



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 	_lowerCAmelCase (    A__						):
      lowercase__  =	SwinvaConfig()
      lowercase__  =	swinva_name.split('_'						)

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

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

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

      if ("22k" in swinva_name) and ("to" not in swinva_name):
            lowercase__  =	21_841
            lowercase__  =	'huggingface/label-files'
            lowercase__  =	'imagenet-22k-id2label.json'
            lowercase__  =	json.load(open(hf_hub_download(A__    ,	A__    ,	repo_type='dataset'						)    ,	'r'						)						)
            lowercase__  =	{int(A__						): v for k, v in idalabel.items()}
            lowercase__  =	idalabel
            lowercase__  =	{v: k for k, v in idalabel.items()}

      else:
            lowercase__  =	1_000
            lowercase__  =	'huggingface/label-files'
            lowercase__  =	'imagenet-1k-id2label.json'
            lowercase__  =	json.load(open(hf_hub_download(A__    ,	A__    ,	repo_type='dataset'						)    ,	'r'						)						)
            lowercase__  =	{int(A__						): v for k, v in idalabel.items()}
            lowercase__  =	idalabel
            lowercase__  =	{v: k for k, v in idalabel.items()}

      lowercase__  =	img_size
      lowercase__  =	num_classes
      lowercase__  =	embed_dim
      lowercase__  =	depths
      lowercase__  =	num_heads
      lowercase__  =	window_size

      return config

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

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

      return name

def 	_lowerCAmelCase (    A__    ,	A__						):
      for key in orig_state_dict.copy().keys():
            lowercase__  =	orig_state_dict.pop(A__						)

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

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

      return orig_state_dict

def 	_lowerCAmelCase (    A__    ,	A__						):
      lowercase__  =	timm.create_model(A__    ,	pretrained=A__						)
      timm_model.eval()

      lowercase__  =	get_swinva_config(A__						)
      lowercase__  =	SwinvaForImageClassification(A__						)
      model.eval()

      lowercase__  =	convert_state_dict(timm_model.state_dict()    ,	A__						)
      model.load_state_dict(A__						)

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

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

      lowercase__  =	timm_model(inputs['pixel_values']						)
      lowercase__  =	model(**A__						).logits

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

      print(F'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''						)
      model.save_pretrained(A__						)

      print(F'''Saving image processor to {pytorch_dump_folder_path}'''						)
      image_processor.save_pretrained(A__						)

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


if __name__ == "__main__":
       a__   :					Tuple 						=      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."
       )

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





def 	_lowerCAmelCase (    A__ = 50_000_000						):
      lowercase__  =	set()
      lowercase__  =	int((limit - 24) ** (1 / 2)						)

      lowercase__  =	set(range(3    ,	prime_square_limit + 1    ,	2						)						)
      primes.add(2						)
      for p in range(3    ,	prime_square_limit + 1    ,	2						):
            if p not in primes:
                  continue
            primes.difference_update(set(range(p * p    ,	prime_square_limit + 1    ,	A__						)						)						)

      for primea in primes:
            lowercase__  =	primea * primea
            for primea in primes:
                  lowercase__  =	primea * primea * primea
                  if square + cube >= limit - 16:
                        break
                  for primea in primes:
                        lowercase__  =	primea * primea * primea * primea
                        lowercase__  =	square + cube + tetr
                        if total >= limit:
                              break
                        ret.add(A__						)

      return len(A__						)


if __name__ == "__main__":
       print(F'''{solution() = }''')
