

import os
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 logging


__UpperCAmelCase								=			logging.get_logger(__name__)

__UpperCAmelCase								=			{"vocab_file": "sentencepiece.bpe.model"}

__UpperCAmelCase								=			{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

__UpperCAmelCase								=			{
    "moussaKam/mbarthez": 1_024,
    "moussaKam/barthez": 1_024,
    "moussaKam/barthez-orangesum-title": 1_024,
}

__UpperCAmelCase								=			"▁"



class SCREAMING_SNAKE_CASE     (  lowercase_			):
  """simple docstring"""






  lowerCamelCase   :      List[Any]										=VOCAB_FILES_NAMES
  lowerCamelCase   :      Optional[Any]										=PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase   :      int										=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase   :      Tuple										=["input_ids", "attention_mask"]







  def __init__(      self			:   str	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   str="<s>"	,							lowerCAmelCase			:   List[Any]="</s>"	,							lowerCAmelCase			:   Optional[int]="</s>"	,							lowerCAmelCase			:   List[Any]="<s>"	,							lowerCAmelCase			:   int="<unk>"	,							lowerCAmelCase			:   Any="<pad>"	,							lowerCAmelCase			:   str="<mask>"	,							lowerCAmelCase			:   str = None	,							**lowerCAmelCase			:   Optional[Any]	,							)							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						AddedToken(a__	,							lstrip=a__	,							rstrip=a__   ) if isinstance(a__	,							a__   ) else mask_token

     __lowerCAmelCase  :      str    =						{} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=a__	,							eos_token=a__	,							unk_token=a__	,							sep_token=a__	,							cls_token=a__	,							pad_token=a__	,							mask_token=a__	,							sp_model_kwargs=self.sp_model_kwargs	,							**a__	,							)

     __lowerCAmelCase  :      Optional[int]    =						vocab_file
     __lowerCAmelCase  :      Union[str, Any]    =						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
     self.sp_model.Load(str(a__   )   )

     __lowerCAmelCase  :      List[Any]    =						{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

     __lowerCAmelCase  :      Tuple    =						len(self.sp_model   ) - 1
     __lowerCAmelCase  :      Tuple    =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   List[str] = None   )							->     List[int]:






     """simple docstring"""







     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     __lowerCAmelCase  :      Dict    =						[self.cls_token_id]
     __lowerCAmelCase  :      Union[str, Any]    =						[self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Optional[int] = None	,							lowerCAmelCase			:   List[Any] = False   )							->     List[int]:






     """simple docstring"""







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

     if token_ids_a is None:
        return [1] + ([0] * len(a__   )) + [1]
     return [1] + ([0] * len(a__   )) + [1, 1] + ([0] * len(a__   )) + [1]







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   List[Any] = None   )							->     List[int]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						[self.sep_token_id]
     __lowerCAmelCase  :      Tuple    =						[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 + sep + token_ids_a + sep   ) * [0]







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     List[Any]:






     """simple docstring"""







     return len(self.sp_model   )







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






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						{self.convert_ids_to_tokens(a__   ): i for i in range(self.vocab_size   )}
     vocab.update(self.added_tokens_encoder   )
     return vocab







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Any   )							->     List[str]:






     """simple docstring"""







     return self.sp_model.encode(a__	,							out_type=a__   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							lowerCAmelCase			:   Optional[int]   )							->     int:






     """simple docstring"""







     if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
     __lowerCAmelCase  :      List[str]    =						self.sp_model.PieceToId(a__   )

     return spm_id if spm_id else self.unk_token_id







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Dict   )							->     Tuple:






     """simple docstring"""







     if index in self.fairseq_ids_to_tokens:
        return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(a__   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   int   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						[]
     __lowerCAmelCase  :      Tuple    =						""""""
     __lowerCAmelCase  :      Optional[int]    =						False
     for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
           if not prev_is_special:
              out_string += " "
           out_string += self.sp_model.decode(a__   ) + token
           __lowerCAmelCase  :      int    =						True
           __lowerCAmelCase  :      str    =						[]
        else:
           current_sub_tokens.append(a__   )
           __lowerCAmelCase  :      Optional[int]    =						False
     out_string += self.sp_model.decode(a__   )
     return out_string.strip()







  def __getstate__(      self			:   int   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.__dict__.copy()
     __lowerCAmelCase  :      Optional[int]    =						None
     return state







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






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						d

     # for backward compatibility
     if not hasattr(self	,							"""sp_model_kwargs"""   ):
        __lowerCAmelCase  :      Union[str, Any]    =						{}

     __lowerCAmelCase  :      Optional[Any]    =						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
     self.sp_model.Load(self.vocab_file   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[int] = None   )							->     Tuple[str]:






     """simple docstring"""







     if not os.path.isdir(a__   ):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
        return
     __lowerCAmelCase  :      Union[str, Any]    =						os.path.join(
         a__	,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

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

     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      snake_case_				(__A					:						Optional[Any]					)      ->     Tuple:
   __lowerCAmelCase  :      Optional[int]    =						SwinConfig()
   __lowerCAmelCase  :      List[Any]    =						swin_name.split("""_"""					)

   __lowerCAmelCase  :      Dict    =						name_split[1]
   __lowerCAmelCase  :      Optional[Any]    =						int(name_split[4]					)
   __lowerCAmelCase  :      List[Any]    =						int(name_split[3][-1]					)

   if model_size == "tiny":
      __lowerCAmelCase  :      List[Any]    =						9_6
      __lowerCAmelCase  :      List[Any]    =						(2, 2, 6, 2)
      __lowerCAmelCase  :      Optional[Any]    =						(3, 6, 1_2, 2_4)
   elif model_size == "small":
      __lowerCAmelCase  :      List[Any]    =						9_6
      __lowerCAmelCase  :      Optional[int]    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      Optional[int]    =						(3, 6, 1_2, 2_4)
   elif model_size == "base":
      __lowerCAmelCase  :      List[Any]    =						1_2_8
      __lowerCAmelCase  :      Tuple    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      int    =						(4, 8, 1_6, 3_2)
   else:
      __lowerCAmelCase  :      List[Any]    =						1_9_2
      __lowerCAmelCase  :      List[str]    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      int    =						(6, 1_2, 2_4, 4_8)

   if "in22k" in swin_name:
      __lowerCAmelCase  :      Dict    =						2_1_8_4_1
   else:
      __lowerCAmelCase  :      Optional[Any]    =						1_0_0_0
      __lowerCAmelCase  :      Union[str, Any]    =						"""huggingface/label-files"""
      __lowerCAmelCase  :      Any    =						"""imagenet-1k-id2label.json"""
      __lowerCAmelCase  :      Any    =						json.load(open(hf_hub_download(__A ,       __A ,       repo_type="""dataset"""					) ,       """r"""					)					)
      __lowerCAmelCase  :      int    =						{int(__A					): v for k, v in idalabel.items()}
      __lowerCAmelCase  :      str    =						idalabel
      __lowerCAmelCase  :      int    =						{v: k for k, v in idalabel.items()}

   __lowerCAmelCase  :      Optional[Any]    =						img_size
   __lowerCAmelCase  :      Optional[Any]    =						num_classes
   __lowerCAmelCase  :      Tuple    =						embed_dim
   __lowerCAmelCase  :      Union[str, Any]    =						depths
   __lowerCAmelCase  :      Optional[Any]    =						num_heads
   __lowerCAmelCase  :      Tuple    =						window_size

   return config





def      snake_case_				(__A					:						int					)      ->     Optional[Any]:
   if "patch_embed.proj" in name:
      __lowerCAmelCase  :      Optional[Any]    =						name.replace("""patch_embed.proj""" ,       """embeddings.patch_embeddings.projection"""					)
   if "patch_embed.norm" in name:
      __lowerCAmelCase  :      List[Any]    =						name.replace("""patch_embed.norm""" ,       """embeddings.norm"""					)
   if "layers" in name:
      __lowerCAmelCase  :      int    =						"""encoder.""" + name
   if "attn.proj" in name:
      __lowerCAmelCase  :      Tuple    =						name.replace("""attn.proj""" ,       """attention.output.dense"""					)
   if "attn" in name:
      __lowerCAmelCase  :      Optional[Any]    =						name.replace("""attn""" ,       """attention.self"""					)
   if "norm1" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""norm1""" ,       """layernorm_before"""					)
   if "norm2" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""norm2""" ,       """layernorm_after"""					)
   if "mlp.fc1" in name:
      __lowerCAmelCase  :      List[str]    =						name.replace("""mlp.fc1""" ,       """intermediate.dense"""					)
   if "mlp.fc2" in name:
      __lowerCAmelCase  :      str    =						name.replace("""mlp.fc2""" ,       """output.dense"""					)

   if name == "norm.weight":
      __lowerCAmelCase  :      Dict    =						"""layernorm.weight"""
   if name == "norm.bias":
      __lowerCAmelCase  :      Optional[int]    =						"""layernorm.bias"""

   if "head" in name:
      __lowerCAmelCase  :      int    =						name.replace("""head""" ,       """classifier"""					)
   else:
      __lowerCAmelCase  :      List[str]    =						"""swin.""" + name

   return name





def      snake_case_				(__A					:						List[Any] ,       __A					:						str					)      ->     int:
   for key in orig_state_dict.copy().keys():
      __lowerCAmelCase  :      Tuple    =						orig_state_dict.pop(__A					)

      if "mask" in key:
         continue
      elif "qkv" in key:
         __lowerCAmelCase  :      Any    =						key.split("""."""					)
         __lowerCAmelCase  :      Union[str, Any]    =						int(key_split[1]					)
         __lowerCAmelCase  :      Optional[Any]    =						int(key_split[3]					)
         __lowerCAmelCase  :      Optional[int]    =						model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

   return orig_state_dict





def      snake_case_				(__A					:						Union[str, Any] ,       __A					:						int					)      ->     Any:
   __lowerCAmelCase  :      List[Any]    =						timm.create_model(__A ,       pretrained=__A					)
   timm_model.eval()

   __lowerCAmelCase  :      str    =						get_swin_config(__A					)
   __lowerCAmelCase  :      Any    =						SwinForImageClassification(__A					)
   model.eval()

   __lowerCAmelCase  :      str    =						convert_state_dict(timm_model.state_dict() ,       __A					)
   model.load_state_dict(__A					)

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

   __lowerCAmelCase  :      Any    =						AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" ,       """-"""					)					)					)
   __lowerCAmelCase  :      List[Any]    =						Image.open(requests.get(__A ,       stream=__A					).raw					)
   __lowerCAmelCase  :      List[str]    =						image_processor(images=__A ,       return_tensors="""pt"""					)

   __lowerCAmelCase  :      Tuple    =						timm_model(inputs["""pixel_values"""]					)
   __lowerCAmelCase  :      Dict    =						model(**__A					).logits

   assert torch.allclose(__A ,       __A ,       atol=1e-3					)

   print(f'''Saving model {swin_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 __name__ == "__main__":
   __UpperCAmelCase								=			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."""
   )

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


