


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

def       lowerCamelCase					(a_						:Dict)     ->							str:
       lowercase		:Optional[int]       							=				['''head.weight''', '''head.bias''']
       for k in ignore_keys:
              state_dict.pop(lowerCAmelCase__				,				lowerCAmelCase__)

def       lowerCamelCase					(a_						:List[str])     ->							str:
       # projection head is used in the self-supervised pre-training in MSN,
       # for downstream task it's not needed.
       lowercase		: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(lowerCAmelCase__				,				lowerCAmelCase__)

def       lowerCamelCase					(a_						:str				,				a_						:int				,				a_						:Any)     ->							int:
       lowercase		:int       							=				dct.pop(lowerCAmelCase__)
       lowercase		:Optional[Any]       							=				val

def       lowerCamelCase					(a_						:Optional[Any]				,				a_						:List[Any])     ->							List[str]:
       lowercase		:Any       							=				ViTMSNConfig()
       lowercase		:Optional[int]       							=				1000

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

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

       lowercase		:List[str]       							=				ViTMSNModel(lowerCAmelCase__)

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

       lowercase		:List[str]       							=				ViTImageProcessor(size=config.image_size)

       remove_projection_head(lowerCAmelCase__)
       lowercase		:int       							=				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()

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

       lowercase		:Union[str, Any]       							=				Image.open(requests.get(lowerCAmelCase__				,				stream=lowerCAmelCase__).raw)
       lowercase		:Optional[Any]       							=				ViTImageProcessor(
           size=config.image_size				,				image_mean=lowerCAmelCase__				,				image_std=lowerCAmelCase__)
       lowercase		:int       							=				image_processor(images=lowerCAmelCase__				,				return_tensors='''pt''')

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

       # 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__":
 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)





"""simple docstring"""



# 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 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      torch.FloatTensor
       __A			:      torch.FloatTensor







class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase     ):
       __A			:      Any        =   1






       @register_to_config
       def __init__(   self  :    int  ,     snake_case__  :    int = 2_0_0_0  ,     snake_case__  :    float = 0.15  ,     snake_case__  :    float = 0.01  ,     snake_case__  :    float = 13_48.0  ,     snake_case__  :    float = 1e-5  ,     snake_case__  :    int = 1  ,     ):

              '''simple docstring'''






              lowercase		:List[Any]       							=				sigma_max

              # setable values
              lowercase		:List[str]       							=				None

              self.set_sigmas(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[int] = None				):

              '''simple docstring'''






              return sample






       def  __snake_case						(   self  :    Optional[int]  ,     snake_case__  :    int  ,     snake_case__  :    float = None  ,     snake_case__  :    Union[str, torch.device] = None				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				sampling_eps if sampling_eps is not None else self.config.sampling_eps

              lowercase		:Tuple       							=				torch.linspace(1  ,     snake_case__  ,     snake_case__  ,     device=snake_case__				)






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    int  ,     snake_case__  :    float = None  ,     snake_case__  :    float = None  ,     snake_case__  :    float = None				):

              '''simple docstring'''






              lowercase		:int       							=				sigma_min if sigma_min is not None else self.config.sigma_min
              lowercase		:Any       							=				sigma_max if sigma_max is not None else self.config.sigma_max
              lowercase		:Tuple       							=				sampling_eps if sampling_eps is not None else self.config.sampling_eps
              if self.timesteps is None:
                     self.set_timesteps(snake_case__  ,     snake_case__				)

              lowercase		:Union[str, Any]       							=				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
              lowercase		:str       							=				torch.exp(torch.linspace(math.log(snake_case__				)  ,     math.log(snake_case__				)  ,     snake_case__				)				)
              lowercase		:List[str]       							=				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]				)






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    Tuple  ,     snake_case__  :    int				):

              '''simple docstring'''






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






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    int  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[torch.Generator] = None  ,     snake_case__  :    bool = True  ,     ):

              '''simple docstring'''






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

              lowercase		:Optional[int]       							=				timestep * torch.ones(
                  sample.shape[0]  ,     device=sample.device				)  # torch.repeat_interleave(timestep, sample.shape[0])
              lowercase		:Optional[int]       							=				(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
              lowercase		:Union[str, Any]       							=				timesteps.to(self.discrete_sigmas.device				)

              lowercase		:List[Any]       							=				self.discrete_sigmas[timesteps].to(sample.device				)
              lowercase		:str       							=				self.get_adjacent_sigma(snake_case__  ,     snake_case__				).to(sample.device				)
              lowercase		:Optional[Any]       							=				torch.zeros_like(snake_case__				)
              lowercase		:List[Any]       							=				(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
              lowercase		:Union[str, Any]       							=				diffusion.flatten()
              while len(diffusion.shape				) < len(sample.shape				):
                     lowercase		:Dict       							=				diffusion.unsqueeze(-1				)
              lowercase		:List[Any]       							=				drift - diffusion**2 * model_output

              #  equation 6: sample noise for the diffusion term of
              lowercase		:List[Any]       							=				randn_tensor(
                  sample.shape  ,     layout=sample.layout  ,     generator=snake_case__  ,     device=sample.device  ,     dtype=sample.dtype				)
              lowercase		:int       							=				sample - drift  # subtract because `dt` is a small negative timestep
              # TODO is the variable diffusion the correct scaling term for the noise?
              lowercase		:Any       							=				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=snake_case__  ,     prev_sample_mean=snake_case__				)






       def  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[torch.Generator] = None  ,     snake_case__  :    bool = True  ,     ):

              '''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
              lowercase		:Tuple       							=				randn_tensor(sample.shape  ,     layout=sample.layout  ,     generator=snake_case__				).to(sample.device				)

              # compute step size from the model_output, the noise, and the snr
              lowercase		:List[Any]       							=				torch.norm(model_output.reshape(model_output.shape[0]  ,     -1				)  ,     dim=-1				).mean()
              lowercase		:List[str]       							=				torch.norm(noise.reshape(noise.shape[0]  ,     -1				)  ,     dim=-1				).mean()
              lowercase		:int       							=				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
              lowercase		: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
              lowercase		:Union[str, Any]       							=				step_size.flatten()
              while len(step_size.shape				) < len(sample.shape				):
                     lowercase		:Union[str, Any]       							=				step_size.unsqueeze(-1				)
              lowercase		:Union[str, Any]       							=				sample + step_size * model_output
              lowercase		:Any       							=				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

              if not return_dict:
                     return (prev_sample,)

              return SchedulerOutput(prev_sample=snake_case__				)






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     ):

              '''simple docstring'''






              lowercase		:List[Any]       							=				timesteps.to(original_samples.device				)
              lowercase		:str       							=				self.discrete_sigmas.to(original_samples.device				)[timesteps]
              lowercase		:Optional[int]       							=				(
                  noise * sigmas[:, None, None, None]
                  if noise is not None
                  else torch.randn_like(snake_case__				) * sigmas[:, None, None, None]
              )
              lowercase		:str       							=				noise + original_samples
              return noisy_samples







       def __len__(   self  :    Optional[Any]				):

              '''simple docstring'''






              return self.config.num_train_timesteps

