
"""simple docstring"""




import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
A__   :					List[Any]							=   logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A__   :					Union[str, Any]							=   []
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
  )
  rename_keys.append(
      (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
  )
  rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
  rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
  rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
  rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
  rename_keys.append(
      (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
  )
  rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
  rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
  rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
  )
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
  )
  rename_keys.append(
      (
          f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
          f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
      )
  )
  rename_keys.append(
      (
          f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
          f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
      )
  )
  rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
  rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
  rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
  rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
  )
  rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
  )
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
  )
  rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
  rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.encoder.norm.weight', 'encoder.layernorm.weight'),
        ('transformer.encoder.norm.bias', 'encoder.layernorm.bias'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
    ]
)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Union[str, Any]       =     state_dict.pop(_UpperCamelCase						)
  _lowercase:			Dict       =     val




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Optional[Any]       =     OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      _lowercase:			Optional[int]       =     key.replace('''backbone.0.body''' ,				'''backbone.conv_encoder.model'''						)
      _lowercase:			Union[str, Any]       =     value
    else:
      _lowercase:			str       =     value

  return new_state_dict




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Dict       =     ''''''

  # first: transformer encoder
  for i in range(6						):
    # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
    _lowercase:			Optional[int]       =     state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''						)
    _lowercase:			List[Any]       =     state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''						)
    # next, add query, keys and values (in that order) to the state dict
    _lowercase:			Optional[Any]       =     in_proj_weight[:256, :]
    _lowercase:			Union[str, Any]       =     in_proj_bias[:256]
    _lowercase:			Optional[int]       =     in_proj_weight[256:512, :]
    _lowercase:			List[str]       =     in_proj_bias[256:512]
    _lowercase:			Union[str, Any]       =     in_proj_weight[-256:, :]
    _lowercase:			Dict       =     in_proj_bias[-256:]
  # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
  for i in range(6						):
    # read in weights + bias of input projection layer of self-attention
    _lowercase:			Any       =     state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''						)
    _lowercase:			str       =     state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''						)
    # next, add query, keys and values (in that order) to the state dict
    _lowercase:			int       =     in_proj_weight[:256, :]
    _lowercase:			Optional[int]       =     in_proj_bias[:256]
    _lowercase:			Tuple       =     in_proj_weight[256:512, :]
    _lowercase:			Any       =     in_proj_bias[256:512]
    _lowercase:			Union[str, Any]       =     in_proj_weight[-256:, :]
    _lowercase:			Union[str, Any]       =     in_proj_bias[-256:]
    # read in weights + bias of input projection layer of cross-attention
    _lowercase:			List[str]       =     state_dict.pop(
        f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''						)
    _lowercase:			Tuple       =     state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''						)
    # next, add query, keys and values (in that order) of cross-attention to the state dict
    _lowercase:			Any       =     in_proj_weight_cross_attn[:256, :]
    _lowercase:			List[str]       =     in_proj_bias_cross_attn[:256]
    _lowercase:			List[Any]       =     in_proj_weight_cross_attn[256:512, :]
    _lowercase:			Tuple       =     in_proj_bias_cross_attn[256:512]
    _lowercase:			int       =     in_proj_weight_cross_attn[-256:, :]
    _lowercase:			Optional[int]       =     in_proj_bias_cross_attn[-256:]




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase ,				_lowercase:			List[str]       =     image.size
  _lowercase:			Any       =     max(_UpperCamelCase ,				_UpperCamelCase						)
  _lowercase:			List[str]       =     800 if '''detection''' in checkpoint_url else 1_000
  _lowercase:			Union[str, Any]       =     target_max_size / current_max_size
  _lowercase:			Dict       =     image.resize((int(round(scale * width						)						), int(round(scale * height						)						))						)

  return resized_image




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			List[str]       =     F.to_tensor(_UpperCamelCase						)
  _lowercase:			List[str]       =     F.normalize(_UpperCamelCase ,				mean=[0.485, 0.456, 0.406] ,				std=[0.229, 0.224, 0.225]						)
  return image




@torch.no_grad()
def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  logger.info('''Converting model...'''						)

  # load original state dict
  _lowercase:			Union[str, Any]       =     torch.hub.load_state_dict_from_url(_UpperCamelCase ,				map_location='''cpu'''						)
  # rename keys
  for src, dest in rename_keys:
    rename_key(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						)
  _lowercase:			List[str]       =     rename_backbone_keys(_UpperCamelCase						)
  # query, key and value matrices need special treatment
  read_in_q_k_v(_UpperCamelCase						)
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  _lowercase:			Optional[int]       =     '''model.'''
  for key in state_dict.copy().keys():
    if not key.startswith('''class_labels_classifier'''						) and not key.startswith('''bbox_predictor'''						):
      _lowercase:			Any       =     state_dict.pop(_UpperCamelCase						)
      _lowercase:			Union[str, Any]       =     val
    # create HuggingFace model and load state dict
  _lowercase:			Dict       =     TableTransformerConfig(
      backbone='''resnet18''' ,				mask_loss_coefficient=1 ,				dice_loss_coefficient=1 ,				ce_loss_coefficient=1 ,				bbox_loss_coefficient=5 ,				giou_loss_coefficient=2 ,				eos_coefficient=0.4 ,				class_cost=1 ,				bbox_cost=5 ,				giou_cost=2 ,				)

  if "detection" in checkpoint_url:
    _lowercase:			Optional[Any]       =     15
    _lowercase:			int       =     2
    _lowercase:			str       =     {0: '''table''', 1: '''table rotated'''}
    _lowercase:			Union[str, Any]       =     idalabel
    _lowercase:			List[str]       =     {v: k for k, v in idalabel.items()}
  else:
    _lowercase:			int       =     125
    _lowercase:			Union[str, Any]       =     6
    _lowercase:			List[str]       =     {
        0: '''table''',
        1: '''table column''',
        2: '''table row''',
        3: '''table column header''',
        4: '''table projected row header''',
        5: '''table spanning cell''',
    }
    _lowercase:			Optional[int]       =     idalabel
    _lowercase:			Tuple       =     {v: k for k, v in idalabel.items()}

  _lowercase:			str       =     DetrImageProcessor(
      format='''coco_detection''' ,				max_size=800 if '''detection''' in checkpoint_url else 1_000						)
  _lowercase:			Dict       =     TableTransformerForObjectDetection(_UpperCamelCase						)
  model.load_state_dict(_UpperCamelCase						)
  model.eval()

  # verify our conversion
  _lowercase:			Union[str, Any]       =     '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
  _lowercase:			Optional[Any]       =     hf_hub_download(repo_id='''nielsr/example-pdf''' ,				repo_type='''dataset''' ,				filename=_UpperCamelCase						)
  _lowercase:			List[str]       =     Image.open(_UpperCamelCase						).convert('''RGB'''						)
  _lowercase:			int       =     normalize(resize(_UpperCamelCase ,				_UpperCamelCase						)						).unsqueeze(0						)

  _lowercase:			Union[str, Any]       =     model(_UpperCamelCase						)

  if "detection" in checkpoint_url:
    _lowercase:			int       =     (1, 15, 3)
    _lowercase:			str       =     torch.tensor(
        [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]						)
    _lowercase:			Dict       =     torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]						)

  else:
    _lowercase:			Union[str, Any]       =     (1, 125, 7)
    _lowercase:			str       =     torch.tensor(
        [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]						)
    _lowercase:			Tuple       =     torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]						)

  assert outputs.logits.shape == expected_shape
  assert torch.allclose(outputs.logits[0, :3, :3] ,				_UpperCamelCase ,				atol=1e-4						)
  assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,				_UpperCamelCase ,				atol=1e-4						)
  print('''Looks ok!'''						)

  if pytorch_dump_folder_path is not None:
    # Save model and image processor
    logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''						)
    Path(_UpperCamelCase						).mkdir(exist_ok=_UpperCamelCase						)
    model.save_pretrained(_UpperCamelCase						)
    image_processor.save_pretrained(_UpperCamelCase						)

  if push_to_hub:
    # Push model to HF hub
    logger.info('''Pushing model to the hub...'''						)
    _lowercase:			int       =     (
        '''microsoft/table-transformer-detection'''
        if '''detection''' in checkpoint_url
        else '''microsoft/table-transformer-structure-recognition'''
    )
    model.push_to_hub(_UpperCamelCase						)
    image_processor.push_to_hub(_UpperCamelCase						)


if __name__ == "__main__":
  A__   :					Union[str, Any]							=   argparse.ArgumentParser()

  parser.add_argument(
      '--checkpoint_url',
      default='https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
      type=str,
      choices=[
          'https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
          'https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth',
      ],
      help='URL of the Table Transformer checkpoint you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
  )
  parser.add_argument(
      '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
  )
  A__   :					List[str]							=   parser.parse_args()
  convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


"""simple docstring"""




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

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


A__   :					str							=   logging.get_logger(__name__)

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

A__   :					List[Any]							=   {
    '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__   :					Any							=   {
    'vinai/phobert-base': 2_5_6,
    'vinai/phobert-large': 2_5_6,
}




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



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

  _lowercase:			Optional[Any]       =     set(_UpperCamelCase						)
  return pairs







class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		VOCAB_FILES_NAMES
       UpperCamelCase_   =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       def __init__(     self ,				A_ ,				A_ ,				A_="<s>" ,				A_="</s>" ,				A_="</s>" ,				A_="<s>" ,				A_="<unk>" ,				A_="<pad>" ,				A_="<mask>" ,				**A_ ,				)     ->	Dict:





         """simple docstring"""




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

         _lowercase:			List[Any]       =     vocab_file
         _lowercase:			Union[str, Any]       =     merges_file

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

         self.add_from_file(A_       )

         _lowercase:			Tuple       =     {v: k for k, v in self.encoder.items()}

         with open(A_ ,				encoding='''utf-8'''       ) as merges_handle:
           _lowercase:			int       =     merges_handle.read().split('''\n'''       )[:-1]
         _lowercase:			Optional[int]       =     [tuple(merge.split()[:-1]       ) for merge in merges]
         _lowercase:			List[str]       =     dict(zip(A_ ,				range(len(A_       )       )       )       )
         _lowercase:			Union[str, Any]       =     {}
       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	List[int]:





         """simple docstring"""




         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         _lowercase:			Optional[Any]       =     [self.cls_token_id]
         _lowercase:			Tuple       =     [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep
       def 							lowercase_							(     self ,				A_ ,				A_ = None ,				A_ = False       )     ->	List[int]:





         """simple docstring"""




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

         if token_ids_a is None:
           return [1] + ([0] * len(A_       )) + [1]
         return [1] + ([0] * len(A_       )) + [1, 1] + ([0] * len(A_       )) + [1]
       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	List[int]:





         """simple docstring"""




         _lowercase:			Tuple       =     [self.sep_token_id]
         _lowercase:			List[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 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         return len(self.encoder       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         return dict(self.encoder ,				**self.added_tokens_encoder       )
       def 							lowercase_							(     self ,				A_       )     ->	Optional[Any]:





         """simple docstring"""




         if token in self.cache:
           return self.cache[token]
         _lowercase:			List[Any]       =     tuple(A_       )
         _lowercase:			Tuple       =     tuple(list(word[:-1]       ) + [word[-1] + '''</w>''']       )
         _lowercase:			List[Any]       =     get_pairs(A_       )

         if not pairs:
           return token

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

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





         """simple docstring"""




         _lowercase:			Dict       =     []

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

         for token in words:
           split_tokens.extend(list(self.bpe(A_       ).split(''' '''       )       )       )
         return split_tokens
       def 							lowercase_							(     self ,				A_       )     ->	List[str]:





         """simple docstring"""




         return self.encoder.get(A_ ,				self.encoder.get(self.unk_token       )       )
       def 							lowercase_							(     self ,				A_       )     ->	Tuple:





         """simple docstring"""




         return self.decoder.get(A_ ,				self.unk_token       )
       def 							lowercase_							(     self ,				A_       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     ''' '''.join(A_       ).replace('''@@ ''' ,				''''''       ).strip()
         return out_string
       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	Tuple[str]:





         """simple docstring"""




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

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

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

         return out_vocab_file, out_merge_file



       def 							lowercase_							(     self ,				A_       )     ->	str:





         """simple docstring"""




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

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