






'''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        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  )    -> Dict:







   """simple docstring"""
   __snake_case				:      int     							= SwinConfig()
   __snake_case				:      List[str]     							= swin_name.split('_'  )

   __snake_case				:      Any     							= name_split[1]
   __snake_case				:      List[str]     							= int(name_split[4]  )
   __snake_case				:      Optional[Any]     							= int(name_split[3][-1]  )

   if model_size == "tiny":
      __snake_case				:      Dict     							= 96
      __snake_case				:      Optional[int]     							= (2, 2, 6, 2)
      __snake_case				:      Any     							= (3, 6, 12, 24)
   elif model_size == "small":
      __snake_case				:      List[str]     							= 96
      __snake_case				:      List[Any]     							= (2, 2, 18, 2)
      __snake_case				:      Any     							= (3, 6, 12, 24)
   elif model_size == "base":
      __snake_case				:      Tuple     							= 1_28
      __snake_case				:      Tuple     							= (2, 2, 18, 2)
      __snake_case				:      Dict     							= (4, 8, 16, 32)
   else:
      __snake_case				:      Optional[Any]     							= 1_92
      __snake_case				:      Optional[int]     							= (2, 2, 18, 2)
      __snake_case				:      Optional[Any]     							= (6, 12, 24, 48)

   if "in22k" in swin_name:
      __snake_case				:      Dict     							= 2_18_41
   else:
      __snake_case				:      Optional[int]     							= 10_00
      __snake_case				:      Union[str, Any]     							= 'huggingface/label-files'
      __snake_case				:      Tuple     							= 'imagenet-1k-id2label.json'
      __snake_case				:      Optional[int]     							= json.load(open(hf_hub_download(A  ,		A  ,		repo_type='dataset'  )  ,		'r'  )  )
      __snake_case				:      str     							= {int(A  ): v for k, v in idalabel.items()}
      __snake_case				:      Optional[int]     							= idalabel
      __snake_case				:      Union[str, Any]     							= {v: k for k, v in idalabel.items()}

   __snake_case				:      Union[str, Any]     							= img_size
   __snake_case				:      Any     							= num_classes
   __snake_case				:      int     							= embed_dim
   __snake_case				:      Optional[int]     							= depths
   __snake_case				:      Optional[Any]     							= num_heads
   __snake_case				:      List[str]     							= window_size

   return config




def        _SCREAMING_SNAKE_CASE  (				A  :       List[str]  )    -> Union[str, Any]:







   """simple docstring"""
   if "patch_embed.proj" in name:
      __snake_case				:      Optional[Any]     							= name.replace('patch_embed.proj'  ,		'embeddings.patch_embeddings.projection'  )
   if "patch_embed.norm" in name:
      __snake_case				:      Optional[Any]     							= name.replace('patch_embed.norm'  ,		'embeddings.norm'  )
   if "layers" in name:
      __snake_case				:      int     							= 'encoder.' + name
   if "attn.proj" in name:
      __snake_case				:      List[Any]     							= name.replace('attn.proj'  ,		'attention.output.dense'  )
   if "attn" in name:
      __snake_case				:      List[str]     							= name.replace('attn'  ,		'attention.self'  )
   if "norm1" in name:
      __snake_case				:      int     							= name.replace('norm1'  ,		'layernorm_before'  )
   if "norm2" in name:
      __snake_case				:      Union[str, Any]     							= name.replace('norm2'  ,		'layernorm_after'  )
   if "mlp.fc1" in name:
      __snake_case				:      Optional[Any]     							= name.replace('mlp.fc1'  ,		'intermediate.dense'  )
   if "mlp.fc2" in name:
      __snake_case				:      List[str]     							= name.replace('mlp.fc2'  ,		'output.dense'  )

   if name == "norm.weight":
      __snake_case				:      Any     							= 'layernorm.weight'
   if name == "norm.bias":
      __snake_case				:      List[str]     							= 'layernorm.bias'

   if "head" in name:
      __snake_case				:      Union[str, Any]     							= name.replace('head'  ,		'classifier'  )
   else:
      __snake_case				:      Union[str, Any]     							= 'swin.' + name

   return name




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       int  )    -> Optional[Any]:







   """simple docstring"""
   for key in orig_state_dict.copy().keys():
      __snake_case				:      str     							= orig_state_dict.pop(A  )

      if "mask" in key:
         continue
      elif "qkv" in key:
         __snake_case				:      Union[str, Any]     							= key.split('.'  )
         __snake_case				:      Any     							= int(key_split[1]  )
         __snake_case				:      Optional[int]     							= int(key_split[3]  )
         __snake_case				:      Optional[int]     							= model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            __snake_case				:      List[Any]     							= val[:dim, :]
            __snake_case				:      Any     							= val[
                dim : dim * 2, :
            ]
            __snake_case				:      str     							= val[-dim:, :]
         else:
            __snake_case				:      Dict     							= val[
                :dim
            ]
            __snake_case				:      List[str]     							= val[
                dim : dim * 2
            ]
            __snake_case				:      Tuple     							= val[
                -dim:
            ]
      else:
         __snake_case				:      Dict     							= val

   return orig_state_dict




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       List[Any]  )    -> Tuple:







   """simple docstring"""
   __snake_case				:      Dict     							= timm.create_model(A  ,		pretrained=A  )
   timm_model.eval()

   __snake_case				:      Any     							= get_swin_config(A  )
   __snake_case				:      Optional[int]     							= SwinForImageClassification(A  )
   model.eval()

   __snake_case				:      List[Any]     							= convert_state_dict(timm_model.state_dict()  ,		A  )
   model.load_state_dict(A  )

   __snake_case				:      Optional[Any]     							= 'http://images.cocodataset.org/val2017/000000039769.jpg'

   __snake_case				:      int     							= AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'  ,		'-'  )  )  )
   __snake_case				:      Dict     							= Image.open(requests.get(A  ,		stream=A  ).raw  )
   __snake_case				:      str     							= image_processor(images=A  ,		return_tensors='pt'  )

   __snake_case				:      Optional[Any]     							= timm_model(inputs['pixel_values']  )
   __snake_case				:      Optional[int]     							= 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)







'''simple docstring'''






import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
      from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
      from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
      from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


__A    	=   DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

__A    	=   '''main'''
# Default branch name
__A    	=   '''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
__A    	=   '''aaaaaaa'''
# This commit does not exist, so we should 404.

__A    	=   '''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
__A    	=   '''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''




@contextlib.contextmanager
def        _SCREAMING_SNAKE_CASE  (				)    -> List[Any]:







   """simple docstring"""
   print('Welcome!'  )
   yield
   print('Bye!'  )




@contextlib.contextmanager
def        _SCREAMING_SNAKE_CASE  (				)    -> Optional[Any]:







   """simple docstring"""
   print('Bonjour!'  )
   yield
   print('Au revoir!'  )




class a_    (    unittest.TestCase							):







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      assert transformers.__spec__ is not None
      assert importlib.util.find_spec('transformers') is not None




class a_    (    unittest.TestCase							):







   @unittest.mock.patch('sys.stdout'					,   new_callable=io.StringIO)
   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      int:
      """simple docstring"""






      with ContextManagers([]):
         print('Transformers are awesome!')
      # The print statement adds a new line at the end of the output
      self.assertEqual(mock_stdout.getvalue()					,   'Transformers are awesome!\n')







   @unittest.mock.patch('sys.stdout'					,   new_callable=io.StringIO)
   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      List[str]:
      """simple docstring"""






      with ContextManagers([context_en()]):
         print('Transformers are awesome!')
      # The output should be wrapped with an English welcome and goodbye
      self.assertEqual(mock_stdout.getvalue()					,   'Welcome!\nTransformers are awesome!\nBye!\n')







   @unittest.mock.patch('sys.stdout'					,   new_callable=io.StringIO)
   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      Tuple:
      """simple docstring"""






      with ContextManagers([context_fr(), context_en()]):
         print('Transformers are awesome!')
      # The output should be wrapped with an English and French welcome and goodbye
      self.assertEqual(mock_stdout.getvalue()					,   'Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n')







   @require_torch
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      self.assertEqual(find_labels(__a)					,   ['labels'])
      self.assertEqual(find_labels(__a)					,   ['labels', 'next_sentence_label'])
      self.assertEqual(find_labels(__a)					,   ['start_positions', 'end_positions'])




      class a_    (    UpperCamelCase_							):
         pass


      self.assertEqual(find_labels(__a)					,   ['labels'])







   @require_tf
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      self.assertEqual(find_labels(__a)					,   ['labels'])
      self.assertEqual(find_labels(__a)					,   ['labels', 'next_sentence_label'])
      self.assertEqual(find_labels(__a)					,   ['start_positions', 'end_positions'])




      class a_    (    UpperCamelCase_							):
         pass


      self.assertEqual(find_labels(__a)					,   ['labels'])







   @require_flax
   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      self.assertEqual(find_labels(__a)					,   [])
      self.assertEqual(find_labels(__a)					,   [])
      self.assertEqual(find_labels(__a)					,   [])




      class a_    (    UpperCamelCase_							):
         pass


      self.assertEqual(find_labels(__a)					,   [])