






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def 	UpperCamelCase	(							snake_case__  : List[Any]	):




     '''simple docstring'''






     __snake_case      :Dict 							=    SwinConfig(image_size=192	)

     if "base" in model_name:
          __snake_case      :str 							=    6
          __snake_case      :List[Any] 							=    128
          __snake_case      :Dict 							=    (2, 2, 18, 2)
          __snake_case      :Tuple 							=    (4, 8, 16, 32)
     elif "large" in model_name:
          __snake_case      :List[str] 							=    12
          __snake_case      :Any 							=    192
          __snake_case      :Dict 							=    (2, 2, 18, 2)
          __snake_case      :int 							=    (6, 12, 24, 48)
     else:
          raise ValueError("""Model not supported, only supports base and large variants"""	)

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

     return config






def 	UpperCamelCase	(							snake_case__  : Dict	):




     '''simple docstring'''






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

     if name == "encoder.norm.weight":
          __snake_case      :Optional[int] 							=    """layernorm.weight"""
     if name == "encoder.norm.bias":
          __snake_case      :List[str] 							=    """layernorm.bias"""

     if "decoder" in name:
          pass
     else:
          __snake_case      :Union[str, Any] 							=    """swin.""" + name

     return name






def 	UpperCamelCase	(							snake_case__  : int						,snake_case__  : Optional[int]	):




     '''simple docstring'''






     for key in orig_state_dict.copy().keys():
          __snake_case      :Any 							=    orig_state_dict.pop(snake_case__	)

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

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

     return orig_state_dict






def 	UpperCamelCase	(							snake_case__  : int						,snake_case__  : Optional[Any]						,snake_case__  : Optional[int]						,snake_case__  : Optional[Any]	):




     '''simple docstring'''






     __snake_case      :Tuple 							=    torch.load(snake_case__						,map_location="""cpu"""	)["""model"""]

     __snake_case      :Dict 							=    get_swin_config(snake_case__	)
     __snake_case      :Any 							=    SwinForMaskedImageModeling(snake_case__	)
     model.eval()

     __snake_case      :List[str] 							=    convert_state_dict(snake_case__						,snake_case__	)
     model.load_state_dict(snake_case__	)

     __snake_case      :List[str] 							=    """http://images.cocodataset.org/val2017/000000039769.jpg"""

     __snake_case      :Optional[int] 							=    ViTImageProcessor(size={"""height""": 192, """width""": 192}	)
     __snake_case      :List[str] 							=    Image.open(requests.get(snake_case__						,stream=snake_case__	).raw	)
     __snake_case      :Optional[Any] 							=    image_processor(images=snake_case__						,return_tensors="""pt"""	)

     with torch.no_grad():
          __snake_case      :List[str] 							=    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__       		=  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__       		=  parser.parse_args()
     convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)










import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate




@dataclass(frozen=lowercase_)
class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					str								=			field(default="audio-classification"						,     metadata={"include_in_asdict_even_if_is_default": True})
       lowerCamelCase				:					ClassVar[Features]								=			Features({"audio": Audio()})
       lowerCamelCase				:					ClassVar[Features]								=			Features({"labels": ClassLabel})
       lowerCamelCase				:					str								=			"audio"
       lowerCamelCase				:					str								=			"labels"



       def        __lowercase    (				self      ,    a__     )			->   Dict:






            '''simple docstring'''





            if self.label_column not in features:
                 raise ValueError(F'''Column {self.label_column} is not present in features.'''     )
            if not isinstance(features[self.label_column]      ,    a__     ):
                 raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''     )
            __snake_case      :Any 							=    copy.deepcopy(self     )
            __snake_case      :List[Any] 							=    self.label_schema.copy()
            __snake_case      :List[Any] 							=    features[self.label_column]
            __snake_case      :Optional[Any] 							=    label_schema
            return task_template



       @property
       def        __lowercase    (				self     )			->   Dict[str, str]:






            '''simple docstring'''





            return {
                self.audio_column: "audio",
                self.label_column: "labels",
            }


