ignore_keys:
		state_dict.pop(UpperCAmelCase_    ,							UpperCAmelCase_						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       List[Any]    ,							UpperCAmelCase_					:       List[str]    ,							UpperCAmelCase_					:       Tuple						)							->    str:

	'''simple docstring'''





	__snake_case    :							int   	=				dct.pop(UpperCAmelCase_						)
	__snake_case    :							str   	=				val
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Dict    ,							UpperCAmelCase_					:       List[str]						)							->    int:

	'''simple docstring'''





	__snake_case    :							Optional[int]   	=				ViTMSNConfig()
	__snake_case    :							str   	=				10_00

	__snake_case    :							Dict   	=				'datasets/huggingface/label-files'
	__snake_case    :							str   	=				'imagenet-1k-id2label.json'
	__snake_case    :							int   	=				json.load(open(hf_hub_download(UpperCAmelCase_    ,							UpperCAmelCase_						)    ,							'r'						)						)
	__snake_case    :							Optional[Any]   	=				{int(UpperCAmelCase_						): v for k, v in idalabel.items()}
	__snake_case    :							Any   	=				idalabel
	__snake_case    :							Any   	=				{v: k for k, v in idalabel.items()}

	if "s16" in checkpoint_url:
		__snake_case    :							Dict   	=				3_84
		__snake_case    :							str   	=				15_36
		__snake_case    :							Optional[int]   	=				6
	elif "l16" in checkpoint_url:
		__snake_case    :							int   	=				10_24
		__snake_case    :							Union[str, Any]   	=				40_96
		__snake_case    :							str   	=				24
		__snake_case    :							List[str]   	=				16
		__snake_case    :							Union[str, Any]   	=				0.1
	elif "b4" in checkpoint_url:
		__snake_case    :							Optional[int]   	=				4
	elif "l7" in checkpoint_url:
		__snake_case    :							List[str]   	=				7
		__snake_case    :							List[Any]   	=				10_24
		__snake_case    :							Tuple   	=				40_96
		__snake_case    :							Optional[int]   	=				24
		__snake_case    :							Dict   	=				16
		__snake_case    :							Any   	=				0.1

	__snake_case    :							Union[str, Any]   	=				ViTMSNModel(UpperCAmelCase_						)

	__snake_case    :							List[Any]   	=				torch.hub.load_state_dict_from_url(UpperCAmelCase_    ,							map_location='cpu'						)['target_encoder']

	__snake_case    :							Dict   	=				ViTImageProcessor(size=config.image_size						)

	remove_projection_head(UpperCAmelCase_						)
	__snake_case    :							Dict   	=				create_rename_keys(UpperCAmelCase_    ,							base_model=UpperCAmelCase_						)

	for src, dest in rename_keys:
		rename_key(UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_						)
	read_in_q_k_v(UpperCAmelCase_    ,							UpperCAmelCase_    ,							base_model=UpperCAmelCase_						)

	model.load_state_dict(UpperCAmelCase_						)
	model.eval()

	__snake_case    :							str   	=				'http://images.cocodataset.org/val2017/000000039769.jpg'

	__snake_case    :							str   	=				Image.open(requests.get(UpperCAmelCase_    ,							stream=UpperCAmelCase_						).raw						)
	__snake_case    :							List[str]   	=				ViTImageProcessor(
	    size=config.image_size    ,							image_mean=UpperCAmelCase_    ,							image_std=UpperCAmelCase_						)
	__snake_case    :							int   	=				image_processor(images=UpperCAmelCase_    ,							return_tensors='pt'						)

	# forward pass
	torch.manual_seed(2						)
	__snake_case    :							str   	=				model(**UpperCAmelCase_						)
	__snake_case    :							Union[str, Any]   	=				outputs.last_hidden_state

	# The following Colab Notebook was used to generate these outputs:
	# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
	if "s16" in checkpoint_url:
		__snake_case    :							Optional[int]   	=				torch.tensor([[-1.0_915, -1.4_876, -1.1_809]]						)
	elif "b16" in checkpoint_url:
		__snake_case    :							int   	=				torch.tensor([[14.2_889, -18.9_045, 11.7_281]]						)
	elif "l16" in checkpoint_url:
		__snake_case    :							Tuple   	=				torch.tensor([[41.5_028, -22.8_681, 45.6_475]]						)
	elif "b4" in checkpoint_url:
		__snake_case    :							Any   	=				torch.tensor([[-4.3_868, 5.2_932, -0.4_137]]						)
	else:
		__snake_case    :							str   	=				torch.tensor([[-0.1_792, -0.6_465, 2.4_263]]						)

	# verify logits
	assert torch.allclose(last_hidden_state[:, 0, :3]    ,							UpperCAmelCase_    ,							atol=1E-4						)

	print(F"Saving model to {pytorch_dump_folder_path}"						)
	model.save_pretrained(UpperCAmelCase_						)

	print(F"Saving image processor to {pytorch_dump_folder_path}"						)
	image_processor.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
	_a  :  Optional[Any]       =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--checkpoint_url",
	    default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
	    type=str,
	    help="URL of the checkpoint you'd like to convert.",
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
	)

	_a  :  Any       =				parser.parse_args()
	convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






"""simple docstring"""







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       list[int]    ,							UpperCAmelCase_					:       str						)							->    list[int]:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				int(UpperCAmelCase_						)

	# Initialize Result
	__snake_case    :							int   	=				[]

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

	return answer


# Driver Code
if __name__ == "__main__":
	_a  :  Optional[int]=				[]
	_a  :  Optional[int]=				"0"

	if (
	    input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
	    == "y"
	):
		_a  :  int=				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()))
		_a  :  Optional[int]=				input("Enter the change you want to make in Indian Currency: ").strip()
	else:
		# All denominations of Indian Currency if user does not enter
		_a  :  Tuple=				[1, 2, 5, 10, 20, 50, 100, 500, 2_000]
		_a  :  List[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}: ''')
		_a  :  List[Any]=				find_minimum_change(denominations, value)
		# Print result
		for i in range(len(answer)):
			print(answer[i], end=" ")





