



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     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      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								=		21841
            lowerCamelCase								=		"huggingface/label-files"
            lowerCamelCase								=		"imagenet-22k-id2label.json"
            lowerCamelCase								=		json.load(open(hf_hub_download(UpperCAmelCase__   ,   UpperCAmelCase__   ,   repo_type="dataset" )   ,   "r" ) )
            lowerCamelCase								=		{int(UpperCAmelCase__ ): 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(UpperCAmelCase__   ,   UpperCAmelCase__   ,   repo_type="dataset" )   ,   "r" ) )
            lowerCamelCase								=		{int(UpperCAmelCase__ ): 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     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      for key in orig_state_dict.copy().keys():
            lowerCamelCase								=		orig_state_dict.pop(UpperCAmelCase__ )

            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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		timm.create_model(UpperCAmelCase__   ,   pretrained=UpperCAmelCase__ )
      timm_model.eval()

      lowerCamelCase								=		get_swinva_config(UpperCAmelCase__ )
      lowerCamelCase								=		SwinvaForImageClassification(UpperCAmelCase__ )
      model.eval()

      lowerCamelCase								=		convert_state_dict(timm_model.state_dict()   ,   UpperCAmelCase__ )
      model.load_state_dict(UpperCAmelCase__ )

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

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

      lowerCamelCase								=		timm_model(inputs["pixel_values"] )
      lowerCamelCase								=		model(**UpperCAmelCase__ ).logits

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

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

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

      model.push_to_hub(
          repo_path_or_name=Path(UpperCAmelCase__   ,   UpperCAmelCase__ )   ,   organization="nandwalritik"   ,   commit_message="Add model"   ,   )


if __name__ == "__main__":
     a_						:     Dict	     =		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_						:     Optional[int]	     =		parser.parse_args()
     convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)




import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


a_						:     Tuple	     =		logging.get_logger(__name__)





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			CLIPConfig

     _A       =			['CLIPEncoderLayer']







     def __init__(self				,			__a    ):






           '''simple docstring'''



           super().__init__(__a    )

           lowerCamelCase								=		CLIPVisionModelWithProjection(config.vision_config    )

           lowerCamelCase								=		nn.Linear(config.vision_config.projection_dim				,			1    )
           lowerCamelCase								=		nn.Linear(config.vision_config.projection_dim				,			1    )







     @torch.no_grad()
     def 						_a      (self				,			__a				,			__a				,			__a=0.5				,			__a=0.5    ):






           '''simple docstring'''



           lowerCamelCase								=		self.vision_model(__a    )[0]

           lowerCamelCase								=		self.p_head(__a    )
           lowerCamelCase								=		nsfw_detected.flatten()
           lowerCamelCase								=		nsfw_detected > p_threshold
           lowerCamelCase								=		nsfw_detected.tolist()

           if any(__a    ):
                 logger.warning(
                     "Potential NSFW content was detected in one or more images. A black image will be returned instead."
                     " Try again with a different prompt and/or seed."    )

           for idx, nsfw_detected_ in enumerate(__a    ):
                 if nsfw_detected_:
                       lowerCamelCase								=		np.zeros(images[idx].shape    )

           lowerCamelCase								=		self.w_head(__a    )
           lowerCamelCase								=		watermark_detected.flatten()
           lowerCamelCase								=		watermark_detected > w_threshold
           lowerCamelCase								=		watermark_detected.tolist()

           if any(__a    ):
                 logger.warning(
                     "Potential watermarked content was detected in one or more images. A black image will be returned instead."
                     " Try again with a different prompt and/or seed."    )

           for idx, watermark_detected_ in enumerate(__a    ):
                 if watermark_detected_:
                       lowerCamelCase								=		np.zeros(images[idx].shape    )

           return images, nsfw_detected, watermark_detected