






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

def      lowerCAmelCase__	(    _a       :			Any    ):
		snake_case_				:							Optional[int]						 =       ["head.weight", "head.bias"]
		for k in ignore_keys:
				state_dict.pop(lowerCamelCase__  ,      lowerCamelCase__    )

def      lowerCAmelCase__	(    _a       :			List[str]    ):
		# projection head is used in the self-supervised pre-training in MSN,
		# for downstream task it's not needed.
		snake_case_				:							Tuple						 =       [
		    "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(lowerCamelCase__  ,      lowerCamelCase__    )

def      lowerCAmelCase__	(    _a       :			Dict  ,      _a       :			Optional[int]  ,      _a       :			Any    ):
		snake_case_				:							List[str]						 =       dct.pop(lowerCamelCase__    )
		snake_case_				:							int						 =       val

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			Optional[int]    ):
		snake_case_				:							Optional[int]						 =       ViTMSNConfig()
		snake_case_				:							Optional[int]						 =       10_00

		snake_case_				:							List[str]						 =       "datasets/huggingface/label-files"
		snake_case_				:							Union[str, Any]						 =       "imagenet-1k-id2label.json"
		snake_case_				:							Union[str, Any]						 =       json.load(open(hf_hub_download(lowerCamelCase__  ,      lowerCamelCase__    )  ,      "r"    )    )
		snake_case_				:							int						 =       {int(lowerCamelCase__    ): v for k, v in idalabel.items()}
		snake_case_				:							List[str]						 =       idalabel
		snake_case_				:							Optional[int]						 =       {v: k for k, v in idalabel.items()}

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

		snake_case_				:							Any						 =       ViTMSNModel(lowerCamelCase__    )

		snake_case_				:							Union[str, Any]						 =       torch.hub.load_state_dict_from_url(lowerCamelCase__  ,      map_location="cpu"    )["target_encoder"]

		snake_case_				:							Tuple						 =       ViTImageProcessor(size=config.image_size    )

		remove_projection_head(lowerCamelCase__    )
		snake_case_				:							List[str]						 =       create_rename_keys(lowerCamelCase__  ,      base_model=lowerCamelCase__    )

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

		model.load_state_dict(lowerCamelCase__    )
		model.eval()

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

		snake_case_				:							str						 =       Image.open(requests.get(lowerCamelCase__  ,      stream=lowerCamelCase__    ).raw    )
		snake_case_				:							Optional[Any]						 =       ViTImageProcessor(
		    size=config.image_size  ,      image_mean=lowerCamelCase__  ,      image_std=lowerCamelCase__    )
		snake_case_				:							List[Any]						 =       image_processor(images=lowerCamelCase__  ,      return_tensors="pt"    )

		# forward pass
		torch.manual_seed(2    )
		snake_case_				:							Union[str, Any]						 =       model(**lowerCamelCase__    )
		snake_case_				:							int						 =       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([[-1.0_915, -1.4_876, -1.1_809]]    )
		elif "b16" in checkpoint_url:
				snake_case_				:							Optional[Any]						 =       torch.tensor([[14.2_889, -18.9_045, 11.7_281]]    )
		elif "l16" in checkpoint_url:
				snake_case_				:							int						 =       torch.tensor([[41.5_028, -22.8_681, 45.6_475]]    )
		elif "b4" in checkpoint_url:
				snake_case_				:							int						 =       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]  ,      lowerCamelCase__  ,      atol=1E-4    )

		print(F'''Saving model to {pytorch_dump_folder_path}'''    )
		model.save_pretrained(lowerCamelCase__    )

		print(F'''Saving image processor to {pytorch_dump_folder_path}'''    )
		image_processor.save_pretrained(lowerCamelCase__    )


if __name__ == "__main__":
							lowercase  :							str			  =    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.'''
							)

							lowercase  :							List[str]			  =    parser.parse_args()
							convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			int    ):
		# "extended trapezoidal rule"
		# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
		snake_case_				:							Optional[Any]						 =       (boundary[1] - boundary[0]) / steps
		snake_case_				:							str						 =       boundary[0]
		snake_case_				:							Dict						 =       boundary[1]
		snake_case_				:							str						 =       make_points(_a  ,      _a  ,      _a    )
		snake_case_				:							str						 =       0.0
		y += (h / 2.0) * f(_a    )
		for i in x_i:
				# print(i)
				y += h * f(_a    )
		y += (h / 2.0) * f(_a    )
		return y

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			Optional[Any]  ,      _a       :			Tuple    ):
		snake_case_				:							Tuple						 =       a + h
		while x < (b - h):
				yield x
				snake_case_				:							List[str]						 =       x + h

def      lowerCAmelCase__	(    _a       :			Tuple    ):  # enter your function here
		snake_case_				:							Tuple						 =       (x - 0) * (x - 0)
		return y

def      lowerCAmelCase__	(    ):
		snake_case_				:							Union[str, Any]						 =       0.0  # Lower bound of integration
		snake_case_				:							List[Any]						 =       1.0  # Upper bound of integration
		snake_case_				:							Tuple						 =       10.0  # define number of steps or resolution
		snake_case_				:							List[Any]						 =       [a, b]  # define boundary of integration
		snake_case_				:							Any						 =       method_a(_a  ,      _a    )
		print(F'''y = {y}'''    )


if __name__ == "__main__":
							main()
