






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       SwinConfig(image_size=192							)

     if "base" in model_name:
          __lowerCAmelCase             =       6
          __lowerCAmelCase             =       128
          __lowerCAmelCase             =       (2, 2, 18, 2)
          __lowerCAmelCase             =       (4, 8, 16, 32)
     elif "large" in model_name:
          __lowerCAmelCase             =       12
          __lowerCAmelCase             =       192
          __lowerCAmelCase             =       (2, 2, 18, 2)
          __lowerCAmelCase             =       (6, 12, 24, 48)
     else:
          raise ValueError("Model not supported, only supports base and large variants"							)

     __lowerCAmelCase             =       window_size
     __lowerCAmelCase             =       embed_dim
     __lowerCAmelCase             =       depths
     __lowerCAmelCase             =       num_heads

     return config





def    __lowerCAmelCase   (    __snake_case							):
     if "encoder.mask_token" in name:
          __lowerCAmelCase             =       name.replace("encoder.mask_token"       ,			"embeddings.mask_token"							)
     if "encoder.patch_embed.proj" in name:
          __lowerCAmelCase             =       name.replace("encoder.patch_embed.proj"       ,			"embeddings.patch_embeddings.projection"							)
     if "encoder.patch_embed.norm" in name:
          __lowerCAmelCase             =       name.replace("encoder.patch_embed.norm"       ,			"embeddings.norm"							)
     if "attn.proj" in name:
          __lowerCAmelCase             =       name.replace("attn.proj"       ,			"attention.output.dense"							)
     if "attn" in name:
          __lowerCAmelCase             =       name.replace("attn"       ,			"attention.self"							)
     if "norm1" in name:
          __lowerCAmelCase             =       name.replace("norm1"       ,			"layernorm_before"							)
     if "norm2" in name:
          __lowerCAmelCase             =       name.replace("norm2"       ,			"layernorm_after"							)
     if "mlp.fc1" in name:
          __lowerCAmelCase             =       name.replace("mlp.fc1"       ,			"intermediate.dense"							)
     if "mlp.fc2" in name:
          __lowerCAmelCase             =       name.replace("mlp.fc2"       ,			"output.dense"							)

     if name == "encoder.norm.weight":
          __lowerCAmelCase             =       "layernorm.weight"
     if name == "encoder.norm.bias":
          __lowerCAmelCase             =       "layernorm.bias"

     if "decoder" in name:
          pass
     else:
          __lowerCAmelCase             =       "swin." + name

     return name





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     for key in orig_state_dict.copy().keys():
          __lowerCAmelCase             =       orig_state_dict.pop(__snake_case							)

          if "attn_mask" in key:
               pass
          elif "qkv" in key:
               __lowerCAmelCase             =       key.split("."							)
               __lowerCAmelCase             =       int(key_split[2]							)
               __lowerCAmelCase             =       int(key_split[4]							)
               __lowerCAmelCase             =       model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

     return orig_state_dict





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       torch.load(__snake_case       ,			map_location="cpu"							)["model"]

     __lowerCAmelCase             =       get_swin_config(__snake_case							)
     __lowerCAmelCase             =       SwinForMaskedImageModeling(__snake_case							)
     model.eval()

     __lowerCAmelCase             =       convert_state_dict(__snake_case       ,			__snake_case							)
     model.load_state_dict(__snake_case							)

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

     __lowerCAmelCase             =       ViTImageProcessor(size={"height": 192, "width": 192}							)
     __lowerCAmelCase             =       Image.open(requests.get(__snake_case       ,			stream=__snake_case							).raw							)
     __lowerCAmelCase             =       image_processor(images=__snake_case       ,			return_tensors="pt"							)

     with torch.no_grad():
          __lowerCAmelCase             =       model(**__snake_case							).logits

     print(outputs.keys()							)
     print("Looks ok!"							)

     if pytorch_dump_folder_path is not None:
          print(F"""Saving model {model_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 push_to_hub:
          print(F"""Pushing model and image processor for {model_name} to hub"""							)
          model.push_to_hub(F"""microsoft/{model_name}"""							)
          image_processor.push_to_hub(F"""microsoft/{model_name}"""							)


if __name__ == "__main__":
 lowerCamelCase      :       int    =						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--model_name''',
     default='''swin-base-simmim-window6-192''',
     type=str,
     choices=['''swin-base-simmim-window6-192''', '''swin-large-simmim-window12-192'''],
     help='''Name of the Swin SimMIM model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--checkpoint_path''',
     default='''/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth''',
     type=str,
     help='''Path to the original PyTorch checkpoint (.pth file).''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
 )

 lowerCamelCase      :       Union[str, Any]    =						parser.parse_args()
 convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)














from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase      :       List[Any]    =						{
    '''configuration_owlvit''': [
        '''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''OwlViTConfig''',
        '''OwlViTOnnxConfig''',
        '''OwlViTTextConfig''',
        '''OwlViTVisionConfig''',
    ],
    '''processing_owlvit''': ['''OwlViTProcessor'''],
}


try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase      :       List[Any]    =						['''OwlViTFeatureExtractor''']
 lowerCamelCase      :       str    =						['''OwlViTImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase      :       List[str]    =						[
     '''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''OwlViTModel''',
     '''OwlViTPreTrainedModel''',
     '''OwlViTTextModel''',
     '''OwlViTVisionModel''',
     '''OwlViTForObjectDetection''',
 ]

if TYPE_CHECKING:
 from .configuration_owlvit import (
     OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     OwlViTConfig,
     OwlViTOnnxConfig,
     OwlViTTextConfig,
     OwlViTVisionConfig,
 )
 from .processing_owlvit import OwlViTProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_owlvit import OwlViTFeatureExtractor
  from .image_processing_owlvit import OwlViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_owlvit import (
      OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      OwlViTForObjectDetection,
      OwlViTModel,
      OwlViTPreTrainedModel,
      OwlViTTextModel,
      OwlViTVisionModel,
  )

else:
 import sys

 lowerCamelCase      :       List[str]    =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






