


'''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_ ( __UpperCamelCase					:       Optional[Any]		)					->	int:







     """simple docstring"""
     _A				=							SwinConfig()
     _A				=							swin_name.split('_'		)

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

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

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

     _A				=							img_size
     _A				=							num_classes
     _A				=							embed_dim
     _A				=							depths
     _A				=							num_heads
     _A				=							window_size

     return config

def 					lowerCamelCase_ ( __UpperCamelCase					:       Dict		)					->	Union[str, Any]:







     """simple docstring"""
     if "patch_embed.proj" in name:
          _A				=							name.replace('patch_embed.proj'							,    'embeddings.patch_embeddings.projection'		)
     if "patch_embed.norm" in name:
          _A				=							name.replace('patch_embed.norm'							,    'embeddings.norm'		)
     if "layers" in name:
          _A				=							'encoder.' + name
     if "attn.proj" in name:
          _A				=							name.replace('attn.proj'							,    'attention.output.dense'		)
     if "attn" in name:
          _A				=							name.replace('attn'							,    'attention.self'		)
     if "norm1" in name:
          _A				=							name.replace('norm1'							,    'layernorm_before'		)
     if "norm2" in name:
          _A				=							name.replace('norm2'							,    'layernorm_after'		)
     if "mlp.fc1" in name:
          _A				=							name.replace('mlp.fc1'							,    'intermediate.dense'		)
     if "mlp.fc2" in name:
          _A				=							name.replace('mlp.fc2'							,    'output.dense'		)

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

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

     return name

def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[Any]							,    __UpperCamelCase					:       List[str]		)					->	Tuple:







     """simple docstring"""
     for key in orig_state_dict.copy().keys():
          _A				=							orig_state_dict.pop(__UpperCamelCase		)

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

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

     return orig_state_dict

def 					lowerCamelCase_ ( __UpperCamelCase					:       int							,    __UpperCamelCase					:       Any		)					->	List[str]:







     """simple docstring"""
     _A				=							timm.create_model(__UpperCamelCase							,    pretrained=__UpperCamelCase		)
     timm_model.eval()

     _A				=							get_swin_config(__UpperCamelCase		)
     _A				=							SwinForImageClassification(__UpperCamelCase		)
     model.eval()

     _A				=							convert_state_dict(timm_model.state_dict()							,    __UpperCamelCase		)
     model.load_state_dict(__UpperCamelCase		)

     _A				=							'http://images.cocodataset.org/val2017/000000039769.jpg'

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

     _A				=							timm_model(inputs['pixel_values']		)
     _A				=							model(**__UpperCamelCase		).logits

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

     print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'		)
     model.save_pretrained(__UpperCamelCase		)

     print(F'Saving image processor to {pytorch_dump_folder_path}'		)
     image_processor.save_pretrained(__UpperCamelCase		)


if __name__ == "__main__":
    lowerCAmelCase						=					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."""
    )

    lowerCAmelCase						=					parser.parse_args()
    convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)









'''simple docstring'''
class        lowerCAmelCase_  :






       def __init__( self			,     _UpperCamelCase   )->      None:
            _A				=							size
            _A				=							[0] * size
            _A				=							[0] * size






       @staticmethod
       def   UpperCamelCase						( _UpperCamelCase   )->      int:
            return index | (index + 1)






       @staticmethod
       def   UpperCamelCase						( _UpperCamelCase   )->      int:
            return (index & (index + 1)) - 1






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      None:
            _A				=							value
            while index < self.size:
                 _A				=							self.get_prev(_UpperCamelCase   ) + 1
                 if current_left_border == index:
                      _A				=							value
                 else:
                      _A				=							max(_UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )
                 _A				=							self.get_next(_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      int:
            right -= 1  # Because of right is exclusive
            _A				=							0
            while left <= right:
                 _A				=							self.get_prev(_UpperCamelCase   )
                 if left <= current_left:
                      _A				=							max(_UpperCamelCase			,     self.tree[right]   )
                      _A				=							current_left
                 else:
                      _A				=							max(_UpperCamelCase			,     self.arr[right]   )
                      right -= 1
            return result


if __name__ == "__main__":
    import doctest

    doctest.testmod()





