

'''simple docstring'''



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      SwinConfig(image_size=1_92						)

     if "base" in model_name:
          __SCREAMING_SNAKE_CASE     =      6
          __SCREAMING_SNAKE_CASE     =      1_28
          __SCREAMING_SNAKE_CASE     =      (2, 2, 18, 2)
          __SCREAMING_SNAKE_CASE     =      (4, 8, 16, 32)
     elif "large" in model_name:
          __SCREAMING_SNAKE_CASE     =      12
          __SCREAMING_SNAKE_CASE     =      1_92
          __SCREAMING_SNAKE_CASE     =      (2, 2, 18, 2)
          __SCREAMING_SNAKE_CASE     =      (6, 12, 24, 48)
     else:
          raise ValueError("""Model not supported, only supports base and large variants"""						)

     __SCREAMING_SNAKE_CASE     =      window_size
     __SCREAMING_SNAKE_CASE     =      embed_dim
     __SCREAMING_SNAKE_CASE     =      depths
     __SCREAMING_SNAKE_CASE     =      num_heads

     return config







def    a__       (  a__						):




     """simple docstring"""


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

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

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

     return name







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     for key in orig_state_dict.copy().keys():
          __SCREAMING_SNAKE_CASE     =      orig_state_dict.pop(a__						)

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

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

     return orig_state_dict







def    a__       (  a__      ,    a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      torch.load(a__      ,    map_location="""cpu"""						)["""model"""]

     __SCREAMING_SNAKE_CASE     =      get_swin_config(a__						)
     __SCREAMING_SNAKE_CASE     =      SwinForMaskedImageModeling(a__						)
     model.eval()

     __SCREAMING_SNAKE_CASE     =      convert_state_dict(a__      ,    a__						)
     model.load_state_dict(a__						)

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

     __SCREAMING_SNAKE_CASE     =      ViTImageProcessor(size={"""height""": 1_92, """width""": 1_92}						)
     __SCREAMING_SNAKE_CASE     =      Image.open(requests.get(a__      ,    stream=a__						).raw						)
     __SCREAMING_SNAKE_CASE     =      image_processor(images=a__      ,    return_tensors="""pt"""						)

     with torch.no_grad():
          __SCREAMING_SNAKE_CASE     =      model(**a__						).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(a__						)

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

     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__":
  UpperCAmelCase :       Optional[Any]			   =     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.'
  )

  UpperCAmelCase :       Tuple			   =     parser.parse_args()
  convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


UpperCAmelCase :       Any			   =     [
    'EAGER',
    'AOT_EAGER',
    'INDUCTOR',
    'NVFUSER',
    'AOT_NVFUSER',
    'AOT_CUDAGRAPHS',
    'OFI',
    'FX2TRT',
    'ONNXRT',
    'IPEX',
]







def    a__       (  a__      ,    a__=None      ,    a__=None      ,    a__=None						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      True
     while ask_again:
          __SCREAMING_SNAKE_CASE     =      input(a__						)
          try:
               if default is not None and len(a__						) == 0:
                    return default
               return convert_value(a__						) if convert_value is not None else result
          except Exception:
               if error_message is not None:
                    print(a__						)







def    a__       (  a__      ,    a__=[]      ,    a__=None      ,    a__=0						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      BulletMenu(a__      ,    a__						)
     __SCREAMING_SNAKE_CASE     =      menu.run(default_choice=a__						)
     return convert_value(a__						) if convert_value is not None else result







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)
     return ComputeEnvironment(["""LOCAL_MACHINE""", """AMAZON_SAGEMAKER"""][value]						)







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)
     return DistributedType(["""NO""", """MULTI_CPU""", """MULTI_XPU""", """MULTI_GPU""", """MULTI_NPU""", """TPU"""][value]						)







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)
     return DynamoBackend(DYNAMO_BACKENDS[value]						).value







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)
     return PrecisionType(["""no""", """fp16""", """bf16""", """fp8"""][value]						)







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)
     return SageMakerDistributedType(["""NO""", """DATA_PARALLEL""", """MODEL_PARALLEL"""][value]						)







def    a__       (  a__						):




     """simple docstring"""


     return {"yes": True, "no": False}[value.lower()]







class 						lowerCAmelCase__   (      argparse.RawDescriptionHelpFormatter					):



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      super()._format_usage(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      usage.replace("""<command> [<args>] """					,							""""""			)
      return usage






