



'''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 a_     (    __UpperCAmelCase				,							__UpperCAmelCase=False					)				->					Union[str, Any]:





			"""simple docstring"""







			snake_case:    str      							=[]
			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[Any]      							=[(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 a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=False					)				->					Optional[Any]:





			"""simple docstring"""







			for i in range(config.num_hidden_layers					):
						if base_model:
									snake_case:    Dict      							=''
						else:
									snake_case:    List[Any]      							='vit.'
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						snake_case:    Tuple      							=state_dict.pop(f'''module.blocks.{i}.attn.qkv.weight'''					)
						snake_case:    Tuple      							=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:    List[Any]      							=in_proj_weight[
						    : config.hidden_size, :
						]
						snake_case:    int      							=in_proj_bias[: config.hidden_size]
						snake_case:    str      							=in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						snake_case:    Union[str, Any]      							=in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						snake_case:    Any      							=in_proj_weight[
						    -config.hidden_size :, :
						]
						snake_case:    Tuple      							=in_proj_bias[-config.hidden_size :]







def a_     (    __UpperCAmelCase					)				->					List[Any]:





			"""simple docstring"""







			snake_case:    Tuple      							=['head.weight', 'head.bias']
			for k in ignore_keys:
						state_dict.pop(__UpperCAmelCase				,							__UpperCAmelCase					)







def a_     (    __UpperCAmelCase					)				->					Any:





			"""simple docstring"""







			snake_case:    int      							=[
			    '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 a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Tuple:





			"""simple docstring"""







			snake_case:    List[str]      							=dct.pop(__UpperCAmelCase					)
			snake_case:    Any      							=val







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







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

			snake_case:    Union[str, Any]      							='datasets/huggingface/label-files'
			snake_case:    Any      							='imagenet-1k-id2label.json'
			snake_case:    Optional[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:    Tuple      							=idalabel
			snake_case:    Union[str, Any]      							={v: k for k, v in idalabel.items()}

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

			snake_case:    Optional[Any]      							=ViTMSNModel(__UpperCAmelCase					)

			snake_case:    Union[str, Any]      							=torch.hub.load_state_dict_from_url(__UpperCAmelCase				,							map_location='cpu'					)['target_encoder']

			snake_case:    Any      							=ViTImageProcessor(size=config.image_size					)

			remove_projection_head(__UpperCAmelCase					)
			snake_case:    Any      							=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:    Union[str, Any]      							='http://images.cocodataset.org/val2017/000000039769.jpg'

			snake_case:    Optional[int]      							=Image.open(requests.get(__UpperCAmelCase				,							stream=__UpperCAmelCase					).raw					)
			snake_case:    Tuple      							=ViTImageProcessor(
			    size=config.image_size				,							image_mean=__UpperCAmelCase				,							image_std=__UpperCAmelCase					)
			snake_case:    Union[str, Any]      							=image_processor(images=__UpperCAmelCase				,							return_tensors='pt'					)

			# forward pass
			torch.manual_seed(2					)
			snake_case:    Union[str, Any]      							=model(**__UpperCAmelCase					)
			snake_case:    str      							=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:    Tuple      							=torch.tensor([[-1.0915, -1.4876, -1.1809]]					)
			elif "b16" in checkpoint_url:
						snake_case:    Optional[Any]      							=torch.tensor([[14.2889, -18.9045, 11.7281]]					)
			elif "l16" in checkpoint_url:
						snake_case:    Union[str, Any]      							=torch.tensor([[41.5028, -22.8681, 45.6475]]					)
			elif "b4" in checkpoint_url:
						snake_case:    str      							=torch.tensor([[-4.3868, 5.2932, -0.4137]]					)
			else:
						snake_case:    Dict      							=torch.tensor([[-0.1792, -0.6465, 2.4263]]					)

			# 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							   =				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							   =				parser.parse_args()
	convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    int      							=0
			if start < end:
						snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
						snake_case:    Optional[Any]      							=a[end]
						snake_case:    List[Any]      							=a[pivot]
						snake_case:    int      							=temp

						snake_case							,  snake_case:    Optional[int]      							=_in_place_partition(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							__UpperCAmelCase				,							p - 1					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							p + 1				,							__UpperCAmelCase					)
			return count







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=0
			snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
			snake_case:    List[Any]      							=a[end]
			snake_case:    List[str]      							=a[pivot]
			snake_case:    Dict      							=temp
			snake_case:    Union[str, Any]      							=start - 1
			for index in range(__UpperCAmelCase				,							__UpperCAmelCase					):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									snake_case:    Optional[Any]      							=new_pivot_index + 1
									snake_case:    Dict      							=a[new_pivot_index]
									snake_case:    Union[str, Any]      							=a[index]
									snake_case:    List[Any]      							=temp

			snake_case:    Union[str, Any]      							=a[new_pivot_index + 1]
			snake_case:    Tuple      							=a[end]
			snake_case:    Union[str, Any]      							=temp
			return new_pivot_index + 1, count


a							   =				TemporaryFile()
a							   =				100  # 1000 elements are to be sorted


a   ,      a							   =				0, 1  # mean and standard deviation
a							   =				np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
a							   =				np.load(outfile)
a							   =				len(M) - 1
a							   =				_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)







print(z)




