



'''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   _lowerCAmelCase   (						A__	):
      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__     =  21_841
      else:
            lowercase__     =  1_000
            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   _lowerCAmelCase   (						A__	):


      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   _lowerCAmelCase   (						A__						,  A__	):


      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   _lowerCAmelCase   (						A__						,  A__	):
      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__      :				Union[str, Any]   	=			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__      :				Optional[int]   	=			parser.parse_args()
       convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin



class       UpperCAmelCase__(							unittest.TestCase     ,							lowerCamelCase	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			List[str])	->				Any:




         """simple docstring"""

         lowercase__     =  load_tool('text-classification')
         self.tool.setup()
         lowercase__     =  load_tool('text-classification'			,				remote=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.tool('That\'s quite cool'			,				['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')


   def 			UpperCAmelCase		(						self  :			int)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.remote_tool('That\'s quite cool'			,				['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  self.tool(text='That\'s quite cool'			,				labels=['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')



   def 			UpperCAmelCase		(						self  :			Any)	->				Any:




         """simple docstring"""

         lowercase__     =  self.remote_tool(text='That\'s quite cool'			,				labels=['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')
