



from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin





@dataclass
class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				42





class 					__lowerCAmelCase (     lowerCamelCase__       ,						lowerCamelCase__       ):




				@register_to_config
				def __init__(							self						,				_snake_case = 16						,				_snake_case = 88						,				_snake_case = None						,				_snake_case = None						,				_snake_case = 1						,				_snake_case = 0.0						,				_snake_case = 32						,				_snake_case = None						,				_snake_case = False						,				_snake_case = None						,				_snake_case = "geglu"						,				_snake_case = True						,				_snake_case = True						,				):



									"""simple docstring"""





									super().__init__()
									_lowerCAmelCase						=    num_attention_heads
									_lowerCAmelCase						=    attention_head_dim
									_lowerCAmelCase						=    num_attention_heads * attention_head_dim

									_lowerCAmelCase						=    in_channels

									_lowerCAmelCase						=    torch.nn.GroupNorm(num_groups=_snake_case						,				num_channels=_snake_case						,				eps=1e-6						,				affine=_snake_case		)
									_lowerCAmelCase						=    nn.Linear(_snake_case						,				_snake_case		)

									# 3. Define transformers blocks
									_lowerCAmelCase						=    nn.ModuleList(
									    [
									        BasicTransformerBlock(
									            _snake_case						,				_snake_case						,				_snake_case						,				dropout=_snake_case						,				cross_attention_dim=_snake_case						,				activation_fn=_snake_case						,				attention_bias=_snake_case						,				double_self_attention=_snake_case						,				norm_elementwise_affine=_snake_case						,				)
									        for d in range(_snake_case		)
									    ]		)

									_lowerCAmelCase						=    nn.Linear(_snake_case						,				_snake_case		)




				def 							snake_case   (							self						,				_snake_case						,				_snake_case=None						,				_snake_case=None						,				_snake_case=None						,				_snake_case=1						,				_snake_case=None						,				_snake_case = True						,				):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase						=    hidden_states.shape
									_lowerCAmelCase						=    batch_frames // num_frames

									_lowerCAmelCase						=    hidden_states

									_lowerCAmelCase						=    hidden_states[None, :].reshape(_snake_case						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case		)
									_lowerCAmelCase						=    hidden_states.permute(0						,				2						,				1						,				3						,				4		)

									_lowerCAmelCase						=    self.norm(_snake_case		)
									_lowerCAmelCase						=    hidden_states.permute(0						,				3						,				4						,				2						,				1		).reshape(batch_size * height * width						,				_snake_case						,				_snake_case		)

									_lowerCAmelCase						=    self.proj_in(_snake_case		)

									# 2. Blocks
									for block in self.transformer_blocks:
														_lowerCAmelCase						=    block(
														    _snake_case						,				encoder_hidden_states=_snake_case						,				timestep=_snake_case						,				cross_attention_kwargs=_snake_case						,				class_labels=_snake_case						,				)

									# 3. Output
									_lowerCAmelCase						=    self.proj_out(_snake_case		)
									_lowerCAmelCase						=    (
									    hidden_states[None, None, :]
									    .reshape(_snake_case						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case		)
									    .permute(0						,				3						,				4						,				1						,				2		)
									    .contiguous()
									)
									_lowerCAmelCase						=    hidden_states.reshape(_snake_case						,				_snake_case						,				_snake_case						,				_snake_case		)

									_lowerCAmelCase						=    hidden_states + residual

									if not return_dict:
														return (output,)

									return TransformerTemporalModelOutput(sample=_snake_case		)









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

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[Any]							)					-> Union[str, Any]:
      SCREAMING_SNAKE_CASE_							=			['head.weight', 'head.bias']
      for k in ignore_keys:
            state_dict.pop(__UpperCAmelCase			,			__UpperCAmelCase							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[int]							)					-> str:
      # projection head is used in the self-supervised pre-training in MSN,
      # for downstream task it's not needed.
      SCREAMING_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(__UpperCAmelCase			,			__UpperCAmelCase							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Dict			,			__UpperCAmelCase						:	Any			,			__UpperCAmelCase						:	Any							)					-> Dict:
      SCREAMING_SNAKE_CASE_							=			dct.pop(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			val

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[int]			,			__UpperCAmelCase						:	Tuple							)					-> List[str]:
      SCREAMING_SNAKE_CASE_							=			ViTMSNConfig()
      SCREAMING_SNAKE_CASE_							=			10_00

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

      if "s16" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			3_84
            SCREAMING_SNAKE_CASE_							=			15_36
            SCREAMING_SNAKE_CASE_							=			6
      elif "l16" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			10_24
            SCREAMING_SNAKE_CASE_							=			40_96
            SCREAMING_SNAKE_CASE_							=			24
            SCREAMING_SNAKE_CASE_							=			16
            SCREAMING_SNAKE_CASE_							=			0.1
      elif "b4" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			4
      elif "l7" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			7
            SCREAMING_SNAKE_CASE_							=			10_24
            SCREAMING_SNAKE_CASE_							=			40_96
            SCREAMING_SNAKE_CASE_							=			24
            SCREAMING_SNAKE_CASE_							=			16
            SCREAMING_SNAKE_CASE_							=			0.1

      SCREAMING_SNAKE_CASE_							=			ViTMSNModel(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_							=			torch.hub.load_state_dict_from_url(__UpperCAmelCase			,			map_location='cpu'							)['target_encoder']

      SCREAMING_SNAKE_CASE_							=			ViTImageProcessor(size=config.image_size							)

      remove_projection_head(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			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()

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

      SCREAMING_SNAKE_CASE_							=			Image.open(requests.get(__UpperCAmelCase			,			stream=__UpperCAmelCase							).raw							)
      SCREAMING_SNAKE_CASE_							=			ViTImageProcessor(
          size=config.image_size			,			image_mean=__UpperCAmelCase			,			image_std=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			image_processor(images=__UpperCAmelCase			,			return_tensors='pt'							)

      # forward pass
      torch.manual_seed(2							)
      SCREAMING_SNAKE_CASE_							=			model(**__UpperCAmelCase							)
      SCREAMING_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:
            SCREAMING_SNAKE_CASE_							=			torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]]							)
      elif "b16" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]]							)
      elif "l16" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]]							)
      elif "b4" in checkpoint_url:
            SCREAMING_SNAKE_CASE_							=			torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]]							)
      else:
            SCREAMING_SNAKE_CASE_							=			torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]]							)

      # 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__":
   lowerCamelCase__ :   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.'
   )

   lowerCamelCase__ :   Union[str, Any]					=							parser.parse_args()
   convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)