


import argparse
import json
from pathlib import Path

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

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
snake_case    :   Union[str, Any]	    =					logging.get_logger(__name__)
def     __lowerCamelCase						(			UpperCAmelCase_						:		Union[str, Any]			,  UpperCAmelCase_						:		Any=False						):




      """simple docstring"""





      a						:Any										=	[]

      # fmt: off
      # stem:
      rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''')						)
      rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''')						)

      rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''')						)
      rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''')						)

      # backbone
      rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''')						)
      rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''')						)
      rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''')						)

      for stage_idx in range(len(config.backbone_config.depths						)						):
            for layer_idx in range(config.backbone_config.depths[stage_idx]						):
                  rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''')						)
                  rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''')						)
                  rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''')						)
                  rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''')						)
                  rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''')						)
                  rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''')						)
                  rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''')						)
                  rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''')						)
                  rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''')						)

            rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''')						)
            rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''')						)
            rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''')						)

      # transformer encoder
      for i in range(config.num_hidden_layers						):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')						)
            rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')						)
            rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')						)
            rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')						)
            rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')						)
            rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')						)
            rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')						)
            rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')						)
            rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')						)
            rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')						)

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ('''norm.weight''', '''layernorm.weight'''),
                    ('''norm.bias''', '''layernorm.bias'''),
                    ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
                    ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
                ]						)

            # if just the base model, we should remove "vit" from all keys that start with "vit"
            a						:List[str]										=	[(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''						) else pair for pair in rename_keys]
      else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ('''norm.weight''', '''vit.layernorm.weight'''),
                    ('''norm.bias''', '''vit.layernorm.bias'''),
                    ('''head.weight''', '''classifier.weight'''),
                    ('''head.bias''', '''classifier.bias'''),
                ]						)
      # fmt: on

      return rename_keys
def     __lowerCamelCase						(			UpperCAmelCase_						:		Any			,  UpperCAmelCase_						:		Dict			,  UpperCAmelCase_						:		Optional[int]=False						):




      """simple docstring"""





      for i in range(config.num_hidden_layers						):
            if base_model:
                  a						:List[Any]										=	''''''
            else:
                  a						:Optional[Any]										=	'''vit.'''
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            a						:Dict										=	state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''						)
            a						:List[Any]										=	state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''						)
            # next, add query, keys and values (in that order) to the state dict
            a						:Optional[int]										=	in_proj_weight[
                : config.hidden_size, :
            ]
            a						:Dict										=	in_proj_bias[: config.hidden_size]
            a						:Dict										=	in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            a						:str										=	in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            a						:Tuple										=	in_proj_weight[
                -config.hidden_size :, :
            ]
            a						:Any										=	in_proj_bias[-config.hidden_size :]
def     __lowerCamelCase						(			UpperCAmelCase_						:		Tuple						):




      """simple docstring"""





      a						:List[Any]										=	['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(UpperCAmelCase_			,  UpperCAmelCase_						)
def     __lowerCamelCase						(			UpperCAmelCase_						:		str			,  UpperCAmelCase_						:		Union[str, Any]			,  UpperCAmelCase_						:		Optional[Any]						):




      """simple docstring"""





      a						:str										=	dct.pop(UpperCAmelCase_						)
      a						:Any										=	val
def     __lowerCamelCase						(			):




      """simple docstring"""





      a						:List[Any]										=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      a						:Any										=	Image.open(requests.get(UpperCAmelCase_			,  stream=UpperCAmelCase_						).raw						)
      return im
@torch.no_grad()
def     __lowerCamelCase						(			UpperCAmelCase_						:		int			,  UpperCAmelCase_						:		List[Any]			,  UpperCAmelCase_						:		List[str]=False						):




      """simple docstring"""





      a						:Any										=	BitConfig(
          global_padding='''same'''			,  layer_type='''bottleneck'''			,  depths=(3, 4, 9)			,  out_features=['''stage3''']			,  embedding_dynamic_padding=UpperCAmelCase_			,  )
      a						:str										=	ViTHybridConfig(backbone_config=UpperCAmelCase_			,  image_size=384			,  num_labels=1000						)
      a						:Tuple										=	False

      # load original model from timm
      a						:List[Any]										=	timm.create_model(UpperCAmelCase_			,  pretrained=UpperCAmelCase_						)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      a						:Union[str, Any]										=	timm_model.state_dict()
      if base_model:
            remove_classification_head_(UpperCAmelCase_						)
      a						:int										=	create_rename_keys(UpperCAmelCase_			,  UpperCAmelCase_						)
      for src, dest in rename_keys:
            rename_key(UpperCAmelCase_			,  UpperCAmelCase_			,  UpperCAmelCase_						)
      read_in_q_k_v(UpperCAmelCase_			,  UpperCAmelCase_			,  UpperCAmelCase_						)

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

      # load HuggingFace model
      if vit_name[-5:] == "in21k":
            a						:List[Any]										=	ViTHybridModel(UpperCAmelCase_						).eval()
      else:
            a						:Optional[int]										=	ViTHybridForImageClassification(UpperCAmelCase_						).eval()
      model.load_state_dict(UpperCAmelCase_						)

      # create image processor
      a						:List[Any]										=	create_transform(**resolve_data_config({}			,  model=UpperCAmelCase_						)						)
      a						:Union[str, Any]										=	transform.transforms

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

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

      a						:Union[str, Any]										=	prepare_img()
      a						:Tuple										=	transform(UpperCAmelCase_						).unsqueeze(0						)
      a						:Any										=	processor(UpperCAmelCase_			,  return_tensors='''pt'''						).pixel_values

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

      # verify logits
      with torch.no_grad():
            a						:Dict										=	model(UpperCAmelCase_						)
            a						:List[str]										=	outputs.logits

      print('''Predicted class:'''			,  logits.argmax(-1						).item()						)
      if base_model:
            a						:List[Any]										=	timm_model.forward_features(UpperCAmelCase_						)
            assert timm_pooled_output.shape == outputs.pooler_output.shape
            assert torch.allclose(UpperCAmelCase_			,  outputs.pooler_output			,  atol=1E-3						)
      else:
            a						:List[Any]										=	timm_model(UpperCAmelCase_						)
            assert timm_logits.shape == outputs.logits.shape
            assert torch.allclose(UpperCAmelCase_			,  outputs.logits			,  atol=1E-3						)
      print('''Looks ok!'''						)

      if pytorch_dump_folder_path is not None:
            Path(UpperCAmelCase_						).mkdir(exist_ok=UpperCAmelCase_						)
            print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''						)
            model.save_pretrained(UpperCAmelCase_						)
            print(F'''Saving processor to {pytorch_dump_folder_path}'''						)
            processor.save_pretrained(UpperCAmelCase_						)

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


if __name__ == "__main__":
     snake_case    :   Union[str, Any]	    =					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--vit_name''',
         default='''vit_base_r50_s16_384''',
         type=str,
         help='''Name of the hybrid ViT 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 upload the model to the HuggingFace hub.'''
     )

     snake_case    :   str	    =					parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)









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

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


snake_case    :   List[str]	    =					logging.get_logger(__name__)

snake_case    :   Optional[Any]	    =					{
    '''vocab_file''': '''vocab.txt''',
    '''merges_file''': '''bpe.codes''',
}

snake_case    :   str	    =					{
    '''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''',
    },
}

snake_case    :   List[Any]	    =					{
    '''vinai/phobert-base''': 2_56,
    '''vinai/phobert-large''': 2_56,
}
def     __lowerCamelCase						(			UpperCAmelCase_						:		List[str]						):




      """simple docstring"""





      a						:Union[str, Any]										=	set()
      a						:str										=	word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)						)
            a						:Optional[int]										=	char

      a						:Optional[int]										=	set(UpperCAmelCase_						)
      return pairs







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__				=	PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase="<s>" ,  _lowerCamelCase="</s>" ,  _lowerCamelCase="</s>" ,  _lowerCamelCase="<s>" ,  _lowerCamelCase="<unk>" ,  _lowerCamelCase="<pad>" ,  _lowerCamelCase="<mask>" ,  **_lowerCamelCase ,  ):
       super().__init__(
           bos_token=_lowerCamelCase ,  eos_token=_lowerCamelCase ,  unk_token=_lowerCamelCase ,  sep_token=_lowerCamelCase ,  cls_token=_lowerCamelCase ,  pad_token=_lowerCamelCase ,  mask_token=_lowerCamelCase ,  **_lowerCamelCase ,  )

       a						:Optional[Any]										=	vocab_file
       a						:Optional[Any]										=	merges_file

       a						:Any										=	{}
       a						:Any										=	0
       a						:int										=	1
       a						:Union[str, Any]										=	2
       a						:List[Any]										=	3

       self.add_from_file(_lowerCamelCase   )

       a						:List[str]										=	{v: k for k, v in self.encoder.items()}

       with open(_lowerCamelCase ,  encoding='''utf-8'''   ) as merges_handle:
             a						:List[str]										=	merges_handle.read().split('''\n'''   )[:-1]
       a						:Any										=	[tuple(merge.split()[:-1]   ) for merge in merges]
       a						:str										=	dict(zip(_lowerCamelCase ,  range(len(_lowerCamelCase   )   )   )   )
       a						:str										=	{}




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):

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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None ,  _lowerCamelCase = False   ):

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

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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       a						:Optional[int]										=	[self.sep_token_id]
       a						:Optional[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   ):
       return len(self.encoder   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return dict(self.encoder ,  **self.added_tokens_encoder   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       if token in self.cache:
             return self.cache[token]
       a						:Optional[int]										=	tuple(_lowerCamelCase   )
       a						:List[str]										=	tuple(list(word[:-1]   ) + [word[-1] + '''</w>''']   )
       a						:Union[str, Any]										=	get_pairs(_lowerCamelCase   )

       if not pairs:
             return token

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

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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:Union[str, Any]										=	[]

       a						:str										=	re.findall(R'''\S+\n?''' ,  _lowerCamelCase   )

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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       return self.encoder.get(_lowerCamelCase ,  self.encoder.get(self.unk_token   )   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       return self.decoder.get(_lowerCamelCase ,  self.unk_token   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:Optional[int]										=	''' '''.join(_lowerCamelCase   ).replace('''@@ ''' ,  ''''''   ).strip()
       return out_string




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       if not os.path.isdir(_lowerCamelCase   ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
             return
       a						:Tuple										=	os.path.join(
           _lowerCamelCase ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
       a						:Optional[int]										=	os.path.join(
           _lowerCamelCase ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']   )

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

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

       return out_vocab_file, out_merge_file




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

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





