





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




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[Any]   )   ->			int:
    UpperCamelCase__							:       Optional[int]      =   ['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(__UpperCAmelCase	,     __UpperCAmelCase   )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict   )   ->			str:
    # projection head is used in the self-supervised pre-training in MSN,
    # for downstream task it's not needed.
    UpperCamelCase__							:       Optional[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 	lowerCAmelCase_					(						__UpperCAmelCase:    List[str]	,     __UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Union[str, Any]   )   ->			Dict:
    UpperCamelCase__							:       Optional[Any]      =   dct.pop(__UpperCAmelCase   )
    UpperCamelCase__							:       int      =   val




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any	,     __UpperCAmelCase:    str   )   ->			str:
    UpperCamelCase__							:       List[Any]      =   ViTMSNConfig()
    UpperCamelCase__							:       Union[str, Any]      =   1000

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

    if "s16" in checkpoint_url:
        UpperCamelCase__							:       Optional[int]      =   384
        UpperCamelCase__							:       Optional[Any]      =   1536
        UpperCamelCase__							:       Optional[Any]      =   6
    elif "l16" in checkpoint_url:
        UpperCamelCase__							:       Any      =   1024
        UpperCamelCase__							:       Dict      =   4096
        UpperCamelCase__							:       Union[str, Any]      =   24
        UpperCamelCase__							:       Optional[int]      =   16
        UpperCamelCase__							:       Optional[int]      =   0.1
    elif "b4" in checkpoint_url:
        UpperCamelCase__							:       Union[str, Any]      =   4
    elif "l7" in checkpoint_url:
        UpperCamelCase__							:       Optional[Any]      =   7
        UpperCamelCase__							:       Optional[int]      =   1024
        UpperCamelCase__							:       Union[str, Any]      =   4096
        UpperCamelCase__							:       Optional[int]      =   24
        UpperCamelCase__							:       Optional[Any]      =   16
        UpperCamelCase__							:       Optional[Any]      =   0.1

    UpperCamelCase__							:       Union[str, Any]      =   ViTMSNModel(__UpperCAmelCase   )

    UpperCamelCase__							:       Dict      =   torch.hub.load_state_dict_from_url(__UpperCAmelCase	,     map_location='''cpu'''   )['''target_encoder''']

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

    remove_projection_head(__UpperCAmelCase   )
    UpperCamelCase__							:       Tuple      =   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()

    UpperCamelCase__							:       Optional[Any]      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''

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

    # forward pass
    torch.manual_seed(2   )
    UpperCamelCase__							:       Tuple      =   model(**__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[Any]      =   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:
        UpperCamelCase__							:       Union[str, Any]      =   torch.tensor([[-1.0915, -1.4876, -1.1809]]   )
    elif "b16" in checkpoint_url:
        UpperCamelCase__							:       Optional[Any]      =   torch.tensor([[14.2889, -18.9045, 11.7281]]   )
    elif "l16" in checkpoint_url:
        UpperCamelCase__							:       Optional[int]      =   torch.tensor([[41.5028, -22.8681, 45.6475]]   )
    elif "b4" in checkpoint_url:
        UpperCamelCase__							:       Dict      =   torch.tensor([[-4.3868, 5.2932, -0.4137]]   )
    else:
        UpperCamelCase__							:       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__":
  UpperCAmelCase_  							=				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_  							=				parser.parse_args()
  convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput






@dataclass
class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							torch.FloatTensor
   a		:							torch.FloatTensor






class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase       ):

   '''simple docstring'''


   a		:							List[Any]     	=	1




   @register_to_config
   def __init__(					self,	__magic_name__ = 2000,	__magic_name__ = 0.15,	__magic_name__ = 0.01,	__magic_name__ = 1348.0,	__magic_name__ = 1E-5,	__magic_name__ = 1,	)				->   int:






       """simple docstring"""
       # standard deviation of the initial noise distribution
       UpperCamelCase__							:       int      =   sigma_max

       # setable values
       UpperCamelCase__							:       Optional[int]      =   None

       self.set_sigmas(__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   torch.FloatTensor:






       """simple docstring"""
       return sample




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       str      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

       UpperCamelCase__							:       List[Any]      =   torch.linspace(1,	__magic_name__,	__magic_name__,	device=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   sigma_min if sigma_min is not None else self.config.sigma_min
       UpperCamelCase__							:       str      =   sigma_max if sigma_max is not None else self.config.sigma_max
       UpperCamelCase__							:       Optional[int]      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
           self.set_timesteps(__magic_name__,	__magic_name__							)

       UpperCamelCase__							:       str      =   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       UpperCamelCase__							:       Optional[Any]      =   torch.exp(torch.linspace(math.log(__magic_name__							),	math.log(__magic_name__							),	__magic_name__							)							)
       UpperCamelCase__							:       Any      =   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   str:






       """simple docstring"""
       return torch.where(
           timesteps == 0,	torch.zeros_like(t.to(timesteps.device							)							),	self.discrete_sigmas[timesteps - 1].to(timesteps.device							),	)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[SdeVeOutput, Tuple]:






       """simple docstring"""
       if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

       UpperCamelCase__							:       Any      =   timestep * torch.ones(
           sample.shape[0],	device=sample.device							)  # torch.repeat_interleave(timestep, sample.shape[0])
       UpperCamelCase__							:       Any      =   (timestep * (len(self.timesteps							) - 1)).long()

       # mps requires indices to be in the same device, so we use cpu as is the default with cuda
       UpperCamelCase__							:       List[Any]      =   timesteps.to(self.discrete_sigmas.device							)

       UpperCamelCase__							:       str      =   self.discrete_sigmas[timesteps].to(sample.device							)
       UpperCamelCase__							:       List[Any]      =   self.get_adjacent_sigma(__magic_name__,	__magic_name__							).to(sample.device							)
       UpperCamelCase__							:       Optional[Any]      =   torch.zeros_like(__magic_name__							)
       UpperCamelCase__							:       str      =   (sigma**2 - adjacent_sigma**2) ** 0.5

       # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
       # also equation 47 shows the analog from SDE models to ancestral sampling methods
       UpperCamelCase__							:       Union[str, Any]      =   diffusion.flatten()
       while len(diffusion.shape							) < len(sample.shape							):
           UpperCamelCase__							:       Any      =   diffusion.unsqueeze(-1							)
       UpperCamelCase__							:       str      =   drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       UpperCamelCase__							:       Union[str, Any]      =   randn_tensor(
           sample.shape,	layout=sample.layout,	generator=__magic_name__,	device=sample.device,	dtype=sample.dtype							)
       UpperCamelCase__							:       Optional[Any]      =   sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       UpperCamelCase__							:       str      =   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

       if not return_dict:
           return (prev_sample, prev_sample_mean)

       return SdeVeOutput(prev_sample=__magic_name__,	prev_sample_mean=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[SchedulerOutput, Tuple]:






       """simple docstring"""
       if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

       # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
       # sample noise for correction
       UpperCamelCase__							:       List[str]      =   randn_tensor(sample.shape,	layout=sample.layout,	generator=__magic_name__							).to(sample.device							)

       # compute step size from the model_output, the noise, and the snr
       UpperCamelCase__							:       str      =   torch.norm(model_output.reshape(model_output.shape[0],	-1							),	dim=-1							).mean()
       UpperCamelCase__							:       Tuple      =   torch.norm(noise.reshape(noise.shape[0],	-1							),	dim=-1							).mean()
       UpperCamelCase__							:       Union[str, Any]      =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
       UpperCamelCase__							:       Optional[Any]      =   step_size * torch.ones(sample.shape[0]							).to(sample.device							)
       # self.repeat_scalar(step_size, sample.shape[0])

       # compute corrected sample: model_output term and noise term
       UpperCamelCase__							:       Tuple      =   step_size.flatten()
       while len(step_size.shape							) < len(sample.shape							):
           UpperCamelCase__							:       int      =   step_size.unsqueeze(-1							)
       UpperCamelCase__							:       int      =   sample + step_size * model_output
       UpperCamelCase__							:       List[Any]      =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	)				->   torch.FloatTensor:






       """simple docstring"""
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCamelCase__							:       Any      =   timesteps.to(original_samples.device							)
       UpperCamelCase__							:       List[str]      =   self.discrete_sigmas.to(original_samples.device							)[timesteps]
       UpperCamelCase__							:       Any      =   (
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(__magic_name__							) * sigmas[:, None, None, None]
       )
       UpperCamelCase__							:       int      =   noise + original_samples
       return noisy_samples




   def __len__(					self							)				->   Union[str, Any]:






       """simple docstring"""
       return self.config.num_train_timesteps

