




'''simple docstring'''



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 			SCREAMING_SNAKE_CASE_			(							__A    :			int  )			->			List[str]:
   _SCREAMING_SNAKE_CASE	=				SwinvaConfig()
   _SCREAMING_SNAKE_CASE	=				swinva_name.split("_"  )

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

   if model_size == "tiny":
      _SCREAMING_SNAKE_CASE	=				96
      _SCREAMING_SNAKE_CASE	=				(2, 2, 6, 2)
      _SCREAMING_SNAKE_CASE	=				(3, 6, 12, 24)
   elif model_size == "small":
      _SCREAMING_SNAKE_CASE	=				96
      _SCREAMING_SNAKE_CASE	=				(2, 2, 18, 2)
      _SCREAMING_SNAKE_CASE	=				(3, 6, 12, 24)
   elif model_size == "base":
      _SCREAMING_SNAKE_CASE	=				1_28
      _SCREAMING_SNAKE_CASE	=				(2, 2, 18, 2)
      _SCREAMING_SNAKE_CASE	=				(4, 8, 16, 32)
   else:
      _SCREAMING_SNAKE_CASE	=				1_92
      _SCREAMING_SNAKE_CASE	=				(2, 2, 18, 2)
      _SCREAMING_SNAKE_CASE	=				(6, 12, 24, 48)

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

   if ("22k" in swinva_name) and ("to" not in swinva_name):
      _SCREAMING_SNAKE_CASE	=				2_18_41
      _SCREAMING_SNAKE_CASE	=				"huggingface/label-files"
      _SCREAMING_SNAKE_CASE	=				"imagenet-22k-id2label.json"
      _SCREAMING_SNAKE_CASE	=				json.load(open(hf_hub_download(__A	,					__A	,					repo_type="dataset"  )	,					"r"  )  )
      _SCREAMING_SNAKE_CASE	=				{int(__A  ): v for k, v in idalabel.items()}
      _SCREAMING_SNAKE_CASE	=				idalabel
      _SCREAMING_SNAKE_CASE	=				{v: k for k, v in idalabel.items()}

   else:
      _SCREAMING_SNAKE_CASE	=				10_00
      _SCREAMING_SNAKE_CASE	=				"huggingface/label-files"
      _SCREAMING_SNAKE_CASE	=				"imagenet-1k-id2label.json"
      _SCREAMING_SNAKE_CASE	=				json.load(open(hf_hub_download(__A	,					__A	,					repo_type="dataset"  )	,					"r"  )  )
      _SCREAMING_SNAKE_CASE	=				{int(__A  ): v for k, v in idalabel.items()}
      _SCREAMING_SNAKE_CASE	=				idalabel
      _SCREAMING_SNAKE_CASE	=				{v: k for k, v in idalabel.items()}

   _SCREAMING_SNAKE_CASE	=				img_size
   _SCREAMING_SNAKE_CASE	=				num_classes
   _SCREAMING_SNAKE_CASE	=				embed_dim
   _SCREAMING_SNAKE_CASE	=				depths
   _SCREAMING_SNAKE_CASE	=				num_heads
   _SCREAMING_SNAKE_CASE	=				window_size

   return config
def 			SCREAMING_SNAKE_CASE_			(							__A    :			str  )			->			List[Any]:
   if "patch_embed.proj" in name:
      _SCREAMING_SNAKE_CASE	=				name.replace("patch_embed.proj"	,					"embeddings.patch_embeddings.projection"  )
   if "patch_embed.norm" in name:
      _SCREAMING_SNAKE_CASE	=				name.replace("patch_embed.norm"	,					"embeddings.norm"  )
   if "layers" in name:
      _SCREAMING_SNAKE_CASE	=				"encoder." + name
   if "attn.proj" in name:
      _SCREAMING_SNAKE_CASE	=				name.replace("attn.proj"	,					"attention.output.dense"  )
   if "attn" in name:
      _SCREAMING_SNAKE_CASE	=				name.replace("attn"	,					"attention.self"  )
   if "norm1" in name:
      _SCREAMING_SNAKE_CASE	=				name.replace("norm1"	,					"layernorm_before"  )
   if "norm2" in name:
      _SCREAMING_SNAKE_CASE	=				name.replace("norm2"	,					"layernorm_after"  )
   if "mlp.fc1" in name:
      _SCREAMING_SNAKE_CASE	=				name.replace("mlp.fc1"	,					"intermediate.dense"  )
   if "mlp.fc2" in name:
      _SCREAMING_SNAKE_CASE	=				name.replace("mlp.fc2"	,					"output.dense"  )
   if "q_bias" in name:
      _SCREAMING_SNAKE_CASE	=				name.replace("q_bias"	,					"query.bias"  )
   if "k_bias" in name:
      _SCREAMING_SNAKE_CASE	=				name.replace("k_bias"	,					"key.bias"  )
   if "v_bias" in name:
      _SCREAMING_SNAKE_CASE	=				name.replace("v_bias"	,					"value.bias"  )
   if "cpb_mlp" in name:
      _SCREAMING_SNAKE_CASE	=				name.replace("cpb_mlp"	,					"continuous_position_bias_mlp"  )
   if name == "norm.weight":
      _SCREAMING_SNAKE_CASE	=				"layernorm.weight"
   if name == "norm.bias":
      _SCREAMING_SNAKE_CASE	=				"layernorm.bias"

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

   return name
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Dict	,					__A    :			List[str]  )			->			Dict:
   for key in orig_state_dict.copy().keys():
      _SCREAMING_SNAKE_CASE	=				orig_state_dict.pop(__A  )

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

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

   return orig_state_dict
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]	,					__A    :			Union[str, Any]  )			->			List[str]:
   _SCREAMING_SNAKE_CASE	=				timm.create_model(__A	,					pretrained=__A  )
   timm_model.eval()

   _SCREAMING_SNAKE_CASE	=				get_swinva_config(__A  )
   _SCREAMING_SNAKE_CASE	=				SwinvaForImageClassification(__A  )
   model.eval()

   _SCREAMING_SNAKE_CASE	=				convert_state_dict(timm_model.state_dict()	,					__A  )
   model.load_state_dict(__A  )

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

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

   _SCREAMING_SNAKE_CASE	=				timm_model(inputs["pixel_values"]  )
   _SCREAMING_SNAKE_CASE	=				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__":
 lowerCamelCase_			=						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.'
 )

 lowerCamelCase_			=						parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)







'''simple docstring'''



import torch

from transformers import AutoModel







class 		lowercase_   (			torch.nn.Module       ):


   """simple docstring"""

   def __init__(   self :     List[Any]  ,				__lowerCamelCase :     Union[str, Any]="sayef/fsner-bert-base-uncased"   ):

      """simple docstring"""
      super(__lowerCamelCase  ,				self   ).__init__()

      _SCREAMING_SNAKE_CASE	=				AutoModel.from_pretrained(__lowerCamelCase  ,				return_dict=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				torch.nn.CosineSimilarity(3  ,				1e-08   )
      _SCREAMING_SNAKE_CASE	=				torch.nn.Softmax(dim=1   )



   def 							lowerCAmelCase_      (   self :     Dict  ,				**__lowerCamelCase :     Any   ):

      """simple docstring"""
      return self.bert(**__lowerCamelCase   ).last_hidden_state



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      return token_embeddings.sum(2  ,				keepdim=__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Tuple=1   ):

      """simple docstring"""
      return self.softmax(T * self.cos(__lowerCamelCase  ,				__lowerCamelCase   )   )



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				W_supports["sizes"].tolist()
      _SCREAMING_SNAKE_CASE	=				W_supports["start_token_id"].item()
      _SCREAMING_SNAKE_CASE	=				W_supports["end_token_id"].item()

      del W_supports["sizes"]
      del W_supports["start_token_id"]
      del W_supports["end_token_id"]

      _SCREAMING_SNAKE_CASE	=				self.BERT(**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.BERT(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				None
      _SCREAMING_SNAKE_CASE	=				None

      _SCREAMING_SNAKE_CASE	=				W_supports["input_ids"] == start_token_id
      _SCREAMING_SNAKE_CASE	=				W_supports["input_ids"] == end_token_id

      for i, size in enumerate(__lowerCamelCase   ):
         if i == 0:
            _SCREAMING_SNAKE_CASE	=				0
         else:
            _SCREAMING_SNAKE_CASE	=				support_sizes[i - 1]

         _SCREAMING_SNAKE_CASE	=				S[s : s + size][start_token_masks[s : s + size]]
         _SCREAMING_SNAKE_CASE	=				S[s : s + size][end_token_masks[s : s + size]]

         _SCREAMING_SNAKE_CASE	=				torch.matmul(q[i]  ,				s_start.T   ).sum(1   ).softmax(0   )
         _SCREAMING_SNAKE_CASE	=				torch.matmul(q[i]  ,				s_end.T   ).sum(1   ).softmax(0   )

         if p_starts is not None:
            _SCREAMING_SNAKE_CASE	=				torch.vstack((p_starts, p_start)   )
            _SCREAMING_SNAKE_CASE	=				torch.vstack((p_ends, p_end)   )
         else:
            _SCREAMING_SNAKE_CASE	=				p_start
            _SCREAMING_SNAKE_CASE	=				p_end

      return p_starts, p_ends

