






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__       		=  logging.get_logger(__name__)






def 	UpperCamelCase	(							snake_case__  : Optional[Any]	):




     '''simple docstring'''






     __snake_case      :Tuple 							=    """huggingface/label-files"""
     __snake_case      :int 							=    """imagenet-1k-id2label.json"""
     __snake_case      :int 							=    json.load(open(hf_hub_download(snake_case__						,snake_case__						,repo_type="""dataset"""	)						,"""r"""	)	)
     __snake_case      :List[Any] 							=    {int(snake_case__	): v for k, v in idalabel.items()}
     __snake_case      :Tuple 							=    {v: k for k, v in idalabel.items()}

     __snake_case      :Dict 							=    """std_conv""" if """bit""" in model_name else False

     # note that when using BiT as backbone for ViT-hybrid checkpoints,
     # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
     # config.conv_layer = "std_conv_same"
     __snake_case      :Any 							=    BitConfig(
         conv_layer=snake_case__						,num_labels=1000						,idalabel=snake_case__						,labelaid=snake_case__						,)

     return config






def 	UpperCamelCase	(							snake_case__  : Union[str, Any]	):




     '''simple docstring'''






     if "stem.conv" in name:
          __snake_case      :List[str] 							=    name.replace("""stem.conv"""						,"""bit.embedder.convolution"""	)
     if "blocks" in name:
          __snake_case      :Optional[Any] 							=    name.replace("""blocks"""						,"""layers"""	)
     if "head.fc" in name:
          __snake_case      :Any 							=    name.replace("""head.fc"""						,"""classifier.1"""	)
     if name.startswith("""norm"""	):
          __snake_case      :Dict 							=    """bit.""" + name
     if "bit" not in name and "classifier" not in name:
          __snake_case      :int 							=    """bit.encoder.""" + name

     return name






def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :Optional[int] 							=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
     __snake_case      :Optional[int] 							=    Image.open(requests.get(snake_case__						,stream=snake_case__	).raw	)
     return im






@torch.no_grad()
def 	UpperCamelCase	(							snake_case__  : Dict						,snake_case__  : Dict						,snake_case__  : Tuple=False	):




     '''simple docstring'''






     __snake_case      :Optional[Any] 							=    get_config(snake_case__	)

     # load original model from timm
     __snake_case      :Union[str, Any] 							=    create_model(snake_case__						,pretrained=snake_case__	)
     timm_model.eval()

     # load state_dict of original model
     __snake_case      :Tuple 							=    timm_model.state_dict()
     for key in state_dict.copy().keys():
          __snake_case      :str 							=    state_dict.pop(snake_case__	)
          __snake_case      :int 							=    val.squeeze() if """head""" in key else val

     # load HuggingFace model
     __snake_case      :str 							=    BitForImageClassification(snake_case__	)
     model.eval()
     model.load_state_dict(snake_case__	)

     # create image processor
     __snake_case      :List[str] 							=    create_transform(**resolve_data_config({}						,model=snake_case__	)	)
     __snake_case      :Dict 							=    transform.transforms

     __snake_case      :List[Any] 							=    {
         """bilinear""": PILImageResampling.BILINEAR,
         """bicubic""": PILImageResampling.BICUBIC,
         """nearest""": PILImageResampling.NEAREST,
     }

     __snake_case      :Optional[Any] 							=    BitImageProcessor(
         do_resize=snake_case__						,size={"""shortest_edge""": timm_transforms[0].size}						,resample=pillow_resamplings[timm_transforms[0].interpolation.value]						,do_center_crop=snake_case__						,crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}						,do_normalize=snake_case__						,image_mean=timm_transforms[-1].mean.tolist()						,image_std=timm_transforms[-1].std.tolist()						,)

     __snake_case      :List[Any] 							=    prepare_img()
     __snake_case      :Optional[int] 							=    transform(snake_case__	).unsqueeze(0	)
     __snake_case      :int 							=    processor(snake_case__						,return_tensors="""pt"""	).pixel_values

     # verify pixel values
     assert torch.allclose(snake_case__						,snake_case__	)

     # verify logits
     with torch.no_grad():
          __snake_case      :str 							=    model(snake_case__	)
          __snake_case      :Tuple 							=    outputs.logits

     print("""Logits:"""						,logits[0, :3]	)
     print("""Predicted class:"""						,model.config.idalabel[logits.argmax(-1	).item()]	)
     __snake_case      :Optional[Any] 							=    timm_model(snake_case__	)
     assert timm_logits.shape == outputs.logits.shape
     assert torch.allclose(snake_case__						,outputs.logits						,atol=1e-3	)
     print("""Looks ok!"""	)

     if pytorch_dump_folder_path is not None:
          Path(snake_case__	).mkdir(exist_ok=snake_case__	)
          print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''	)
          model.save_pretrained(snake_case__	)
          processor.save_pretrained(snake_case__	)

     if push_to_hub:
          print(f'''Pushing model {model_name} and processor to the hub'''	)
          model.push_to_hub(f'''ybelkada/{model_name}'''	)
          processor.push_to_hub(f'''ybelkada/{model_name}'''	)


if __name__ == "__main__":
     lowerCamelCase__       		=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--model_name""",
         default="""resnetv2_50x1_bitm""",
         type=str,
         help="""Name of the BiT 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."""
     )
     parser.add_argument(
         """--push_to_hub""",
         action="""store_true""",
         help="""Whether to push the model to the hub.""",
     )

     lowerCamelCase__       		=  parser.parse_args()
     convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {"""vocab_file""": """sentencepiece.model"""}

lowerCamelCase__       		=  {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

lowerCamelCase__       		=  {
    """google/rembert""": 256,
}




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					str								=			VOCAB_FILES_NAMES
       lowerCamelCase				:					Tuple								=			PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase				:					Optional[int]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



       def __init__(				self      ,    a__      ,    a__=False      ,    a__=True      ,    a__=True      ,    a__="[CLS]"      ,    a__="[SEP]"      ,    a__="[UNK]"      ,    a__="[SEP]"      ,    a__="[PAD]"      ,    a__="[CLS]"      ,    a__="[MASK]"      ,    **a__      ,    )			->   List[str]:






            '''simple docstring'''





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

            __snake_case      :List[Any] 							=    do_lower_case
            __snake_case      :Dict 							=    remove_space
            __snake_case      :int 							=    keep_accents
            __snake_case      :Optional[int] 							=    vocab_file

            __snake_case      :int 							=    spm.SentencePieceProcessor()
            self.sp_model.Load(a__     )



       @property
       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            return len(self.sp_model     )



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





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



       def __getstate__(				self     )			->   str:






            '''simple docstring'''





            __snake_case      :str 							=    self.__dict__.copy()
            __snake_case      :Dict 							=    None
            return state



       def __setstate__(				self      ,    a__     )			->   str:






            '''simple docstring'''





            __snake_case      :Dict 							=    d
            __snake_case      :Dict 							=    spm.SentencePieceProcessor()
            self.sp_model.Load(self.vocab_file     )



       def        __lowercase    (				self      ,    a__      ,    a__=False     )			->   Tuple:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    self.sp_model.EncodeAsPieces(a__     )
            return pieces



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            return self.sp_model.PieceToId(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Any:






            '''simple docstring'''





            return self.sp_model.IdToPiece(a__     )



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






            '''simple docstring'''





            __snake_case      :List[Any] 							=    self.sp_model.decode_pieces(a__     )
            return out_string



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    [self.sep_token_id]
            __snake_case      :List[Any] 							=    [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        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = False     )			->   List[int]:






            '''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(a__     )) + [1] + ([0] * len(a__     )) + [1]
            return [1] + ([0] * len(a__     )) + [1]



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :Any 							=    [self.sep_token_id]
            __snake_case      :Any 							=    [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        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   Tuple[str]:






            '''simple docstring'''





            if not os.path.isdir(a__     ):
                 logger.error("""Vocabulary path ({}) should be a directory""".format(a__     )     )
                 return
            __snake_case      :Optional[int] 							=    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__     ):
                 copyfile(self.vocab_file      ,    a__     )

            return (out_vocab_file,)


