




'''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'''



# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCamelCase_			=						abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Dict  )			->			Dict:
   from diffusers.utils.testing_utils import pytest_addoption_shared

   pytest_addoption_shared(__A  )






def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]  )			->			str:
   from diffusers.utils.testing_utils import pytest_terminal_summary_main

   _SCREAMING_SNAKE_CASE	=				terminalreporter.config.getoption("--make-reports"  )
   if make_reports:
      pytest_terminal_summary_main(__A	,					id=__A  )

