'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def 	UpperCamelCase_		(   snake_case_			:			Tuple )   ->	Tuple:






 '''simple docstring'''

 __lowerCAmelCase											=		SwinConfig(image_size=1_92 )

 if "base" in model_name:
  __lowerCAmelCase											=		6
  __lowerCAmelCase											=		1_28
  __lowerCAmelCase											=		(2, 2, 18, 2)
  __lowerCAmelCase											=		(4, 8, 16, 32)
 elif "large" in model_name:
  __lowerCAmelCase											=		12
  __lowerCAmelCase											=		1_92
  __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 	UpperCamelCase_		(   snake_case_			:			List[str] )   ->	List[Any]:






 '''simple docstring'''

 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 	UpperCamelCase_		(   snake_case_			:			Union[str, Any]		,						snake_case_			:			Optional[int] )   ->	Optional[Any]:






 '''simple docstring'''

 for key in orig_state_dict.copy().keys():
  __lowerCAmelCase											=		orig_state_dict.pop(_UpperCamelCase )

  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 	UpperCamelCase_		(   snake_case_			:			Any		,						snake_case_			:			str		,						snake_case_			:			Union[str, Any]		,						snake_case_			:			Optional[int] )   ->	Any:






 '''simple docstring'''

 __lowerCAmelCase											=		torch.load(_UpperCamelCase		,						map_location="""cpu""" )["""model"""]

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

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

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

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

 with torch.no_grad():
  __lowerCAmelCase											=		model(**_UpperCamelCase ).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(_UpperCamelCase )

  print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
  image_processor.save_pretrained(_UpperCamelCase )

 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__":
 _A     : 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.'''
 )

 _A     : List[str]    =							parser.parse_args()
 convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_rembert import RemBertTokenizer
else:
      a_     :    int     		=   None

a_     :    List[Any]     		=   logging.get_logger(__name__)
a_     :    List[Any]     		=   {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

a_     :    Optional[int]     		=   {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

a_     :    Union[str, Any]     		=   {
    "google/rembert": 256,
}

a_     :    List[Any]     		=   "▁"




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =VOCAB_FILES_NAMES
    __UpperCamelCase    =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase    =RemBertTokenizer

    def __init__(      self			:   int				,     snake_case__			:   Any=None				,     snake_case__			:   Optional[Any]=None				,     snake_case__			:   Optional[int]=True				,     snake_case__			:   Any=True				,     snake_case__			:   Any=False				,     snake_case__			:   Dict="[CLS]"				,     snake_case__			:   List[str]="[SEP]"				,     snake_case__			:   List[str]="<unk>"				,     snake_case__			:   Union[str, Any]="[SEP]"				,     snake_case__			:   List[Any]="<pad>"				,     snake_case__			:   Tuple="[CLS]"				,     snake_case__			:   str="[MASK]"				,     **snake_case__			:   Union[str, Any]				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      AddedToken(snake_case__				,     lstrip=snake_case__				,     rstrip=snake_case__     ) if isinstance(snake_case__				,     snake_case__     ) else mask_token

     super().__init__(
         snake_case__				,     tokenizer_file=snake_case__				,     do_lower_case=snake_case__				,     remove_space=snake_case__				,     keep_accents=snake_case__				,     bos_token=snake_case__				,     eos_token=snake_case__				,     unk_token=snake_case__				,     sep_token=snake_case__				,     pad_token=snake_case__				,     cls_token=snake_case__				,     mask_token=snake_case__				,     **snake_case__				,     )

     SCREAMING_SNAKE_CASE		    =      do_lower_case
     SCREAMING_SNAKE_CASE		    =      remove_space
     SCREAMING_SNAKE_CASE		    =      keep_accents
     SCREAMING_SNAKE_CASE		    =      vocab_file
     SCREAMING_SNAKE_CASE		    =      False if not self.vocab_file else True

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      [self.sep_token_id]
     SCREAMING_SNAKE_CASE		    =      [self.cls_token_id]
     if token_ids_a is None:
      return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None				,     snake_case__			:   bool = False     ):

     """simple docstring"""






     if already_has_special_tokens:
      if token_ids_a is not None:
       raise ValueError(
           'You should not supply a second sequence if the provided sequence of '
           'ids is already formatted with special tokens for the model.'     )
      return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

     if token_ids_a is not None:
      return [1] + ([0] * len(snake_case__     )) + [1] + ([0] * len(snake_case__     )) + [1]
     return [1] + ([0] * len(snake_case__     )) + [1]

    def UpperCamelCase      (      self			:   int				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      [self.sep_token_id]
     SCREAMING_SNAKE_CASE		    =      [self.cls_token_id]

     if token_ids_a is None:
      return len(cls + token_ids_a + sep     ) * [0]
     return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]




    def UpperCamelCase      (      self			:   Dict				,     snake_case__			:   str				,     snake_case__			:   Optional[str] = None     ):

     """simple docstring"""






     if not os.path.isdir(snake_case__     ):
      logger.error('Vocabulary path ({}) should be a directory'.format(snake_case__     )     )
      return
     SCREAMING_SNAKE_CASE		    =      os.path.join(
         snake_case__				,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

     if os.path.abspath(self.vocab_file     ) != os.path.abspath(snake_case__     ):
      copyfile(self.vocab_file				,     snake_case__     )

     return (out_vocab_file,)

