




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      lowerCamelCase_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: Optional[Any]=False )	->			Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase									=       []
				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"
								__lowerCamelCase									=       [(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      lowerCamelCase_						(			UpperCamelCase__							: Union[str, Any]					,	UpperCamelCase__							: Any					,	UpperCamelCase__							: Dict=False )	->			Optional[int]:





				"""simple docstring"""


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







def      lowerCamelCase_						(			UpperCamelCase__							: Tuple )	->			Any:





				"""simple docstring"""


				__lowerCamelCase									=       ['head.weight', 'head.bias']
				for k in ignore_keys:
								state_dict.pop(UpperCamelCase__					,	UpperCamelCase__ )







def      lowerCamelCase_						(			UpperCamelCase__							: Any )	->			int:





				"""simple docstring"""


				__lowerCamelCase									=       [
				    '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      lowerCamelCase_						(			UpperCamelCase__							: Any					,	UpperCamelCase__							: Dict					,	UpperCamelCase__							: Union[str, Any] )	->			str:





				"""simple docstring"""


				__lowerCamelCase									=       dct.pop(UpperCamelCase__ )
				__lowerCamelCase									=       val







def      lowerCamelCase_						(			UpperCamelCase__							: List[str]					,	UpperCamelCase__							: int )	->			Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase									=       ViTMSNConfig()
				__lowerCamelCase									=       1000

				__lowerCamelCase									=       'datasets/huggingface/label-files'
				__lowerCamelCase									=       'imagenet-1k-id2label.json'
				__lowerCamelCase									=       json.load(open(hf_hub_download(UpperCamelCase__					,	UpperCamelCase__ )					,	'r' ) )
				__lowerCamelCase									=       {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
				__lowerCamelCase									=       idalabel
				__lowerCamelCase									=       {v: k for k, v in idalabel.items()}

				if "s16" in checkpoint_url:
								__lowerCamelCase									=       384
								__lowerCamelCase									=       1536
								__lowerCamelCase									=       6
				elif "l16" in checkpoint_url:
								__lowerCamelCase									=       1024
								__lowerCamelCase									=       4096
								__lowerCamelCase									=       24
								__lowerCamelCase									=       16
								__lowerCamelCase									=       0.1
				elif "b4" in checkpoint_url:
								__lowerCamelCase									=       4
				elif "l7" in checkpoint_url:
								__lowerCamelCase									=       7
								__lowerCamelCase									=       1024
								__lowerCamelCase									=       4096
								__lowerCamelCase									=       24
								__lowerCamelCase									=       16
								__lowerCamelCase									=       0.1

				__lowerCamelCase									=       ViTMSNModel(UpperCamelCase__ )

				__lowerCamelCase									=       torch.hub.load_state_dict_from_url(UpperCamelCase__					,	map_location='cpu' )['target_encoder']

				__lowerCamelCase									=       ViTImageProcessor(size=config.image_size )

				remove_projection_head(UpperCamelCase__ )
				__lowerCamelCase									=       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()

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

				__lowerCamelCase									=       Image.open(requests.get(UpperCamelCase__					,	stream=UpperCamelCase__ ).raw )
				__lowerCamelCase									=       ViTImageProcessor(
				    size=config.image_size					,	image_mean=UpperCamelCase__					,	image_std=UpperCamelCase__ )
				__lowerCamelCase									=       image_processor(images=UpperCamelCase__					,	return_tensors='pt' )

				# forward pass
				torch.manual_seed(2 )
				__lowerCamelCase									=       model(**UpperCamelCase__ )
				__lowerCamelCase									=       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:
								__lowerCamelCase									=       torch.tensor([[-1.09_15, -1.48_76, -1.18_09]] )
				elif "b16" in checkpoint_url:
								__lowerCamelCase									=       torch.tensor([[14.28_89, -18.90_45, 11.72_81]] )
				elif "l16" in checkpoint_url:
								__lowerCamelCase									=       torch.tensor([[41.50_28, -22.86_81, 45.64_75]] )
				elif "b4" in checkpoint_url:
								__lowerCamelCase									=       torch.tensor([[-4.38_68, 5.29_32, -0.41_37]] )
				else:
								__lowerCamelCase									=       torch.tensor([[-0.17_92, -0.64_65, 2.42_63]] )

				# 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)








def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			list:





				"""simple docstring"""


				if n_term == "":
								return []
				__lowerCamelCase									=       []
				for temp in range(int(UpperCamelCase__ ) ):
								series.append(F"""1/{temp + 1}""" if series else '1' )
				return series


if __name__ == "__main__":
					__A								=       input("Enter the last number (nth term) of the Harmonic Series")
					print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
					print(harmonic_series(nth_term))


