
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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase=False				):
    snake_case__    =					[]
    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__    =					[(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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase=False				):
    for i in range(config.num_hidden_layers				):
        if base_model:
            snake_case__    =					""
        else:
            snake_case__    =					"vit."
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        snake_case__    =					state_dict.pop(F"""module.blocks.{i}.attn.qkv.weight"""				)
        snake_case__    =					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__    =					in_proj_weight[
            : config.hidden_size, :
        ]
        snake_case__    =					in_proj_bias[: config.hidden_size]
        snake_case__    =					in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        snake_case__    =					in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        snake_case__    =					in_proj_weight[
            -config.hidden_size :, :
        ]
        snake_case__    =					in_proj_bias[-config.hidden_size :]





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					["head.weight", "head.bias"]
    for k in ignore_keys:
        state_dict.pop(__lowerCAmelCase     ,		__lowerCAmelCase				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    # projection head is used in the self-supervised pre-training in MSN,
    # for downstream task it's not needed.
    snake_case__    =					[
        "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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					dct.pop(__lowerCAmelCase				)
    snake_case__    =					val





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					ViTMSNConfig()
    snake_case__    =					1_000

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

    if "s16" in checkpoint_url:
        snake_case__    =					384
        snake_case__    =					1_536
        snake_case__    =					6
    elif "l16" in checkpoint_url:
        snake_case__    =					1_024
        snake_case__    =					4_096
        snake_case__    =					24
        snake_case__    =					16
        snake_case__    =					0.1
    elif "b4" in checkpoint_url:
        snake_case__    =					4
    elif "l7" in checkpoint_url:
        snake_case__    =					7
        snake_case__    =					1_024
        snake_case__    =					4_096
        snake_case__    =					24
        snake_case__    =					16
        snake_case__    =					0.1

    snake_case__    =					ViTMSNModel(__lowerCAmelCase				)

    snake_case__    =					torch.hub.load_state_dict_from_url(__lowerCAmelCase     ,		map_location="cpu"				)["target_encoder"]

    snake_case__    =					ViTImageProcessor(size=config.image_size				)

    remove_projection_head(__lowerCAmelCase				)
    snake_case__    =					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__    =					"http://images.cocodataset.org/val2017/000000039769.jpg"

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

    # forward pass
    torch.manual_seed(2				)
    snake_case__    =					model(**__lowerCAmelCase				)
    snake_case__    =					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__    =					torch.tensor([[-1.0_915, -1.4_876, -1.1_809]]				)
    elif "b16" in checkpoint_url:
        snake_case__    =					torch.tensor([[14.2_889, -18.9_045, 11.7_281]]				)
    elif "l16" in checkpoint_url:
        snake_case__    =					torch.tensor([[41.5_028, -22.8_681, 45.6_475]]				)
    elif "b4" in checkpoint_url:
        snake_case__    =					torch.tensor([[-4.3_868, 5.2_932, -0.4_137]]				)
    else:
        snake_case__    =					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__":
     __magic_name__  							=	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.'''
     )

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





from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

__magic_name__  							=	TypeVar('''T''')




class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self		,						lowerCamelCase			):
          snake_case__    =					data
          snake_case__    =					None



      def __str__(      self			):
          return F"""{self.data}"""





class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self			):
          snake_case__    =					None
      def __iter__(      self			):
          snake_case__    =					self.top
          while node:
              yield node.data
              snake_case__    =					node.next
      def __str__(      self			):
          return "->".join([str(lowerCamelCase			) for item in self]			)
      def __len__(      self			):
          return len(tuple(iter(self			)			)			)
      def A_      (      self			):
          return self.top is None
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					Node(lowerCamelCase			)
          if not self.is_empty():
              snake_case__    =					self.top
          snake_case__    =					node
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("pop from empty stack"			)
          assert isinstance(self.top		,						lowerCamelCase			)
          snake_case__    =					self.top
          snake_case__    =					self.top.next
          return pop_node.data
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("peek from empty stack"			)

          assert self.top is not None
          return self.top.data



      def A_      (      self			):
          snake_case__    =					None


if __name__ == "__main__":
     from doctest import testmod

     testmod()



