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

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


a_    					=       logging.get_logger(__name__)

a_    					=       {
    'vocab_file': 'vocab.txt',
    'merges_file': 'bpe.codes',
}

a_    					=       {
    'vocab_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt',
    },
    'merges_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes',
    },
}

a_    					=       {
    'vinai/phobert-base': 256,
    'vinai/phobert-large': 256,
}





def    __lowercase     (						lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_					:						Tuple =					set()
 UpperCamelCase_					:						Dict =					word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)	)
  UpperCamelCase_					:						Any =					char

 UpperCamelCase_					:						Tuple =					set(lowerCamelCase	)
 return pairs






class 		_lowercase						(					snake_case_       ):
 lowercase				     =   VOCAB_FILES_NAMES
 lowercase				     =   PRETRAINED_VOCAB_FILES_MAP
 lowercase				     =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

 def __init__( self	:							Any   ,      snake_case	:							Optional[Any]   ,      snake_case	:							str   ,      snake_case	:							Optional[int]="<s>"   ,      snake_case	:							int="</s>"   ,      snake_case	:							List[Any]="</s>"   ,      snake_case	:							str="<s>"   ,      snake_case	:							Dict="<unk>"   ,      snake_case	:							Any="<pad>"   ,      snake_case	:							str="<mask>"   ,      **snake_case	:							Optional[int]   ,      )   ->						str:

  """simple docstring"""





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

  UpperCamelCase_					:						str =					vocab_file
  UpperCamelCase_					:						Dict =					merges_file

  UpperCamelCase_					:						int =					{}
  UpperCamelCase_					:						Any =					0
  UpperCamelCase_					:						List[Any] =					1
  UpperCamelCase_					:						Optional[Any] =					2
  UpperCamelCase_					:						Optional[Any] =					3

  self.add_from_file(snake_case			)

  UpperCamelCase_					:						Union[str, Any] =					{v: k for k, v in self.encoder.items()}

  with open(snake_case   ,      encoding='utf-8'			) as merges_handle:
   UpperCamelCase_					:						Tuple =					merges_handle.read().split('\n'			)[:-1]
  UpperCamelCase_					:						Optional[int] =					[tuple(merge.split()[:-1]			) for merge in merges]
  UpperCamelCase_					:						str =					dict(zip(snake_case   ,      range(len(snake_case			)			)			)			)
  UpperCamelCase_					:						Dict =					{}

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

  """simple docstring"""





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

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

  """simple docstring"""





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

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							List[int]   ,      snake_case	:							Optional[List[int]] = None			)   ->						List[int]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					[self.sep_token_id]
  UpperCamelCase_					:						Union[str, 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 + sep + token_ids_a + sep			) * [0]

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

  """simple docstring"""





  return len(self.encoder			)

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

  """simple docstring"""





  return dict(self.encoder   ,      **self.added_tokens_encoder			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							str			)   ->						Optional[int]:

  """simple docstring"""





  if token in self.cache:
   return self.cache[token]
  UpperCamelCase_					:						List[Any] =					tuple(snake_case			)
  UpperCamelCase_					:						Optional[int] =					tuple(list(word[:-1]			) + [word[-1] + '</w>']			)
  UpperCamelCase_					:						str =					get_pairs(snake_case			)

  if not pairs:
   return token

  while True:
   UpperCamelCase_					:						Tuple =					min(snake_case   ,      key=lambda snake_case			: self.bpe_ranks.get(snake_case   ,      float('inf'			)			)			)
   if bigram not in self.bpe_ranks:
    break
   UpperCamelCase_, UpperCamelCase_					:						str =					bigram
   UpperCamelCase_					:						Optional[Any] =					[]
   UpperCamelCase_					:						int =					0
   while i < len(snake_case			):
    try:
     UpperCamelCase_					:						List[Any] =					word.index(snake_case   ,      snake_case			)
    except ValueError:
     new_word.extend(word[i:]			)
     break
    else:
     new_word.extend(word[i:j]			)
     UpperCamelCase_					:						List[str] =					j

    if word[i] == first and i < len(snake_case			) - 1 and word[i + 1] == second:
     new_word.append(first + second			)
     i += 2
    else:
     new_word.append(word[i]			)
     i += 1
   UpperCamelCase_					:						Optional[Any] =					tuple(snake_case			)
   UpperCamelCase_					:						Any =					new_word
   if len(snake_case			) == 1:
    break
   else:
    UpperCamelCase_					:						List[str] =					get_pairs(snake_case			)
  UpperCamelCase_					:						Any =					'@@ '.join(snake_case			)
  UpperCamelCase_					:						List[Any] =					word[:-4]
  UpperCamelCase_					:						Dict =					word
  return word

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

  """simple docstring"""





  UpperCamelCase_					:						Dict =					[]

  UpperCamelCase_					:						List[Any] =					re.findall(R'\S+\n?'   ,      snake_case			)

  for token in words:
   split_tokens.extend(list(self.bpe(snake_case			).split(' '			)			)			)
  return split_tokens

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

  """simple docstring"""





  return self.encoder.get(snake_case   ,      self.encoder.get(self.unk_token			)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							Union[str, Any]			)   ->						int:

  """simple docstring"""





  return self.decoder.get(snake_case   ,      self.unk_token			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict   ,      snake_case	:							int			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						int =					' '.join(snake_case			).replace('@@ '   ,      ''			).strip()
  return out_string

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

  """simple docstring"""





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

  if os.path.abspath(self.vocab_file			) != os.path.abspath(snake_case			):
   copyfile(self.vocab_file   ,      snake_case			)

  if os.path.abspath(self.merges_file			) != os.path.abspath(snake_case			):
   copyfile(self.merges_file   ,      snake_case			)

  return out_vocab_file, out_merge_file


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Dict			)   ->						Tuple:

  """simple docstring"""





  if isinstance(snake_case   ,      snake_case			):
   try:
    with open(snake_case   ,      'r'   ,      encoding='utf-8'			) as fd:
     self.add_from_file(snake_case			)
   except FileNotFoundError as fnfe:
    raise fnfe
   except UnicodeError:
    raise Exception(f"Incorrect encoding detected in {f}, please rebuild the dataset"			)
   return

  UpperCamelCase_					:						Dict =					f.readlines()
  for lineTmp in lines:
   UpperCamelCase_					:						Dict =					lineTmp.strip()
   UpperCamelCase_					:						str =					line.rfind(' '			)
   if idx == -1:
    raise ValueError('Incorrect dictionary format, expected \'<token> <cnt>\''			)
   UpperCamelCase_					:						List[Any] =					line[:idx]
   UpperCamelCase_					:						Optional[int] =					len(self.encoder			)




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()
a_    					=       logging.get_logger(__name__)





def    __lowercase     (						lowerCamelCase					:     Optional[Any]	):
 UpperCamelCase_					:						List[Any] =					'huggingface/label-files'
 UpperCamelCase_					:						int =					'imagenet-1k-id2label.json'
 UpperCamelCase_					:						int =					json.load(open(hf_hub_download(lowerCamelCase  ,  lowerCamelCase  ,  repo_type='dataset'	)  ,  'r'	)	)
 UpperCamelCase_					:						str =					{int(lowerCamelCase	): v for k, v in idalabel.items()}
 UpperCamelCase_					:						Optional[int] =					{v: k for k, v in idalabel.items()}

 UpperCamelCase_					:						str =					'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"
 UpperCamelCase_					:						int =					BitConfig(
     conv_layer=lowerCamelCase  ,  num_labels=1000  ,  idalabel=lowerCamelCase  ,  labelaid=lowerCamelCase  ,  )

 return config





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

 return name





def    __lowercase     (						):
 UpperCamelCase_					:						Tuple =					'http://images.cocodataset.org/val2017/000000039769.jpg'
 UpperCamelCase_					:						List[Any] =					Image.open(requests.get(lowerCamelCase  ,  stream=lowerCamelCase	).raw	)
 return im





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     int  ,  lowerCamelCase					:     str  ,  lowerCamelCase					:     List[str]=False	):
 UpperCamelCase_					:						Optional[Any] =					get_config(lowerCamelCase	)

 # load original model from timm
 UpperCamelCase_					:						Dict =					create_model(lowerCamelCase  ,  pretrained=lowerCamelCase	)
 timm_model.eval()

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

 # load HuggingFace model
 UpperCamelCase_					:						Dict =					BitForImageClassification(lowerCamelCase	)
 model.eval()
 model.load_state_dict(lowerCamelCase	)

 # create image processor
 UpperCamelCase_					:						int =					create_transform(**resolve_data_config({}  ,  model=lowerCamelCase	)	)
 UpperCamelCase_					:						List[Any] =					transform.transforms

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

 UpperCamelCase_					:						Tuple =					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()  ,  )

 UpperCamelCase_					:						Dict =					prepare_img()
 UpperCamelCase_					:						Any =					transform(lowerCamelCase	).unsqueeze(0	)
 UpperCamelCase_					:						Union[str, Any] =					processor(lowerCamelCase  ,  return_tensors='pt'	).pixel_values

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

 # verify logits
 with torch.no_grad():
  UpperCamelCase_					:						Dict =					model(lowerCamelCase	)
  UpperCamelCase_					:						Tuple =					outputs.logits

 print('Logits:'  ,  logits[0, :3]	)
 print('Predicted class:'  ,  model.config.idalabel[logits.argmax(-1	).item()]	)
 UpperCamelCase_					:						List[Any] =					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__":
   a_    					=       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.',
   )

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



