import os
from typing import List, Optional, Union

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


lowerCAmelCase__     						=			logging.get_logger(__name__)

lowerCAmelCase__     						=			{'vocab_file': 'vocab.txt'}

lowerCAmelCase__     						=			{
    'vocab_file': {
        'facebook/esm2_t6_8M_UR50D': 'https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt',
        'facebook/esm2_t12_35M_UR50D': 'https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt',
    },
}

lowerCAmelCase__     						=			{
    'facebook/esm2_t6_8M_UR50D': 1024,
    'facebook/esm2_t12_35M_UR50D': 1024,
}






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       with open(lowerCAmelCase__     ,  'r'     ) as f:
              lowerCAmelCase__					     = f.read().splitlines()
              return [l.strip() for l in lines]



class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= VOCAB_FILES_NAMES
 UpperCAmelCase_     			= PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_     			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_     			= ['input_ids', 'attention_mask']




 def __init__(      self :  Optional[Any]      ,   lowercase__ :  str      ,   lowercase__ :  Dict="<unk>"      ,   lowercase__ :  List[Any]="<cls>"      ,   lowercase__ :  Dict="<pad>"      ,   lowercase__ :  Optional[Any]="<mask>"      ,   lowercase__ :  Tuple="<eos>"      ,   **lowercase__ :  Optional[Any]      ,   ):







        '''simple docstring'''



        super().__init__(**lowercase__)
        lowerCAmelCase__					     = load_vocab_file(lowercase__)
        lowerCAmelCase__					     = dict(enumerate(self.all_tokens))
        lowerCAmelCase__					     = {tok: ind for ind, tok in enumerate(self.all_tokens)}
        lowerCAmelCase__					     = unk_token
        lowerCAmelCase__					     = cls_token
        lowerCAmelCase__					     = pad_token
        lowerCAmelCase__					     = mask_token
        lowerCAmelCase__					     = eos_token
        lowerCAmelCase__					     = self.all_tokens
        self._create_trie(self.unique_no_split_tokens)




 def 		__snake_case						(      self :  Any      ,   lowercase__ :  int):







        '''simple docstring'''



        return self._id_to_token.get(lowercase__      ,   self.unk_token)




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  str):







        '''simple docstring'''



        return self._token_to_id.get(lowercase__      ,   self._token_to_id.get(self.unk_token))




 def 		__snake_case						(      self :  Dict      ,   lowercase__ :  str      ,   **lowercase__ :  int):







        '''simple docstring'''



        return text.split()




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  List[str]=False):







        '''simple docstring'''



        return len(self._id_to_token)




 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        return {token: i for i, token in enumerate(self.all_tokens)}




 def 		__snake_case						(      self :  Dict      ,   lowercase__ :  str):







        '''simple docstring'''



        return self._token_to_id.get(lowercase__      ,   self._token_to_id.get(self.unk_token))




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  int):







        '''simple docstring'''



        return self._id_to_token.get(lowercase__      ,   self.unk_token)




 def 		__snake_case						(      self :  int      ,   lowercase__ :  List[int]      ,   lowercase__ :  Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__					     = [self.cls_token_id]
        lowerCAmelCase__					     = [self.eos_token_id]  # No sep token in ESM vocabulary
        if token_ids_a is None:
               if self.eos_token_id is None:
                      return cls + token_ids_a
               else:
                      return cls + token_ids_a + sep
        elif self.eos_token_id is None:
               raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!')
        return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token




 def 		__snake_case						(      self :  Dict      ,   lowercase__ :  List      ,   lowercase__ :  Optional[List] = None      ,   lowercase__ :  bool = False):







        '''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 token in self.all_special_ids else 0 for token in token_ids_a]
        lowerCAmelCase__					     = [1] + ([0] * len(lowercase__)) + [1]
        if token_ids_a is not None:
               mask += [0] * len(lowercase__) + [1]
        return mask




 def 		__snake_case						(      self :  str      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = os.path.join(lowercase__      ,   (filename_prefix + '-' if filename_prefix else '') + 'vocab.txt')
        with open(lowercase__      ,   'w') as f:
               f.write('\n'.join(self.all_tokens))
        return (vocab_file,)




 @property
 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        return self.get_vocab_size(with_added_tokens=lowercase__)




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  Union[List[str], List[AddedToken]]      ,   lowercase__ :  bool = False):







        '''simple docstring'''



        return super()._add_tokens(lowercase__      ,   special_tokens=lowercase__)

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 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = 'huggingface/label-files'
       lowerCAmelCase__					     = 'imagenet-1k-id2label.json'
       lowerCAmelCase__					     = json.load(open(hf_hub_download(lowerCAmelCase__     ,  lowerCAmelCase__     ,  repo_type='dataset'     )     ,  'r'     )     )
       lowerCAmelCase__					     = {int(lowerCAmelCase__     ): v for k, v in idalabel.items()}
       lowerCAmelCase__					     = {v: k for k, v in idalabel.items()}

       lowerCAmelCase__					     = '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"
       lowerCAmelCase__					     = BitConfig(
           conv_layer=lowerCAmelCase__     ,  num_labels=1_0_0_0     ,  idalabel=lowerCAmelCase__     ,  labelaid=lowerCAmelCase__     ,  )

       return config






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       if "stem.conv" in name:
              lowerCAmelCase__					     = name.replace('stem.conv'     ,  'bit.embedder.convolution'     )
       if "blocks" in name:
              lowerCAmelCase__					     = name.replace('blocks'     ,  'layers'     )
       if "head.fc" in name:
              lowerCAmelCase__					     = name.replace('head.fc'     ,  'classifier.1'     )
       if name.startswith('norm'     ):
              lowerCAmelCase__					     = 'bit.' + name
       if "bit" not in name and "classifier" not in name:
              lowerCAmelCase__					     = 'bit.encoder.' + name

       return name






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = 'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowerCAmelCase__					     = Image.open(requests.get(lowerCAmelCase__     ,  stream=lowerCAmelCase__     ).raw     )
       return im






@torch.no_grad()
def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=False     ):
       lowerCAmelCase__					     = get_config(lowerCAmelCase__     )

       # load original model from timm
       lowerCAmelCase__					     = create_model(lowerCAmelCase__     ,  pretrained=lowerCAmelCase__     )
       timm_model.eval()

       # load state_dict of original model
       lowerCAmelCase__					     = timm_model.state_dict()
       for key in state_dict.copy().keys():
              lowerCAmelCase__					     = state_dict.pop(lowerCAmelCase__     )
              lowerCAmelCase__					     = val.squeeze() if 'head' in key else val

       # load HuggingFace model
       lowerCAmelCase__					     = BitForImageClassification(lowerCAmelCase__     )
       model.eval()
       model.load_state_dict(lowerCAmelCase__     )

       # create image processor
       lowerCAmelCase__					     = create_transform(**resolve_data_config({}     ,  model=lowerCAmelCase__     )     )
       lowerCAmelCase__					     = transform.transforms

       lowerCAmelCase__					     = {
           'bilinear': PILImageResampling.BILINEAR,
           'bicubic': PILImageResampling.BICUBIC,
           'nearest': PILImageResampling.NEAREST,
       }

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

       lowerCAmelCase__					     = prepare_img()
       lowerCAmelCase__					     = transform(lowerCAmelCase__     ).unsqueeze(0     )
       lowerCAmelCase__					     = processor(lowerCAmelCase__     ,  return_tensors='pt'     ).pixel_values

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

       # verify logits
       with torch.no_grad():
              lowerCAmelCase__					     = model(lowerCAmelCase__     )
              lowerCAmelCase__					     = outputs.logits

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

       if pytorch_dump_folder_path is not None:
              Path(lowerCAmelCase__     ).mkdir(exist_ok=lowerCAmelCase__     )
              print(F"""Saving model {model_name} and processor to {pytorch_dump_folder_path}"""     )
              model.save_pretrained(lowerCAmelCase__     )
              processor.save_pretrained(lowerCAmelCase__     )

       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)
