

"""simple docstring"""





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   _A  (  UpperCamelCase_     :       Tuple)	->						int:
   '''simple docstring'''


   __lowercase								=						SwinConfig()
   __lowercase								=						swin_name.split("_")

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

   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 "in22k" in swin_name:
      __lowercase								=						21841
   else:
      __lowercase								=						1000
      __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   _A  (  UpperCamelCase_     :       Dict)	->						int:
   '''simple docstring'''


   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 name == "norm.weight":
      __lowercase								=						'''layernorm.weight'''
   if name == "norm.bias":
      __lowercase								=						'''layernorm.bias'''

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

   return name




def   _A  (  UpperCamelCase_     :       Dict,			UpperCamelCase_     :       Tuple)	->						Any:
   '''simple docstring'''


   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.swin.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   _A  (  UpperCamelCase_     :       Dict,			UpperCamelCase_     :       List[str])	->						str:
   '''simple docstring'''


   __lowercase								=						timm.create_model(__a,			pretrained=__a)
   timm_model.eval()

   __lowercase								=						get_swin_config(__a)
   __lowercase								=						SwinForImageClassification(__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(swin_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 {swin_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)


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


a__				=		logging.get_logger(__name__)



def    __UpperCAmelCase (	__a      :			Dict	)			->     Tuple:

					"""simple docstring"""

					_a	:    Any         =		R'''\w+[.]\d+'''
					_a	:    Union[str, Any]         =		re.findall(__a		,__a	)
					for pat in pats:
										_a	:    int         =		key.replace(__a		,'''_'''.join(pat.split('''.'''	)	)	)
					return key



def    __UpperCAmelCase (	__a      :			List[str]		,__a      :			Union[str, Any]		,__a      :			Optional[int]	)			->     Tuple:

					"""simple docstring"""

					_a	:    Dict         =		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)
					):
										_a	:    Dict         =		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:
										_a	:    Optional[int]         =		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:
										_a	:    Union[str, Any]         =		pt_tuple_key[:-1] + ('''embedding''',)
										return renamed_pt_tuple_key, pt_tensor

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

					# linear layer
					_a	:    Dict         =		pt_tuple_key[:-1] + ('''kernel''',)
					if pt_tuple_key[-1] == "weight":
										_a	:    Union[str, Any]         =		pt_tensor.T
										return renamed_pt_tuple_key, pt_tensor

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

					# old PyTorch layer norm bias
					_a	:    Union[str, Any]         =		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    __UpperCAmelCase (	__a      :			Dict		,__a      :			str		,__a      :			str=42	)			->     Optional[int]:

					"""simple docstring"""

					_a	:    Tuple         =		{k: v.numpy() for k, v in pt_state_dict.items()}

					# Step 2: Since the model is stateless, get random Flax params
					_a	:    List[Any]         =		flax_model.init_weights(PRNGKey(__a	)	)

					_a	:    Optional[int]         =		flatten_dict(__a	)
					_a	:    Tuple         =		{}

					# Need to change some parameters name to match Flax names
					for pt_key, pt_tensor in pt_state_dict.items():
										_a	:    List[str]         =		rename_key(__a	)
										_a	:    Optional[Any]         =		tuple(renamed_pt_key.split('''.'''	)	)

										# Correctly rename weight parameters
										_a      ,					_a	:    List[str]         =		rename_key_and_reshape_tensor(__a		,__a		,__a	)

										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
										_a	:    Dict         =		jnp.asarray(__a	)

					return unflatten_dict(__a	)



