


import mpmath  # for roots of unity
import numpy as np

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case=None      , __snake_case=None   ):
         snake_case					=    list(poly_a or [0]   )[:]
         snake_case					=    list(poly_b or [0]   )[:]

         # Remove leading zero coefficients
         while self.polyA[-1] == 0:
              self.polyA.pop()
         snake_case					=    len(self.polyA   )

         while self.polyB[-1] == 0:
              self.polyB.pop()
         snake_case					=    len(self.polyB   )

         # Add 0 to make lengths equal a power of 2
         snake_case					=    int(
             2 ** np.ceil(np.loga(len(self.polyA   ) + len(self.polyB   ) - 1   )   )   )

         while len(self.polyA   ) < self.c_max_length:
              self.polyA.append(0   )
         while len(self.polyB   ) < self.c_max_length:
              self.polyB.append(0   )
         # A complex root used for the fourier transform
         snake_case					=    complex(mpmath.root(x=1      , n=self.c_max_length      , k=1   )   )

         # The product
         snake_case					=    self.__multiply()
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    [[x] for x in self.polyA] if which == "A" else [[x] for x in self.polyB]
         # Corner case
         if len(_SCREAMING_SNAKE_CASE   ) <= 1:
              return dft[0]
         #
         snake_case					=    self.c_max_length // 2
         while next_ncol > 0:
              snake_case					=    [[] for i in range(_SCREAMING_SNAKE_CASE   )]
              snake_case					=    self.root**next_ncol

              # First half of next step
              snake_case					=    1
              for j in range(self.c_max_length // (next_ncol * 2)   ):
                   for i in range(_SCREAMING_SNAKE_CASE   ):
                        new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]   )
                   current_root *= root
              # Second half of next step
              snake_case					=    1
              for j in range(self.c_max_length // (next_ncol * 2)   ):
                   for i in range(_SCREAMING_SNAKE_CASE   ):
                        new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]   )
                   current_root *= root
              # Update
              snake_case					=    new_dft
              snake_case					=    next_ncol // 2
         return dft[0]
    def 		a_      (	self   ):
         snake_case					=    self.__dft('''A'''   )
         snake_case					=    self.__dft('''B'''   )
         snake_case					=    [[dft_a[i] * dft_b[i] for i in range(self.c_max_length   )]]
         del dft_a
         del dft_b

         # Corner Case
         if len(inverce_c[0]   ) <= 1:
              return inverce_c[0]
         # Inverse DFT
         snake_case					=    2
         while next_ncol <= self.c_max_length:
              snake_case					=    [[] for i in range(_SCREAMING_SNAKE_CASE   )]
              snake_case					=    self.root ** (next_ncol // 2)
              snake_case					=    1
              # First half of next step
              for j in range(self.c_max_length // next_ncol   ):
                   for i in range(next_ncol // 2   ):
                        # Even positions
                        new_inverse_c[i].append(
                            (
                                inverce_c[i][j]
                                + inverce_c[i][j + self.c_max_length // next_ncol]
                            )
                            / 2   )
                        # Odd positions
                        new_inverse_c[i + next_ncol // 2].append(
                            (
                                inverce_c[i][j]
                                - inverce_c[i][j + self.c_max_length // next_ncol]
                            )
                            / (2 * current_root)   )
                   current_root *= root
              # Update
              snake_case					=    new_inverse_c
              next_ncol *= 2
         # Unpack
         snake_case					=    [round(x[0].real      , 8   ) + round(x[0].imag      , 8   ) * 1J for x in inverce_c]

         # Remove leading 0's
         while inverce_c[-1] == 0:
              inverce_c.pop()
         return inverce_c




    def __str__(	self   ):
         snake_case					=    "A = " + " + ".join(
             F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A]   )   )
         snake_case					=    "B = " + " + ".join(
             F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B]   )   )
         snake_case					=    "A*B = " + " + ".join(
             F'''{coef}*x^{i}''' for coef, i in enumerate(self.product   )   )

         return F'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()








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_							,UpperCamelCase_=False    ):



     """simple docstring"""






     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       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=False    ):



     """simple docstring"""






     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       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    ['''head.weight''', '''head.bias''']
     for k in ignore_keys:
          state_dict.pop(UpperCamelCase_							,UpperCamelCase_    )
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     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_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    dct.pop(UpperCamelCase_    )
     snake_case					=    val
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    ViTMSNConfig()
     snake_case					=    10_00

     snake_case					=    '''datasets/huggingface/label-files'''
     snake_case					=    '''imagenet-1k-id2label.json'''
     snake_case					=    json.load(open(hf_hub_download(UpperCamelCase_							,UpperCamelCase_    )							,'''r'''    )    )
     snake_case					=    {int(UpperCamelCase_    ): 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					=    3_84
          snake_case					=    15_36
          snake_case					=    6
     elif "l16" in checkpoint_url:
          snake_case					=    10_24
          snake_case					=    40_96
          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					=    10_24
          snake_case					=    40_96
          snake_case					=    24
          snake_case					=    16
          snake_case					=    0.1

     snake_case					=    ViTMSNModel(UpperCamelCase_    )

     snake_case					=    torch.hub.load_state_dict_from_url(UpperCamelCase_							,map_location='''cpu'''    )['''target_encoder''']

     snake_case					=    ViTImageProcessor(size=config.image_size    )

     remove_projection_head(UpperCamelCase_    )
     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()

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

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

     # forward pass
     torch.manual_seed(2    )
     snake_case					=    model(**UpperCamelCase_    )
     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([[-1.0915, -1.4876, -1.1809]]    )
     elif "b16" in checkpoint_url:
          snake_case					=    torch.tensor([[14.2889, -18.9045, 11.7281]]    )
     elif "l16" in checkpoint_url:
          snake_case					=    torch.tensor([[41.5028, -22.8681, 45.6475]]    )
     elif "b4" in checkpoint_url:
          snake_case					=    torch.tensor([[-4.3868, 5.2932, -0.4137]]    )
     else:
          snake_case					=    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__":
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   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."
 )

 _SCREAMING_SNAKE_CASE      :			Any      				=   parser.parse_args()
 convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




