

"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor




def 					a__						(						lowerCAmelCase    )	-> Dict:
      UpperCAmelCase__			:							Union[str, Any]							=    SwinConfig(image_size=1_92    )

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

      UpperCAmelCase__			:							Tuple							=    window_size
      UpperCAmelCase__			:							int							=    embed_dim
      UpperCAmelCase__			:							List[str]							=    depths
      UpperCAmelCase__			:							str							=    num_heads

      return config




def 					a__						(						lowerCAmelCase    )	-> str:
      if "encoder.mask_token" in name:
            UpperCAmelCase__			:							Optional[Any]							=    name.replace("""encoder.mask_token"""			,	"""embeddings.mask_token"""    )
      if "encoder.patch_embed.proj" in name:
            UpperCAmelCase__			:							str							=    name.replace("""encoder.patch_embed.proj"""			,	"""embeddings.patch_embeddings.projection"""    )
      if "encoder.patch_embed.norm" in name:
            UpperCAmelCase__			:							str							=    name.replace("""encoder.patch_embed.norm"""			,	"""embeddings.norm"""    )
      if "attn.proj" in name:
            UpperCAmelCase__			:							Union[str, Any]							=    name.replace("""attn.proj"""			,	"""attention.output.dense"""    )
      if "attn" in name:
            UpperCAmelCase__			:							List[str]							=    name.replace("""attn"""			,	"""attention.self"""    )
      if "norm1" in name:
            UpperCAmelCase__			:							List[str]							=    name.replace("""norm1"""			,	"""layernorm_before"""    )
      if "norm2" in name:
            UpperCAmelCase__			:							Optional[Any]							=    name.replace("""norm2"""			,	"""layernorm_after"""    )
      if "mlp.fc1" in name:
            UpperCAmelCase__			:							List[Any]							=    name.replace("""mlp.fc1"""			,	"""intermediate.dense"""    )
      if "mlp.fc2" in name:
            UpperCAmelCase__			:							Any							=    name.replace("""mlp.fc2"""			,	"""output.dense"""    )

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

      if "decoder" in name:
            pass
      else:
            UpperCAmelCase__			:							Optional[Any]							=    """swin.""" + name

      return name




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> Optional[int]:
      for key in orig_state_dict.copy().keys():
            UpperCAmelCase__			:							Optional[Any]							=    orig_state_dict.pop(lowerCAmelCase    )

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

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

      return orig_state_dict




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> Dict:
      UpperCAmelCase__			:							Dict							=    torch.load(lowerCAmelCase			,	map_location="""cpu"""    )["""model"""]

      UpperCAmelCase__			:							Optional[Any]							=    get_swin_config(lowerCAmelCase    )
      UpperCAmelCase__			:							Optional[Any]							=    SwinForMaskedImageModeling(lowerCAmelCase    )
      model.eval()

      UpperCAmelCase__			:							str							=    convert_state_dict(lowerCAmelCase			,	lowerCAmelCase    )
      model.load_state_dict(lowerCAmelCase    )

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

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

      with torch.no_grad():
            UpperCAmelCase__			:							Optional[int]							=    model(**lowerCAmelCase    ).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(lowerCAmelCase    )

            print(F"""Saving image processor to {pytorch_dump_folder_path}"""    )
            image_processor.save_pretrained(lowerCAmelCase    )

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




"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_A							=  logging.get_logger(__name__)

class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  ['input_ids', 'attention_mask']



  def __init__(self							,       _lowerCamelCase="</s>"							,       _lowerCamelCase="<unk>"							,       _lowerCamelCase="<pad>"							,       _lowerCamelCase=125							,       _lowerCamelCase=None							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        if extra_ids > 0 and additional_special_tokens is None:
              UpperCAmelCase__			:							List[str]							=    [F"""<extra_id_{i}>""" for i in range(_lowerCamelCase	)]
        elif extra_ids > 0 and additional_special_tokens is not None:
              # Check that we have the right number of extra_id special tokens
              UpperCAmelCase__			:							int							=    len(set(filter(lambda _lowerCamelCase	: bool("""extra_id""" in str(_lowerCamelCase	)	)							,       _lowerCamelCase	)	)	)
              if extra_tokens != extra_ids:
                    raise ValueError(
                        F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                        """ provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"""
                        """ extra_ids tokens"""	)

        UpperCAmelCase__			:							List[str]							=    AddedToken(_lowerCamelCase							,       lstrip=_lowerCamelCase							,       rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,       _lowerCamelCase	) else pad_token
        UpperCAmelCase__			:							int							=    AddedToken(_lowerCamelCase							,       lstrip=_lowerCamelCase							,       rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,       _lowerCamelCase	) else eos_token
        UpperCAmelCase__			:							Optional[int]							=    AddedToken(_lowerCamelCase							,       lstrip=_lowerCamelCase							,       rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,       _lowerCamelCase	) else unk_token

        super().__init__(
            eos_token=_lowerCamelCase							,       unk_token=_lowerCamelCase							,       pad_token=_lowerCamelCase							,       extra_ids=_lowerCamelCase							,       additional_special_tokens=_lowerCamelCase							,       **_lowerCamelCase							,       )

        UpperCAmelCase__			:							int							=    extra_ids

        UpperCAmelCase__			:							List[str]							=    2**8  # utf is 8 bits

        # define special tokens dict
        UpperCAmelCase__			:							Dict[int, str]							=    {
            self.pad_token: 0,
            self.eos_token: 1,
            self.unk_token: 2,
        }
        UpperCAmelCase__			:							str							=    len(self.special_tokens_encoder	)
        UpperCAmelCase__			:							Union[str, Any]							=    len(_lowerCamelCase	)
        for i, token in enumerate(_lowerCamelCase	):
              UpperCAmelCase__			:							Optional[Any]							=    self.vocab_size + i - n
        UpperCAmelCase__			:							Dict[str, int]							=    {v: k for k, v in self.special_tokens_encoder.items()}



  @property
  def  _a      (self	):


        """simple docstring"""

        return self._utf_vocab_size + self._num_special_tokens + self._extra_ids



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None							,       _lowerCamelCase = False	):


        """simple docstring"""

        if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=_lowerCamelCase							,       token_ids_a=_lowerCamelCase							,       already_has_special_tokens=_lowerCamelCase	)

        # normal case: some special tokens
        if token_ids_a is None:
              return ([0] * len(_lowerCamelCase	)) + [1]
        return ([0] * len(_lowerCamelCase	)) + [1] + ([0] * len(_lowerCamelCase	)) + [1]



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if len(_lowerCamelCase	) > 0 and token_ids[-1] == self.eos_token_id:
              warnings.warn(
                  F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
                  """ eos tokens being added."""	)
              return token_ids
        else:
              return token_ids + [self.eos_token_id]



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    [self.eos_token_id]

        if token_ids_a is None:
              return len(token_ids_a + eos	) * [0]
        return len(token_ids_a + eos + token_ids_a + eos	) * [0]



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    self._add_eos_if_not_present(_lowerCamelCase	)
        if token_ids_a is None:
              return token_ids_a
        else:
              UpperCAmelCase__			:							List[str]							=    self._add_eos_if_not_present(_lowerCamelCase	)
              return token_ids_a + token_ids_a



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[Any]							=    [chr(_lowerCamelCase	) for i in text.encode("""utf-8"""	)]
        return tokens



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if token in self.special_tokens_encoder:
              UpperCAmelCase__			:							List[str]							=    self.special_tokens_encoder[token]
        elif token in self.added_tokens_encoder:
              UpperCAmelCase__			:							Tuple							=    self.added_tokens_encoder[token]
        elif len(_lowerCamelCase	) != 1:
              UpperCAmelCase__			:							Dict							=    self.unk_token_id
        else:
              UpperCAmelCase__			:							Tuple							=    ord(_lowerCamelCase	) + self._num_special_tokens
        return token_id



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if index in self.special_tokens_decoder:
              UpperCAmelCase__			:							Optional[Any]							=    self.special_tokens_decoder[index]
        else:
              UpperCAmelCase__			:							Dict							=    chr(index - self._num_special_tokens	)
        return token



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    B""""""
        for token in tokens:
              if token in self.special_tokens_decoder:
                    UpperCAmelCase__			:							Tuple							=    self.special_tokens_decoder[token].encode("""utf-8"""	)
              elif token in self.added_tokens_decoder:
                    UpperCAmelCase__			:							Optional[Any]							=    self.special_tokens_decoder[token].encode("""utf-8"""	)
              elif token in self.special_tokens_encoder:
                    UpperCAmelCase__			:							str							=    token.encode("""utf-8"""	)
              elif token in self.added_tokens_encoder:
                    UpperCAmelCase__			:							List[Any]							=    token.encode("""utf-8"""	)
              else:
                    UpperCAmelCase__			:							Optional[int]							=    bytes([ord(_lowerCamelCase	)]	)
              bstring += tok_string
        UpperCAmelCase__			:							int							=    bstring.decode("""utf-8"""							,       errors="""ignore"""	)
        return string




  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None	):


        """simple docstring"""

        return ()

