"""simple docstring"""



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor



def 					_lowerCamelCase(					a							):
       __a     				=  SwinConfig(image_size=1_9_2							)

       if "base" in model_name:
              __a     				=  6
              __a     				=  1_2_8
              __a     				=  (2, 2, 1_8, 2)
              __a     				=  (4, 8, 1_6, 3_2)
       elif "large" in model_name:
              __a     				=  1_2
              __a     				=  1_9_2
              __a     				=  (2, 2, 1_8, 2)
              __a     				=  (6, 1_2, 2_4, 4_8)
       else:
              raise ValueError("Model not supported, only supports base and large variants"							)

       __a     				=  window_size
       __a     				=  embed_dim
       __a     				=  depths
       __a     				=  num_heads

       return config



def 					_lowerCamelCase(					a							):
       if "encoder.mask_token" in name:
              __a     				=  name.replace("encoder.mask_token" ,  "embeddings.mask_token"							)
       if "encoder.patch_embed.proj" in name:
              __a     				=  name.replace("encoder.patch_embed.proj" ,  "embeddings.patch_embeddings.projection"							)
       if "encoder.patch_embed.norm" in name:
              __a     				=  name.replace("encoder.patch_embed.norm" ,  "embeddings.norm"							)
       if "attn.proj" in name:
              __a     				=  name.replace("attn.proj" ,  "attention.output.dense"							)
       if "attn" in name:
              __a     				=  name.replace("attn" ,  "attention.self"							)
       if "norm1" in name:
              __a     				=  name.replace("norm1" ,  "layernorm_before"							)
       if "norm2" in name:
              __a     				=  name.replace("norm2" ,  "layernorm_after"							)
       if "mlp.fc1" in name:
              __a     				=  name.replace("mlp.fc1" ,  "intermediate.dense"							)
       if "mlp.fc2" in name:
              __a     				=  name.replace("mlp.fc2" ,  "output.dense"							)

       if name == "encoder.norm.weight":
              __a     				=  "layernorm.weight"
       if name == "encoder.norm.bias":
              __a     				=  "layernorm.bias"

       if "decoder" in name:
              pass
       else:
              __a     				=  "swin." + name

       return name



def 					_lowerCamelCase(					a ,  a							):
       for key in orig_state_dict.copy().keys():
              __a     				=  orig_state_dict.pop(a							)

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

                     if "weight" in key:
                            __a     				=  val[:dim, :]
                            __a     				=  val[
                                dim : dim * 2, :
                            ]
                            __a     				=  val[-dim:, :]
                     else:
                            __a     				=  val[
                                :dim
                            ]
                            __a     				=  val[
                                dim : dim * 2
                            ]
                            __a     				=  val[
                                -dim:
                            ]
              else:
                     __a     				=  val

       return orig_state_dict



def 					_lowerCamelCase(					a ,  a ,  a ,  a							):
       __a     				=  torch.load(a ,  map_location="cpu"							)["model"]

       __a     				=  get_swin_config(a							)
       __a     				=  SwinForMaskedImageModeling(a							)
       model.eval()

       __a     				=  convert_state_dict(a ,  a							)
       model.load_state_dict(a							)

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

       __a     				=  ViTImageProcessor(size={"height": 1_9_2, "width": 1_9_2}							)
       __a     				=  Image.open(requests.get(a ,  stream=a							).raw							)
       __a     				=  image_processor(images=a ,  return_tensors="pt"							)

       with torch.no_grad():
              __a     				=  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__":
   SCREAMING_SNAKE_CASE__:int			=	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."""
   )

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

"""simple docstring"""



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_barthez import BarthezTokenizer
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]			=	None

SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:int			=	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """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"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__:Dict			=	{
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

SCREAMING_SNAKE_CASE__:List[str]			=	"""▁"""






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			str           =		VOCAB_FILES_NAMES
 _snake_case   :			Optional[Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			str           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Dict           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			str           =		BarthezTokenizer


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<mask>"  ,						**lowerCamelCase  ,						):
        # Mask token behave like a normal word, i.e. include the space before it
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else mask_token

        super().__init__(
            lowerCamelCase  ,						tokenizer_file=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						unk_token=lowerCamelCase  ,						sep_token=lowerCamelCase  ,						cls_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						mask_token=lowerCamelCase  ,						**lowerCamelCase  ,						)

        __a     				=  vocab_file
        __a     				=  False if not self.vocab_file else True


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):

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


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __a     				=  [self.sep_token_id]
        __a     				=  [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]






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

        if not os.path.isdir(lowerCamelCase		):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
               return
        __a     				=  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		):
               copyfile(self.vocab_file  ,						lowerCamelCase		)

        return (out_vocab_file,)
