
"""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_     (__a				,   __a=False				):

					'''simple docstring'''





					A_				 =						[]
					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"
										A_				 =						[(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_     (__a				,   __a				,   __a=False				):

					'''simple docstring'''





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

					'''simple docstring'''





					A_				 =						["head.weight", "head.bias"]
					for k in ignore_keys:
										state_dict.pop(__a				,   __a				)
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						[
					    "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(__a				,   __a				)
def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						dct.pop(__a				)
					A_				 =						val
def 							A_     (__a				,   __a				):

					'''simple docstring'''





					A_				 =						ViTMSNConfig()
					A_				 =						1000

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

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

					A_				 =						ViTMSNModel(__a				)

					A_				 =						torch.hub.load_state_dict_from_url(__a				,   map_location="cpu"				)["target_encoder"]

					A_				 =						ViTImageProcessor(size=config.image_size				)

					remove_projection_head(__a				)
					A_				 =						create_rename_keys(__a				,   base_model=__a				)

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

					model.load_state_dict(__a				)
					model.eval()

					A_				 =						"http://images.cocodataset.org/val2017/000000039769.jpg"

					A_				 =						Image.open(requests.get(__a				,   stream=__a				).raw				)
					A_				 =						ViTImageProcessor(
					    size=config.image_size				,   image_mean=__a				,   image_std=__a				)
					A_				 =						image_processor(images=__a				,   return_tensors="pt"				)

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

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

					print(f'Saving model to {pytorch_dump_folder_path}'				)
					model.save_pretrained(__a				)

					print(f'Saving image processor to {pytorch_dump_folder_path}'				)
					image_processor.save_pretrained(__a				)


if __name__ == "__main__":
		UpperCamelCase_					:						int				= 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.'''
		)

		UpperCamelCase_					:						List[Any]				= parser.parse_args()
		convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






"""simple docstring"""





import torch
def 							A_     ():

					'''simple docstring'''





					if torch.cuda.is_available():
										A_				 =						torch.cuda.device_count()
					else:
										A_				 =						0
					print(f'Successfully ran on {num_gpus} GPUs'				)


if __name__ == "__main__":
		main()




