


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		Union[str, Any]:

       """simple docstring"""

       a_					:		Dict							=  SwinConfig(image_size=1_92				)

       if "base" in model_name:
              a_					:		List[str]							=  6
              a_					:		int							=  1_28
              a_					:		Tuple							=  (2, 2, 18, 2)
              a_					:		Optional[int]							=  (4, 8, 16, 32)
       elif "large" in model_name:
              a_					:		List[str]							=  12
              a_					:		Union[str, Any]							=  1_92
              a_					:		Union[str, Any]							=  (2, 2, 18, 2)
              a_					:		str							=  (6, 12, 24, 48)
       else:
              raise ValueError('Model not supported, only supports base and large variants'				)

       a_					:		List[str]							=  window_size
       a_					:		Any							=  embed_dim
       a_					:		Optional[int]							=  depths
       a_					:		List[Any]							=  num_heads

       return config







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		Optional[int]:

       """simple docstring"""

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

       if name == "encoder.norm.weight":
              a_					:		Optional[int]							=  'layernorm.weight'
       if name == "encoder.norm.bias":
              a_					:		Union[str, Any]							=  'layernorm.bias'

       if "decoder" in name:
              pass
       else:
              a_					:		List[str]							=  'swin.' + name

       return name







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[Any]  ,    __A :			Tuple				)	->		List[str]:

       """simple docstring"""

       for key in orig_state_dict.copy().keys():
              a_					:		List[str]							=  orig_state_dict.pop(__A				)

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

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

       return orig_state_dict







def        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			Any  ,    __A :			str  ,    __A :			Optional[int]				)	->		Union[str, Any]:

       """simple docstring"""

       a_					:		str							=  torch.load(__A  ,    map_location='cpu'				)['model']

       a_					:		Union[str, Any]							=  get_swin_config(__A				)
       a_					:		List[str]							=  SwinForMaskedImageModeling(__A				)
       model.eval()

       a_					:		Dict							=  convert_state_dict(__A  ,    __A				)
       model.load_state_dict(__A				)

       a_					:		List[Any]							=  'http://images.cocodataset.org/val2017/000000039769.jpg'

       a_					:		List[str]							=  ViTImageProcessor(size={'height': 1_92, 'width': 1_92}				)
       a_					:		Optional[Any]							=  Image.open(requests.get(__A  ,    stream=__A				).raw				)
       a_					:		List[str]							=  image_processor(images=__A  ,    return_tensors='pt'				)

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






import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		str:

       """simple docstring"""

       a_					:		Tuple							=  []
       for line in lines:
              a_					:		Any							=  re.sub(R'#.*'  ,    ''  ,    __A				)  # remove comments
              if line:
                     filtered_lines.append(__A				)
       a_					:		Tuple							=  '\n'.join(__A				)

       # Make a hash from all this code
       a_					:		Tuple							=  full_str.encode('utf-8'				)
       return shaaaa(__A				).hexdigest()


# get importable module names and hash for caching
UpperCAmelCase_      : List[Any]  					=       {
    'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
UpperCAmelCase_      : Dict  					=       {
    '.csv': ('csv', {}),
    '.tsv': ('csv', {'sep': '\t'}),
    '.json': ('json', {}),
    '.jsonl': ('json', {}),
    '.parquet': ('parquet', {}),
    '.arrow': ('arrow', {}),
    '.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCAmelCase_      : Optional[int]  					=       {'imagefolder', 'audiofolder'}

# Used to filter data files based on extensions given a module name
UpperCAmelCase_      : Dict[str, List[str]]  					=       {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
   _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')


