





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification

def   a_   (			lowerCAmelCase_				:      Optional[int]     ):
   __lowerCAmelCase							     =     SwinvaConfig()
   __lowerCAmelCase							     =     swinva_name.split('_'     )

   __lowerCAmelCase							     =     name_split[1]
   if "to" in name_split[3]:
      __lowerCAmelCase							     =     int(name_split[3][-3:]     )
   else:
      __lowerCAmelCase							     =     int(name_split[3]     )
   if "to" in name_split[2]:
      __lowerCAmelCase							     =     int(name_split[2][-2:]     )
   else:
      __lowerCAmelCase							     =     int(name_split[2][6:]     )

   if model_size == "tiny":
      __lowerCAmelCase							     =     96
      __lowerCAmelCase							     =     (2, 2, 6, 2)
      __lowerCAmelCase							     =     (3, 6, 12, 24)
   elif model_size == "small":
      __lowerCAmelCase							     =     96
      __lowerCAmelCase							     =     (2, 2, 18, 2)
      __lowerCAmelCase							     =     (3, 6, 12, 24)
   elif model_size == "base":
      __lowerCAmelCase							     =     128
      __lowerCAmelCase							     =     (2, 2, 18, 2)
      __lowerCAmelCase							     =     (4, 8, 16, 32)
   else:
      __lowerCAmelCase							     =     192
      __lowerCAmelCase							     =     (2, 2, 18, 2)
      __lowerCAmelCase							     =     (6, 12, 24, 48)

   if "to" in swinva_name:
      __lowerCAmelCase							     =     (12, 12, 12, 6)

   if ("22k" in swinva_name) and ("to" not in swinva_name):
      __lowerCAmelCase							     =     2_1841
      __lowerCAmelCase							     =     """huggingface/label-files"""
      __lowerCAmelCase							     =     """imagenet-22k-id2label.json"""
      __lowerCAmelCase							     =     json.load(open(hf_hub_download(lowerCAmelCase__,     lowerCAmelCase__,     repo_type='dataset'     ),     'r'     )     )
      __lowerCAmelCase							     =     {int(lowerCAmelCase__     ): v for k, v in idalabel.items()}
      __lowerCAmelCase							     =     idalabel
      __lowerCAmelCase							     =     {v: k for k, v in idalabel.items()}

   else:
      __lowerCAmelCase							     =     1000
      __lowerCAmelCase							     =     """huggingface/label-files"""
      __lowerCAmelCase							     =     """imagenet-1k-id2label.json"""
      __lowerCAmelCase							     =     json.load(open(hf_hub_download(lowerCAmelCase__,     lowerCAmelCase__,     repo_type='dataset'     ),     'r'     )     )
      __lowerCAmelCase							     =     {int(lowerCAmelCase__     ): v for k, v in idalabel.items()}
      __lowerCAmelCase							     =     idalabel
      __lowerCAmelCase							     =     {v: k for k, v in idalabel.items()}

   __lowerCAmelCase							     =     img_size
   __lowerCAmelCase							     =     num_classes
   __lowerCAmelCase							     =     embed_dim
   __lowerCAmelCase							     =     depths
   __lowerCAmelCase							     =     num_heads
   __lowerCAmelCase							     =     window_size

   return config

def   a_   (			lowerCAmelCase_				:      List[Any]     ):





   if "patch_embed.proj" in name:
      __lowerCAmelCase							     =     name.replace('patch_embed.proj',     'embeddings.patch_embeddings.projection'     )
   if "patch_embed.norm" in name:
      __lowerCAmelCase							     =     name.replace('patch_embed.norm',     'embeddings.norm'     )
   if "layers" in name:
      __lowerCAmelCase							     =     """encoder.""" + name
   if "attn.proj" in name:
      __lowerCAmelCase							     =     name.replace('attn.proj',     'attention.output.dense'     )
   if "attn" in name:
      __lowerCAmelCase							     =     name.replace('attn',     'attention.self'     )
   if "norm1" in name:
      __lowerCAmelCase							     =     name.replace('norm1',     'layernorm_before'     )
   if "norm2" in name:
      __lowerCAmelCase							     =     name.replace('norm2',     'layernorm_after'     )
   if "mlp.fc1" in name:
      __lowerCAmelCase							     =     name.replace('mlp.fc1',     'intermediate.dense'     )
   if "mlp.fc2" in name:
      __lowerCAmelCase							     =     name.replace('mlp.fc2',     'output.dense'     )
   if "q_bias" in name:
      __lowerCAmelCase							     =     name.replace('q_bias',     'query.bias'     )
   if "k_bias" in name:
      __lowerCAmelCase							     =     name.replace('k_bias',     'key.bias'     )
   if "v_bias" in name:
      __lowerCAmelCase							     =     name.replace('v_bias',     'value.bias'     )
   if "cpb_mlp" in name:
      __lowerCAmelCase							     =     name.replace('cpb_mlp',     'continuous_position_bias_mlp'     )
   if name == "norm.weight":
      __lowerCAmelCase							     =     """layernorm.weight"""
   if name == "norm.bias":
      __lowerCAmelCase							     =     """layernorm.bias"""

   if "head" in name:
      __lowerCAmelCase							     =     name.replace('head',     'classifier'     )
   else:
      __lowerCAmelCase							     =     """swinv2.""" + name

   return name

def   a_   (			lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      int     ):





   for key in orig_state_dict.copy().keys():
      __lowerCAmelCase							     =     orig_state_dict.pop(lowerCAmelCase__     )

      if "mask" in key:
         continue
      elif "qkv" in key:
         __lowerCAmelCase							     =     key.split('.'     )
         __lowerCAmelCase							     =     int(key_split[1]     )
         __lowerCAmelCase							     =     int(key_split[3]     )
         __lowerCAmelCase							     =     model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            __lowerCAmelCase							     =     val[:dim, :]
            __lowerCAmelCase							     =     val[dim : dim * 2, :]
            __lowerCAmelCase							     =     val[-dim:, :]
         else:
            __lowerCAmelCase							     =     val[:dim]
            __lowerCAmelCase							     =     val[
                dim : dim * 2
            ]
            __lowerCAmelCase							     =     val[-dim:]
      else:
         __lowerCAmelCase							     =     val

   return orig_state_dict

def   a_   (			lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      str     ):
   __lowerCAmelCase							     =     timm.create_model(lowerCAmelCase__,     pretrained=lowerCAmelCase__     )
   timm_model.eval()

   __lowerCAmelCase							     =     get_swinva_config(lowerCAmelCase__     )
   __lowerCAmelCase							     =     SwinvaForImageClassification(lowerCAmelCase__     )
   model.eval()

   __lowerCAmelCase							     =     convert_state_dict(timm_model.state_dict(),     lowerCAmelCase__     )
   model.load_state_dict(lowerCAmelCase__     )

   __lowerCAmelCase							     =     """http://images.cocodataset.org/val2017/000000039769.jpg"""

   __lowerCAmelCase							     =     AutoImageProcessor.from_pretrained('microsoft/{}'.format(swinva_name.replace('_',     '-'     )     )     )
   __lowerCAmelCase							     =     Image.open(requests.get(lowerCAmelCase__,     stream=lowerCAmelCase__     ).raw     )
   __lowerCAmelCase							     =     image_processor(images=lowerCAmelCase__,     return_tensors='pt'     )

   __lowerCAmelCase							     =     timm_model(inputs['pixel_values']     )
   __lowerCAmelCase							     =     model(**lowerCAmelCase__     ).logits

   assert torch.allclose(lowerCAmelCase__,     lowerCAmelCase__,     atol=1E-3     )

   print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""     )
   model.save_pretrained(lowerCAmelCase__     )

   print(F"""Saving image processor to {pytorch_dump_folder_path}"""     )
   image_processor.save_pretrained(lowerCAmelCase__     )

   model.push_to_hub(
       repo_path_or_name=Path(lowerCAmelCase__,     lowerCAmelCase__     ),     organization='nandwalritik',     commit_message='Add model',     )


if __name__ == "__main__":
       _snake_case  :   Any						   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--swinv2_name',
           default='swinv2_tiny_patch4_window8_256',
           type=str,
           help='Name of the Swinv2 timm model you\'d like to convert.',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
       )

       _snake_case  :   List[Any]						   =  parser.parse_args()
       convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)








import mpmath  # for roots of unity
import numpy as np



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		List[Any] ,  lowerCAmelCase_			:		Dict=None ,  lowerCAmelCase_			:		str=None					)							->						List[Any]:
     # Input as list
     __lowerCAmelCase							     =     list(poly_a or [0]					)[:]
     __lowerCAmelCase							     =     list(poly_b or [0]					)[:]

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

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

     # Add 0 to make lengths equal a power of 2
     __lowerCAmelCase							     =     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
     __lowerCAmelCase							     =     complex(mpmath.root(x=1 ,  n=self.c_max_length ,  k=1					)					)

     # The product
     __lowerCAmelCase							     =     self.__multiply()






  def     lowercase		(    self			:		Optional[int] ,  lowerCAmelCase_			:		str					)							->						Optional[int]:
     __lowerCAmelCase							     =     [[x] for x in self.polyA] if which == 'A' else [[x] for x in self.polyB]
     # Corner case
     if len(lowerCAmelCase_					) <= 1:
        return dft[0]
     #
     __lowerCAmelCase							     =     self.c_max_length // 2
     while next_ncol > 0:
        __lowerCAmelCase							     =     [[] for i in range(lowerCAmelCase_					)]
        __lowerCAmelCase							     =     self.root**next_ncol

        # First half of next step
        __lowerCAmelCase							     =     1
        for j in range(self.c_max_length // (next_ncol * 2)					):
           for i in range(lowerCAmelCase_					):
              new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]					)
           current_root *= root
        # Second half of next step
        __lowerCAmelCase							     =     1
        for j in range(self.c_max_length // (next_ncol * 2)					):
           for i in range(lowerCAmelCase_					):
              new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]					)
           current_root *= root
        # Update
        __lowerCAmelCase							     =     new_dft
        __lowerCAmelCase							     =     next_ncol // 2
     return dft[0]






  def     lowercase		(    self			:		Optional[int]					)							->						Any:
     __lowerCAmelCase							     =     self.__dft('A'					)
     __lowerCAmelCase							     =     self.__dft('B'					)
     __lowerCAmelCase							     =     [[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
     __lowerCAmelCase							     =     2
     while next_ncol <= self.c_max_length:
        __lowerCAmelCase							     =     [[] for i in range(lowerCAmelCase_					)]
        __lowerCAmelCase							     =     self.root ** (next_ncol // 2)
        __lowerCAmelCase							     =     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
        __lowerCAmelCase							     =     new_inverse_c
        next_ncol *= 2
     # Unpack
     __lowerCAmelCase							     =     [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			:		Dict					)							->						int:
     __lowerCAmelCase							     =     'A = ' + ' + '.join(
         f"""{coef}*x^{i}""" for coef, i in enumerate(self.polyA[: self.len_A]					)					)
     __lowerCAmelCase							     =     'B = ' + ' + '.join(
         f"""{coef}*x^{i}""" for coef, i in enumerate(self.polyB[: self.len_B]					)					)
     __lowerCAmelCase							     =     '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()

