




import pickle

import numpy as np
from matplotlib import pyplot as plt





class        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Any=0.2				,				_lowerCAmelCase  :		Dict=0.2						):
       SCREAMING_SNAKE_CASE_							=			bp_numa
       SCREAMING_SNAKE_CASE_							=			bp_numa
       SCREAMING_SNAKE_CASE_							=			bp_numa
       SCREAMING_SNAKE_CASE_							=			conva_get[:2]
       SCREAMING_SNAKE_CASE_							=			conva_get[2]
       SCREAMING_SNAKE_CASE_							=			size_pa
       SCREAMING_SNAKE_CASE_							=			rate_w
       SCREAMING_SNAKE_CASE_							=			rate_t
       SCREAMING_SNAKE_CASE_							=			[
           np.mat(-1 * np.random.rand(self.conva[0]				,				self.conva[0]						) + 0.5						)
           for i in range(self.conva[1]						)
       ]
       SCREAMING_SNAKE_CASE_							=			np.mat(-1 * np.random.rand(self.num_bpa				,				self.num_bpa						) + 0.5						)
       SCREAMING_SNAKE_CASE_							=			np.mat(-1 * np.random.rand(self.num_bpa				,				self.num_bpa						) + 0.5						)
       SCREAMING_SNAKE_CASE_							=			-2 * np.random.rand(self.conva[1]						) + 1
       SCREAMING_SNAKE_CASE_							=			-2 * np.random.rand(self.num_bpa						) + 1
       SCREAMING_SNAKE_CASE_							=			-2 * np.random.rand(self.num_bpa						) + 1



 def       lowerCAmelCase_  (							self  :		str				,				_lowerCAmelCase  :		int						):
       # save model dict with pickle
       SCREAMING_SNAKE_CASE_							=			{
           'num_bp1': self.num_bpa,
           'num_bp2': self.num_bpa,
           'num_bp3': self.num_bpa,
           'conv1': self.conva,
           'step_conv1': self.step_conva,
           'size_pooling1': self.size_poolinga,
           'rate_weight': self.rate_weight,
           'rate_thre': self.rate_thre,
           'w_conv1': self.w_conva,
           'wkj': self.wkj,
           'vji': self.vji,
           'thre_conv1': self.thre_conva,
           'thre_bp2': self.thre_bpa,
           'thre_bp3': self.thre_bpa,
       }
       with open(_lowerCAmelCase				,				'wb'						) as f:
             pickle.dump(_lowerCAmelCase				,				_lowerCAmelCase						)

       print(F"Model saved： {save_path}"						)



 @classmethod
 def       lowerCAmelCase_  (							cls  :		Any				,				_lowerCAmelCase  :		Tuple						):
       # read saved model
       with open(_lowerCAmelCase				,				'rb'						) as f:
             SCREAMING_SNAKE_CASE_							=			pickle.load(_lowerCAmelCase						)  # noqa: S301

       SCREAMING_SNAKE_CASE_							=			model_dic.get('conv1'						)
       conv_get.append(model_dic.get('step_conv1'						)						)
       SCREAMING_SNAKE_CASE_							=			model_dic.get('size_pooling1'						)
       SCREAMING_SNAKE_CASE_							=			model_dic.get('num_bp1'						)
       SCREAMING_SNAKE_CASE_							=			model_dic.get('num_bp2'						)
       SCREAMING_SNAKE_CASE_							=			model_dic.get('num_bp3'						)
       SCREAMING_SNAKE_CASE_							=			model_dic.get('rate_weight'						)
       SCREAMING_SNAKE_CASE_							=			model_dic.get('rate_thre'						)
       # create model instance
       SCREAMING_SNAKE_CASE_							=			CNN(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase						)
       # modify model parameter
       SCREAMING_SNAKE_CASE_							=			model_dic.get('w_conv1'						)
       SCREAMING_SNAKE_CASE_							=			model_dic.get('wkj'						)
       SCREAMING_SNAKE_CASE_							=			model_dic.get('vji'						)
       SCREAMING_SNAKE_CASE_							=			model_dic.get('thre_conv1'						)
       SCREAMING_SNAKE_CASE_							=			model_dic.get('thre_bp2'						)
       SCREAMING_SNAKE_CASE_							=			model_dic.get('thre_bp3'						)
       return conv_ins



 def       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		str						):
       return 1 / (1 + np.exp(-1 * x						))



 def       lowerCAmelCase_  (							self  :		List[str]				,				_lowerCAmelCase  :		Optional[Any]						):
       return round(_lowerCAmelCase				,				3						)



 def       lowerCAmelCase_  (							self  :		str				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		int						):
       # convolution process
       SCREAMING_SNAKE_CASE_							=			convs[0]
       SCREAMING_SNAKE_CASE_							=			convs[1]
       SCREAMING_SNAKE_CASE_							=			np.shape(_lowerCAmelCase						)[0]
       # get the data slice of original image data, data_focus
       SCREAMING_SNAKE_CASE_							=			[]
       for i_focus in range(0				,				size_data - size_conv + 1				,				_lowerCAmelCase						):
             for j_focus in range(0				,				size_data - size_conv + 1				,				_lowerCAmelCase						):
                   SCREAMING_SNAKE_CASE_							=			data[
                       i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
                   ]
                   data_focus.append(_lowerCAmelCase						)
        # calculate the feature map of every single kernel, and saved as list of matrix
       SCREAMING_SNAKE_CASE_							=			[]
       SCREAMING_SNAKE_CASE_							=			int((size_data - size_conv) / conv_step + 1						)
       for i_map in range(_lowerCAmelCase						):
             SCREAMING_SNAKE_CASE_							=			[]
             for i_focus in range(len(_lowerCAmelCase						)						):
                   SCREAMING_SNAKE_CASE_							=			(
                       np.sum(np.multiply(data_focus[i_focus]				,				w_convs[i_map]						)						)
                       - thre_convs[i_map]
                   )
                   featuremap.append(self.sig(_lowerCAmelCase						)						)
             SCREAMING_SNAKE_CASE_							=			np.asmatrix(_lowerCAmelCase						).reshape(
                 _lowerCAmelCase				,				_lowerCAmelCase						)
             data_featuremap.append(_lowerCAmelCase						)

       # expanding the data slice to One dimenssion
       SCREAMING_SNAKE_CASE_							=			[]
       for each_focus in data_focus:
             focusa_list.extend(self.Expand_Mat(_lowerCAmelCase						)						)
       SCREAMING_SNAKE_CASE_							=			np.asarray(_lowerCAmelCase						)
       return focus_list, data_featuremap



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		Optional[int]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		Union[str, Any]="average_pool"						):
       # pooling process
       SCREAMING_SNAKE_CASE_							=			len(featuremaps[0]						)
       SCREAMING_SNAKE_CASE_							=			int(size_map / size_pooling						)
       SCREAMING_SNAKE_CASE_							=			[]
       for i_map in range(len(_lowerCAmelCase						)						):
             SCREAMING_SNAKE_CASE_							=			featuremaps[i_map]
             SCREAMING_SNAKE_CASE_							=			[]
             for i_focus in range(0				,				_lowerCAmelCase				,				_lowerCAmelCase						):
                   for j_focus in range(0				,				_lowerCAmelCase				,				_lowerCAmelCase						):
                         SCREAMING_SNAKE_CASE_							=			feature_map[
                             i_focus : i_focus + size_pooling,
                             j_focus : j_focus + size_pooling,
                         ]
                         if pooling_type == "average_pool":
                               # average pooling
                               map_pooled.append(np.average(_lowerCAmelCase						)						)
                         elif pooling_type == "max_pooling":
                               # max pooling
                               map_pooled.append(np.max(_lowerCAmelCase						)						)
             SCREAMING_SNAKE_CASE_							=			np.asmatrix(_lowerCAmelCase						).reshape(_lowerCAmelCase				,				_lowerCAmelCase						)
             featuremap_pooled.append(_lowerCAmelCase						)
       return featuremap_pooled



 def       lowerCAmelCase_  (							self  :		Optional[Any]				,				_lowerCAmelCase  :		int						):
       # expanding three dimension data to one dimension list
       SCREAMING_SNAKE_CASE_							=			[]
       for i in range(len(_lowerCAmelCase						)						):
             SCREAMING_SNAKE_CASE_							=			np.shape(data[i]						)
             SCREAMING_SNAKE_CASE_							=			data[i].reshape(1				,				shapes[0] * shapes[1]						)
             SCREAMING_SNAKE_CASE_							=			data_listed.getA().tolist()[0]
             data_expanded.extend(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			np.asarray(_lowerCAmelCase						)
       return data_expanded



 def       lowerCAmelCase_  (							self  :		str				,				_lowerCAmelCase  :		str						):
       # expanding matrix to one dimension list
       SCREAMING_SNAKE_CASE_							=			np.asarray(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			np.shape(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			data_mat.reshape(1				,				shapes[0] * shapes[1]						)
       return data_expanded



 def       lowerCAmelCase_  (							self  :		Dict				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_							=			[]
       SCREAMING_SNAKE_CASE_							=			0
       for i_map in range(_lowerCAmelCase						):
             SCREAMING_SNAKE_CASE_							=			np.ones((size_map, size_map)						)
             for i in range(0				,				_lowerCAmelCase				,				_lowerCAmelCase						):
                   for j in range(0				,				_lowerCAmelCase				,				_lowerCAmelCase						):
                         SCREAMING_SNAKE_CASE_							=			pd_pool[
                             i_pool
                         ]
                         SCREAMING_SNAKE_CASE_							=			i_pool + 1
             SCREAMING_SNAKE_CASE_							=			np.multiply(
                 _lowerCAmelCase				,				np.multiply(out_map[i_map]				,				(1 - out_map[i_map])						)						)
             pd_all.append(_lowerCAmelCase						)
       return pd_all



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Any=bool						):
       # model traning
       print('----------------------Start Training-------------------------'						)
       print((' - - Shape: Train_Data  ', np.shape(_lowerCAmelCase						))						)
       print((' - - Shape: Teach_Data  ', np.shape(_lowerCAmelCase						))						)
       SCREAMING_SNAKE_CASE_							=			0
       SCREAMING_SNAKE_CASE_							=			[]
       SCREAMING_SNAKE_CASE_							=			10_000
       while rp < n_repeat and mse >= error_accuracy:
             SCREAMING_SNAKE_CASE_							=			0
             print(F"-------------Learning Time {rp}--------------"						)
             for p in range(len(_lowerCAmelCase						)						):
                   # print('------------Learning Image: %d--------------'%p)
                   SCREAMING_SNAKE_CASE_							=			np.asmatrix(datas_train[p]						)
                   SCREAMING_SNAKE_CASE_							=			np.asarray(datas_teach[p]						)
                   SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.convolute(
                       _lowerCAmelCase				,				self.conva				,				self.w_conva				,				self.thre_conva				,				conv_step=self.step_conva				,				)
                   SCREAMING_SNAKE_CASE_							=			self.pooling(_lowerCAmelCase				,				self.size_poolinga						)
                   SCREAMING_SNAKE_CASE_							=			np.shape(_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_							=			self._expand(_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_							=			data_bp_input

                   SCREAMING_SNAKE_CASE_							=			np.dot(_lowerCAmelCase				,				self.vji.T						) - self.thre_bpa
                   SCREAMING_SNAKE_CASE_							=			self.sig(_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_							=			np.dot(_lowerCAmelCase				,				self.wkj.T						) - self.thre_bpa
                   SCREAMING_SNAKE_CASE_							=			self.sig(_lowerCAmelCase						)

                   # --------------Model Leaning ------------------------
                   # calculate error and gradient---------------
                   SCREAMING_SNAKE_CASE_							=			np.multiply(
                       (data_teach - bp_outa)				,				np.multiply(_lowerCAmelCase				,				(1 - bp_outa)						)						)
                   SCREAMING_SNAKE_CASE_							=			np.multiply(
                       np.dot(_lowerCAmelCase				,				self.wkj						)				,				np.multiply(_lowerCAmelCase				,				(1 - bp_outa)						)						)
                   SCREAMING_SNAKE_CASE_							=			np.dot(_lowerCAmelCase				,				self.vji						)

                   SCREAMING_SNAKE_CASE_							=			pd_i_all / (self.size_poolinga * self.size_poolinga)
                   SCREAMING_SNAKE_CASE_							=			pd_conva_pooled.T.getA().tolist()
                   SCREAMING_SNAKE_CASE_							=			self._calculate_gradient_from_pool(
                       _lowerCAmelCase				,				_lowerCAmelCase				,				shape_featuremapa[0]				,				shape_featuremapa[1]				,				self.size_poolinga				,				)
                   # weight and threshold learning process---------
                   # convolution layer
                   for k_conv in range(self.conva[1]						):
                         SCREAMING_SNAKE_CASE_							=			self._expand_mat(pd_conva_all[k_conv]						)
                         SCREAMING_SNAKE_CASE_							=			self.rate_weight * np.dot(_lowerCAmelCase				,				_lowerCAmelCase						)

                         SCREAMING_SNAKE_CASE_							=			self.w_conva[k_conv] + delta_w.reshape(
                             (self.conva[0], self.conva[0])						)

                         SCREAMING_SNAKE_CASE_							=			(
                             self.thre_conva[k_conv]
                             - np.sum(pd_conva_all[k_conv]						) * self.rate_thre
                         )
                   # all connected layer
                   SCREAMING_SNAKE_CASE_							=			self.wkj + pd_k_all.T * bp_outa * self.rate_weight
                   SCREAMING_SNAKE_CASE_							=			self.vji + pd_j_all.T * bp_outa * self.rate_weight
                   SCREAMING_SNAKE_CASE_							=			self.thre_bpa - pd_k_all * self.rate_thre
                   SCREAMING_SNAKE_CASE_							=			self.thre_bpa - pd_j_all * self.rate_thre
                   # calculate the sum error of all single image
                   SCREAMING_SNAKE_CASE_							=			np.sum(abs(data_teach - bp_outa						)						)
                   error_count += errors
                   # print('   ----Teach      ',data_teach)
                   # print('   ----BP_output  ',bp_out3)
             SCREAMING_SNAKE_CASE_							=			rp + 1
             SCREAMING_SNAKE_CASE_							=			error_count / patterns
             all_mse.append(_lowerCAmelCase						)

       def draw_error():
             SCREAMING_SNAKE_CASE_							=			[error_accuracy for i in range(int(n_repeat * 1.2						)						)]
             plt.plot(_lowerCAmelCase				,				'+-'						)
             plt.plot(_lowerCAmelCase				,				'r--'						)
             plt.xlabel('Learning Times'						)
             plt.ylabel('All_mse'						)
             plt.grid(_lowerCAmelCase				,				alpha=0.5						)
             plt.show()

       print('------------------Training Complished---------------------'						)
       print((' - - Training epoch: ', rp, F"     - - Mse: {mse:.6f}")						)
       if draw_e:
             draw_error()
       return mse



 def       lowerCAmelCase_  (							self  :		Dict				,				_lowerCAmelCase  :		int						):
       # model predict
       SCREAMING_SNAKE_CASE_							=			[]
       print('-------------------Start Testing-------------------------'						)
       print((' - - Shape: Test_Data  ', np.shape(_lowerCAmelCase						))						)
       for p in range(len(_lowerCAmelCase						)						):
             SCREAMING_SNAKE_CASE_							=			np.asmatrix(datas_test[p]						)
             SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.convolute(
                 _lowerCAmelCase				,				self.conva				,				self.w_conva				,				self.thre_conva				,				conv_step=self.step_conva				,				)
             SCREAMING_SNAKE_CASE_							=			self.pooling(_lowerCAmelCase				,				self.size_poolinga						)
             SCREAMING_SNAKE_CASE_							=			self._expand(_lowerCAmelCase						)

             SCREAMING_SNAKE_CASE_							=			data_bp_input
             SCREAMING_SNAKE_CASE_							=			bp_outa * self.vji.T - self.thre_bpa
             SCREAMING_SNAKE_CASE_							=			self.sig(_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_							=			bp_outa * self.wkj.T - self.thre_bpa
             SCREAMING_SNAKE_CASE_							=			self.sig(_lowerCAmelCase						)
             produce_out.extend(bp_outa.getA().tolist()						)
       SCREAMING_SNAKE_CASE_							=			[list(map(self.do_round				,				_lowerCAmelCase						)						) for each in produce_out]
       return np.asarray(_lowerCAmelCase						)




 def       lowerCAmelCase_  (							self  :		List[Any]				,				_lowerCAmelCase  :		str						):
       # return the data of image after convoluting process so we can check it out
       SCREAMING_SNAKE_CASE_							=			np.asmatrix(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.convolute(
           _lowerCAmelCase				,				self.conva				,				self.w_conva				,				self.thre_conva				,				conv_step=self.step_conva				,				)
       SCREAMING_SNAKE_CASE_							=			self.pooling(_lowerCAmelCase				,				self.size_poolinga						)

       return data_conveda, data_pooleda


if __name__ == "__main__":
   pass





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)