





import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE_						   =       {
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    }
}

SCREAMING_SNAKE_CASE_						   =       {
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

# Segments (not really needed)
SCREAMING_SNAKE_CASE_						   =       0
SCREAMING_SNAKE_CASE_						   =       1
SCREAMING_SNAKE_CASE_						   =       2
SCREAMING_SNAKE_CASE_						   =       3
SCREAMING_SNAKE_CASE_						   =       4

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Any			=			VOCAB_FILES_NAMES
     __snake_case    :	Union[str, Any]			=			PRETRAINED_VOCAB_FILES_MAP
     __snake_case    :	str			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __snake_case    :	int			=			"left"






     def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    Union[str, Any]						,lowerCamelCase__						:    str=False						,lowerCamelCase__						:    Any=True						,lowerCamelCase__						:    Any=False						,lowerCamelCase__						:    Union[str, Any]="<s>"						,lowerCamelCase__						:    int="</s>"						,lowerCamelCase__						:    Any="<unk>"						,lowerCamelCase__						:    str="<sep>"						,lowerCamelCase__						:    Tuple="<pad>"						,lowerCamelCase__						:    Optional[int]="<cls>"						,lowerCamelCase__						:    Any="<mask>"						,lowerCamelCase__						:    Union[str, Any]=["<eop>", "<eod>"]						,lowerCamelCase__						:    Optional[Dict[str, Any]] = None						,**lowerCamelCase__						:    Union[str, Any]						,)     ->		None:




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE		=						{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=lowerCamelCase__						,remove_space=lowerCamelCase__						,keep_accents=lowerCamelCase__						,bos_token=lowerCamelCase__						,eos_token=lowerCamelCase__						,unk_token=lowerCamelCase__						,sep_token=lowerCamelCase__						,pad_token=lowerCamelCase__						,cls_token=lowerCamelCase__						,mask_token=lowerCamelCase__						,additional_special_tokens=lowerCamelCase__						,sp_model_kwargs=self.sp_model_kwargs						,**lowerCamelCase__						,)

           SCREAMING_SNAKE_CASE		=						3

           SCREAMING_SNAKE_CASE		=						do_lower_case
           SCREAMING_SNAKE_CASE		=						remove_space
           SCREAMING_SNAKE_CASE		=						keep_accents
           SCREAMING_SNAKE_CASE		=						vocab_file

           SCREAMING_SNAKE_CASE		=						spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(lowerCamelCase__       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		List[str]:




           '''simple docstring'''


           return len(self.sp_model       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{self.convert_ids_to_tokens(lowerCamelCase__       ): i for i in range(self.vocab_size       )}
           vocab.update(self.added_tokens_encoder       )
           return vocab






     def __getstate__(							self						:    Dict       )     ->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.__dict__.copy()
           SCREAMING_SNAKE_CASE		=						None
           return state






     def __setstate__(							self						:    List[str]						,lowerCamelCase__						:    str       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						d

           # for backward compatibility
           if not hasattr(self						,"""sp_model_kwargs"""       ):
                 SCREAMING_SNAKE_CASE		=						{}

           SCREAMING_SNAKE_CASE		=						spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(self.vocab_file       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]						,lowerCamelCase__						:    int       )     ->		Any:




           '''simple docstring'''


           if self.remove_space:
                 SCREAMING_SNAKE_CASE		=						""" """.join(inputs.strip().split()       )
           else:
                 SCREAMING_SNAKE_CASE		=						inputs
           SCREAMING_SNAKE_CASE		=						outputs.replace("""``"""						,"""\""""       ).replace("""''"""						,"""\""""       )

           if not self.keep_accents:
                 SCREAMING_SNAKE_CASE		=						unicodedata.normalize("""NFKD"""						,lowerCamelCase__       )
                 SCREAMING_SNAKE_CASE		=						"""""".join([c for c in outputs if not unicodedata.combining(lowerCamelCase__       )]       )
           if self.do_lower_case:
                 SCREAMING_SNAKE_CASE		=						outputs.lower()

           return outputs






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]						,lowerCamelCase__						:    str       )     ->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.preprocess_text(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						self.sp_model.encode(lowerCamelCase__						,out_type=lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						[]
           for piece in pieces:
                 if len(lowerCamelCase__       ) > 1 and piece[-1] == str(""","""       ) and piece[-2].isdigit():
                       SCREAMING_SNAKE_CASE		=						self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCamelCase__						,""""""       )       )
                       if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                             if len(cur_pieces[0]       ) == 1:
                                   SCREAMING_SNAKE_CASE		=						cur_pieces[1:]
                             else:
                                   SCREAMING_SNAKE_CASE		=						cur_pieces[0][1:]
                       cur_pieces.append(piece[-1]       )
                       new_pieces.extend(lowerCamelCase__       )
                 else:
                       new_pieces.append(lowerCamelCase__       )

           return new_pieces






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]						,lowerCamelCase__						:    Any       )     ->		Union[str, Any]:




           '''simple docstring'''


           return self.sp_model.PieceToId(lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]						,lowerCamelCase__						:    Union[str, Any]       )     ->		Tuple:




           '''simple docstring'''


           return self.sp_model.IdToPiece(lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int						,lowerCamelCase__						:    str       )     ->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""""".join(lowerCamelCase__       ).replace(lowerCamelCase__						,""" """       ).strip()
           return out_string






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict						,lowerCamelCase__						:    List[int]						,lowerCamelCase__						:    bool = False						,lowerCamelCase__						:    bool = None						,lowerCamelCase__						:    bool = True						,**lowerCamelCase__						:    List[Any]						,)     ->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						kwargs.pop("""use_source_tokenizer"""						,lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						self.convert_ids_to_tokens(lowerCamelCase__						,skip_special_tokens=lowerCamelCase__       )

           # To avoid mixing byte-level and unicode for byte-level BPT
           # we need to build string separately for added tokens and byte-level tokens
           # cf. https://github.com/huggingface/transformers/issues/1133
           SCREAMING_SNAKE_CASE		=						[]
           SCREAMING_SNAKE_CASE		=						[]
           for token in filtered_tokens:
                 if skip_special_tokens and token in self.all_special_ids:
                       continue
                 if token in self.added_tokens_encoder:
                       if current_sub_text:
                             sub_texts.append(self.convert_tokens_to_string(lowerCamelCase__       )       )
                             SCREAMING_SNAKE_CASE		=						[]
                       sub_texts.append(lowerCamelCase__       )
                 else:
                       current_sub_text.append(lowerCamelCase__       )
           if current_sub_text:
                 sub_texts.append(self.convert_tokens_to_string(lowerCamelCase__       )       )

           # Mimic the behavior of the Rust tokenizer:
           # By default, there are no spaces between special tokens
           SCREAMING_SNAKE_CASE		=						"""""".join(lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						(
               clean_up_tokenization_spaces
               if clean_up_tokenization_spaces is not None
               else self.clean_up_tokenization_spaces
           )
           if clean_up_tokenization_spaces:
                 SCREAMING_SNAKE_CASE		=						self.clean_up_tokenization(lowerCamelCase__       )
                 return clean_text
           else:
                 return text






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict						,lowerCamelCase__						:    List[int]						,lowerCamelCase__						:    Optional[List[int]] = None       )     ->		List[int]:




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any						,lowerCamelCase__						:    List[int]						,lowerCamelCase__						:    Optional[List[int]] = None						,lowerCamelCase__						:    bool = False       )     ->		List[int]:




           '''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__       )

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]						,lowerCamelCase__						:    List[int]						,lowerCamelCase__						:    Optional[List[int]] = None       )     ->		List[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[self.sep_token_id]
           SCREAMING_SNAKE_CASE		=						[2]

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]						,lowerCamelCase__						:    str						,lowerCamelCase__						:    Optional[str] = None       )     ->		Tuple[str]:




           '''simple docstring'''


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

           if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowerCamelCase__       ) and os.path.isfile(self.vocab_file       ):
                 copyfile(self.vocab_file						,lowerCamelCase__       )
           elif not os.path.isfile(self.vocab_file       ):
                 with open(lowerCamelCase__						,"""wb"""       ) as fi:
                       SCREAMING_SNAKE_CASE		=						self.sp_model.serialized_model_proto()
                       fi.write(lowerCamelCase__       )

           return (out_vocab_file,)









import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						SwinConfig()
      SCREAMING_SNAKE_CASE		=						swin_name.split("""_"""	)

      SCREAMING_SNAKE_CASE		=						name_split[1]
      SCREAMING_SNAKE_CASE		=						int(name_split[4]	)
      SCREAMING_SNAKE_CASE		=						int(name_split[3][-1]	)

      if model_size == "tiny":
            SCREAMING_SNAKE_CASE		=						96
            SCREAMING_SNAKE_CASE		=						(2, 2, 6, 2)
            SCREAMING_SNAKE_CASE		=						(3, 6, 12, 24)
      elif model_size == "small":
            SCREAMING_SNAKE_CASE		=						96
            SCREAMING_SNAKE_CASE		=						(2, 2, 18, 2)
            SCREAMING_SNAKE_CASE		=						(3, 6, 12, 24)
      elif model_size == "base":
            SCREAMING_SNAKE_CASE		=						1_28
            SCREAMING_SNAKE_CASE		=						(2, 2, 18, 2)
            SCREAMING_SNAKE_CASE		=						(4, 8, 16, 32)
      else:
            SCREAMING_SNAKE_CASE		=						1_92
            SCREAMING_SNAKE_CASE		=						(2, 2, 18, 2)
            SCREAMING_SNAKE_CASE		=						(6, 12, 24, 48)

      if "in22k" in swin_name:
            SCREAMING_SNAKE_CASE		=						2_18_41
      else:
            SCREAMING_SNAKE_CASE		=						10_00
            SCREAMING_SNAKE_CASE		=						"""huggingface/label-files"""
            SCREAMING_SNAKE_CASE		=						"""imagenet-1k-id2label.json"""
            SCREAMING_SNAKE_CASE		=						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						repo_type="""dataset"""	) ,						"""r"""	)	)
            SCREAMING_SNAKE_CASE		=						{int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE		=						idalabel
            SCREAMING_SNAKE_CASE		=						{v: k for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE		=						img_size
      SCREAMING_SNAKE_CASE		=						num_classes
      SCREAMING_SNAKE_CASE		=						embed_dim
      SCREAMING_SNAKE_CASE		=						depths
      SCREAMING_SNAKE_CASE		=						num_heads
      SCREAMING_SNAKE_CASE		=						window_size

      return config



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Dict:


      '''simple docstring'''


      if "patch_embed.proj" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""patch_embed.proj""" ,						"""embeddings.patch_embeddings.projection"""	)
      if "patch_embed.norm" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""patch_embed.norm""" ,						"""embeddings.norm"""	)
      if "layers" in name:
            SCREAMING_SNAKE_CASE		=						"""encoder.""" + name
      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 == "norm.weight":
            SCREAMING_SNAKE_CASE		=						"""layernorm.weight"""
      if name == "norm.bias":
            SCREAMING_SNAKE_CASE		=						"""layernorm.bias"""

      if "head" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""head""" ,						"""classifier"""	)
      else:
            SCREAMING_SNAKE_CASE		=						"""swin.""" + name

      return name



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Any:


      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE		=						orig_state_dict.pop(_SCREAMING_SNAKE_CASE	)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  SCREAMING_SNAKE_CASE		=						key.split("""."""	)
                  SCREAMING_SNAKE_CASE		=						int(key_split[1]	)
                  SCREAMING_SNAKE_CASE		=						int(key_split[3]	)
                  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    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						timm.create_model(_SCREAMING_SNAKE_CASE ,						pretrained=_SCREAMING_SNAKE_CASE	)
      timm_model.eval()

      SCREAMING_SNAKE_CASE		=						get_swin_config(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						SwinForImageClassification(_SCREAMING_SNAKE_CASE	)
      model.eval()

      SCREAMING_SNAKE_CASE		=						convert_state_dict(timm_model.state_dict() ,						_SCREAMING_SNAKE_CASE	)
      model.load_state_dict(_SCREAMING_SNAKE_CASE	)

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

      SCREAMING_SNAKE_CASE		=						AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" ,						"""-"""	)	)	)
      SCREAMING_SNAKE_CASE		=						Image.open(requests.get(_SCREAMING_SNAKE_CASE ,						stream=_SCREAMING_SNAKE_CASE	).raw	)
      SCREAMING_SNAKE_CASE		=						image_processor(images=_SCREAMING_SNAKE_CASE ,						return_tensors="""pt"""	)

      SCREAMING_SNAKE_CASE		=						timm_model(inputs["""pixel_values"""]	)
      SCREAMING_SNAKE_CASE		=						model(**_SCREAMING_SNAKE_CASE	).logits

      assert torch.allclose(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						atol=1E-3	)

      print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}"""	)
      model.save_pretrained(_SCREAMING_SNAKE_CASE	)

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						   =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--swin_name""",
          default="""swin_tiny_patch4_window7_224""",
          type=str,
          help="""Name of the Swin timm model you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
      )

      SCREAMING_SNAKE_CASE_						   =       parser.parse_args()
      convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)


