


import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Union[str, Any]:
   UpperCAmelCase      					=  SwinConfig()
   UpperCAmelCase      					=  swin_name.split("""_""" )

   UpperCAmelCase      					=  name_split[1]
   UpperCAmelCase      					=  int(name_split[4] )
   UpperCAmelCase      					=  int(name_split[3][-1] )

   if model_size == "tiny":
      UpperCAmelCase      					=  9_6
      UpperCAmelCase      					=  (2, 2, 6, 2)
      UpperCAmelCase      					=  (3, 6, 1_2, 2_4)
   elif model_size == "small":
      UpperCAmelCase      					=  9_6
      UpperCAmelCase      					=  (2, 2, 1_8, 2)
      UpperCAmelCase      					=  (3, 6, 1_2, 2_4)
   elif model_size == "base":
      UpperCAmelCase      					=  1_2_8
      UpperCAmelCase      					=  (2, 2, 1_8, 2)
      UpperCAmelCase      					=  (4, 8, 1_6, 3_2)
   else:
      UpperCAmelCase      					=  1_9_2
      UpperCAmelCase      					=  (2, 2, 1_8, 2)
      UpperCAmelCase      					=  (6, 1_2, 2_4, 4_8)

   if "in22k" in swin_name:
      UpperCAmelCase      					=  2_1_8_4_1
   else:
      UpperCAmelCase      					=  1_0_0_0
      UpperCAmelCase      					=  """huggingface/label-files"""
      UpperCAmelCase      					=  """imagenet-1k-id2label.json"""
      UpperCAmelCase      					=  json.load(open(hf_hub_download(__snake_case	,    __snake_case	,    repo_type="""dataset""" )	,    """r""" ) )
      UpperCAmelCase      					=  {int(__snake_case ): v for k, v in idalabel.items()}
      UpperCAmelCase      					=  idalabel
      UpperCAmelCase      					=  {v: k for k, v in idalabel.items()}

   UpperCAmelCase      					=  img_size
   UpperCAmelCase      					=  num_classes
   UpperCAmelCase      					=  embed_dim
   UpperCAmelCase      					=  depths
   UpperCAmelCase      					=  num_heads
   UpperCAmelCase      					=  window_size

   return config
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Tuple:
   if "patch_embed.proj" in name:
      UpperCAmelCase      					=  name.replace("""patch_embed.proj"""	,    """embeddings.patch_embeddings.projection""" )
   if "patch_embed.norm" in name:
      UpperCAmelCase      					=  name.replace("""patch_embed.norm"""	,    """embeddings.norm""" )
   if "layers" in name:
      UpperCAmelCase      					=  """encoder.""" + name
   if "attn.proj" in name:
      UpperCAmelCase      					=  name.replace("""attn.proj"""	,    """attention.output.dense""" )
   if "attn" in name:
      UpperCAmelCase      					=  name.replace("""attn"""	,    """attention.self""" )
   if "norm1" in name:
      UpperCAmelCase      					=  name.replace("""norm1"""	,    """layernorm_before""" )
   if "norm2" in name:
      UpperCAmelCase      					=  name.replace("""norm2"""	,    """layernorm_after""" )
   if "mlp.fc1" in name:
      UpperCAmelCase      					=  name.replace("""mlp.fc1"""	,    """intermediate.dense""" )
   if "mlp.fc2" in name:
      UpperCAmelCase      					=  name.replace("""mlp.fc2"""	,    """output.dense""" )

   if name == "norm.weight":
      UpperCAmelCase      					=  """layernorm.weight"""
   if name == "norm.bias":
      UpperCAmelCase      					=  """layernorm.bias"""

   if "head" in name:
      UpperCAmelCase      					=  name.replace("""head"""	,    """classifier""" )
   else:
      UpperCAmelCase      					=  """swin.""" + name

   return name
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[Any]:
   for key in orig_state_dict.copy().keys():
      UpperCAmelCase      					=  orig_state_dict.pop(__snake_case )

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

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

   return orig_state_dict
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Union[str, Any]:
   UpperCAmelCase      					=  timm.create_model(__snake_case	,    pretrained=__snake_case )
   timm_model.eval()

   UpperCAmelCase      					=  get_swin_config(__snake_case )
   UpperCAmelCase      					=  SwinForImageClassification(__snake_case )
   model.eval()

   UpperCAmelCase      					=  convert_state_dict(timm_model.state_dict()	,    __snake_case )
   model.load_state_dict(__snake_case )

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

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

   UpperCAmelCase      					=  timm_model(inputs["""pixel_values"""] )
   UpperCAmelCase      					=  model(**__snake_case ).logits

   assert torch.allclose(__snake_case	,    __snake_case	,    atol=1e-3 )

   print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}""" )
   model.save_pretrained(__snake_case )

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


if __name__ == "__main__":
 __a	 =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--swin_name""",
     default="""swin_tiny_patch4_window7_224""",
     type=str,
     help="""Name of the Swin 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	 =      parser.parse_args()
 convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)











import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase      :       int    =						logging.get_logger(__name__)





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       r"\w+[.]\d+"
     __lowerCAmelCase             =       re.findall(__snake_case       ,			__snake_case							)
     for pat in pats:
          __lowerCAmelCase             =       key.replace(__snake_case       ,			"_".join(pat.split("."							)							)							)
     return key





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("scale",)
     if (
         any("norm" in str_ for str_ in pt_tuple_key							)
         and (pt_tuple_key[-1] == "bias")
         and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
         and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
     ):
          __lowerCAmelCase             =       pt_tuple_key[:-1] + ("scale",)
          return renamed_pt_tuple_key, pt_tensor
     elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
          __lowerCAmelCase             =       pt_tuple_key[:-1] + ("scale",)
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
          __lowerCAmelCase             =       pt_tuple_key[:-1] + ("embedding",)
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("kernel",)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
          __lowerCAmelCase             =       pt_tensor.transpose(2       ,			3       ,			1       ,			0							)
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("kernel",)
     if pt_tuple_key[-1] == "weight":
          __lowerCAmelCase             =       pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("weight",)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm bias
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("bias",)
     if pt_tuple_key[-1] == "beta":
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case=42							):
     # Step 1: Convert pytorch tensor to numpy
     __lowerCAmelCase             =       {k: v.numpy() for k, v in pt_state_dict.items()}

     # Step 2: Since the model is stateless, get random Flax params
     __lowerCAmelCase             =       flax_model.init_weights(PRNGKey(__snake_case							)							)

     __lowerCAmelCase             =       flatten_dict(__snake_case							)
     __lowerCAmelCase             =       {}

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          __lowerCAmelCase             =       rename_key(__snake_case							)
          __lowerCAmelCase             =       tuple(renamed_pt_key.split("."							)							)

          # Correctly rename weight parameters
          __lowerCAmelCase      ,			__lowerCAmelCase             =       rename_key_and_reshape_tensor(__snake_case       ,			__snake_case       ,			__snake_case							)

          if flax_key in random_flax_state_dict:
               if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                        F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""							)

        # also add unexpected weight so that warning is thrown
          __lowerCAmelCase             =       jnp.asarray(__snake_case							)

     return unflatten_dict(__snake_case							)






