

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()
snake_case_        =			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case_        =			[]
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      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   List[Any]      ):



     '''simple docstring'''





     lowercase__   :       Union[str, Any]				=	state_dict.pop(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Tuple				=	val

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Dict      ):



     '''simple docstring'''





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

     return new_state_dict

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[str]      ):



     '''simple docstring'''





     lowercase__   :       int				=	''

     # 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__   :       List[str]				=	state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""      )
          lowercase__   :       Optional[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__   :       Dict				=	in_proj_weight[:256, :]
          lowercase__   :       str				=	in_proj_bias[:256]
          lowercase__   :       Union[str, Any]				=	in_proj_weight[256:512, :]
          lowercase__   :       Tuple				=	in_proj_bias[256:512]
          lowercase__   :       Any				=	in_proj_weight[-256:, :]
          lowercase__   :       List[str]				=	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__   :       Optional[int]				=	state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""      )
          lowercase__   :       List[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__   :       Optional[Any]				=	in_proj_weight[:256, :]
          lowercase__   :       Optional[Any]				=	in_proj_bias[:256]
          lowercase__   :       Dict				=	in_proj_weight[256:512, :]
          lowercase__   :       Optional[int]				=	in_proj_bias[256:512]
          lowercase__   :       List[Any]				=	in_proj_weight[-256:, :]
          lowercase__   :       List[str]				=	in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          lowercase__   :       Any				=	state_dict.pop(
              f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""      )
          lowercase__   :       str				=	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__   :       Tuple				=	in_proj_weight_cross_attn[:256, :]
          lowercase__   :       Union[str, Any]				=	in_proj_bias_cross_attn[:256]
          lowercase__   :       Optional[Any]				=	in_proj_weight_cross_attn[256:512, :]
          lowercase__   :       int				=	in_proj_bias_cross_attn[256:512]
          lowercase__   :       Tuple				=	in_proj_weight_cross_attn[-256:, :]
          lowercase__   :       List[Any]				=	in_proj_bias_cross_attn[-256:]

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]      ):



     '''simple docstring'''





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

     return resized_image

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Dict      ):



     '''simple docstring'''





     lowercase__   :       str				=	F.to_tensor(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       int				=	F.normalize(SCREAMING_SNAKE_CASE_     ,					mean=[0.485, 0.456, 0.406]     ,					std=[0.229, 0.224, 0.225]      )
     return image

@torch.no_grad()
def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Dict     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]      ):



     '''simple docstring'''





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

     # load original state dict
     lowercase__   :       Optional[int]				=	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_     ,					map_location='cpu'      )
     # rename keys
     for src, dest in rename_keys:
          rename_key(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Union[str, Any]				=	rename_backbone_keys(SCREAMING_SNAKE_CASE_      )
     # query, key and value matrices need special treatment
     read_in_q_k_v(SCREAMING_SNAKE_CASE_      )
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     lowercase__   :       List[Any]				=	'model.'
     for key in state_dict.copy().keys():
          if not key.startswith('class_labels_classifier'      ) and not key.startswith('bbox_predictor'      ):
               lowercase__   :       Dict				=	state_dict.pop(SCREAMING_SNAKE_CASE_      )
               lowercase__   :       Union[str, Any]				=	val
    # create HuggingFace model and load state dict
     lowercase__   :       Union[str, Any]				=	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__   :       Any				=	15
          lowercase__   :       List[Any]				=	2
          lowercase__   :       Any				=	{0: 'table', 1: 'table rotated'}
          lowercase__   :       Tuple				=	idalabel
          lowercase__   :       Dict				=	{v: k for k, v in idalabel.items()}
     else:
          lowercase__   :       Tuple				=	125
          lowercase__   :       int				=	6
          lowercase__   :       Any				=	{
              0: 'table',
              1: 'table column',
              2: 'table row',
              3: 'table column header',
              4: 'table projected row header',
              5: 'table spanning cell',
          }
          lowercase__   :       List[Any]				=	idalabel
          lowercase__   :       Any				=	{v: k for k, v in idalabel.items()}

     lowercase__   :       List[Any]				=	DetrImageProcessor(
         format='coco_detection'     ,					max_size=800 if 'detection' in checkpoint_url else 1_000      )
     lowercase__   :       Tuple				=	TableTransformerForObjectDetection(SCREAMING_SNAKE_CASE_      )
     model.load_state_dict(SCREAMING_SNAKE_CASE_      )
     model.eval()

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

     lowercase__   :       Tuple				=	model(SCREAMING_SNAKE_CASE_      )

     if "detection" in checkpoint_url:
          lowercase__   :       int				=	(1, 15, 3)
          lowercase__   :       Dict				=	torch.tensor(
              [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]      )
          lowercase__   :       Optional[Any]				=	torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]      )

     else:
          lowercase__   :       Optional[int]				=	(1, 125, 7)
          lowercase__   :       Tuple				=	torch.tensor(
              [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]      )
          lowercase__   :       Optional[Any]				=	torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]]      )

     assert outputs.logits.shape == expected_shape
     assert torch.allclose(outputs.logits[0, :3, :3]     ,					SCREAMING_SNAKE_CASE_     ,					atol=1E-4      )
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,					SCREAMING_SNAKE_CASE_     ,					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(SCREAMING_SNAKE_CASE_      ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_      )
          model.save_pretrained(SCREAMING_SNAKE_CASE_      )
          image_processor.save_pretrained(SCREAMING_SNAKE_CASE_      )

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


if __name__ == "__main__":
       snake_case_        =			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.'''
       )
       snake_case_        =			parser.parse_args()
       convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




import json
import os
from typing import Dict, List, Optional, Tuple

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


snake_case_        =			logging.get_logger(__name__)


snake_case_        =			{
    '''vocab_file''': '''vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
    '''merges_file''': '''merges.txt''',
}

snake_case_        =			{
    '''vocab_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
        ),
    },
    '''tokenizer_config_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
        ),
    },
    '''merges_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
        ),
    },
}

snake_case_        =			'''</w>'''
snake_case_        =			'''@@ '''

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]      ):



     '''simple docstring'''





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


# Speech2Text2 has no max input length
snake_case_        =			{'''facebook/s2t-wav2vec2-large-en-de''': 1_024}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[str]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	int														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	Dict														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	Union[str, Any]														=					["""input_ids""", """attention_mask"""]





    def __init__(				self						,       a						,       a="<s>"						,       a="<pad>"						,       a="</s>"						,       a="<unk>"						,       a=False						,       a=None						,       **a						,       ):
         super().__init__(
             unk_token=a						,       bos_token=a						,       eos_token=a						,       pad_token=a						,       do_lower_case=a						,       **a						,       )

         lowercase__   :       str				=	do_lower_case

         with open(a						,       encoding='utf-8') as vocab_handle:
              lowercase__   :       Tuple				=	json.load(a)
         lowercase__   :       Union[str, Any]				=	{v: k for k, v in self.encoder.items()}

         if merges_file is None:
              logger.info(f"""No merges files provided. {self.__class__.__name__} can only be used for decoding.""")

              lowercase__   :       int				=	None
              lowercase__   :       List[Any]				=	None
         else:
              with open(a						,       encoding='utf-8') as merges_handle:
                   lowercase__   :       List[Any]				=	merges_handle.read().split('\n')[:-1]

              lowercase__   :       Optional[int]				=	[tuple(merge.split()[:2]) for merge in merges]
              lowercase__   :       Tuple				=	dict(zip(a						,       range(len(a))))
              lowercase__   :       List[str]				=	{}





    @property
    def 				snake_case_    (				self):
         return len(self.decoder)





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





    def 				snake_case_    (				self						,       a):
         lowercase__   :       int				=	tuple(token[:-1]) + (token[-1] + BPE_TOKEN_MERGES,)
         if token in self.cache:
              return self.cache[token]
         lowercase__   :       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__   :       Dict				=	bigram
              lowercase__   :       Union[str, Any]				=	[]
              lowercase__   :       int				=	0
              while i < len(a):
                   try:
                        lowercase__   :       Dict				=	word.index(a						,       a)
                   except ValueError:
                        new_word.extend(word[i:])
                        break
                   else:
                        new_word.extend(word[i:j])
                        lowercase__   :       Optional[int]				=	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__   :       str				=	tuple(a)
              lowercase__   :       Union[str, Any]				=	new_word
              if len(a) == 1:
                   break
              else:
                   lowercase__   :       Optional[Any]				=	get_pairs(a)
         lowercase__   :       List[str]				=	' '.join(a)
         if word == "\n  " + BPE_TOKEN_MERGES:
              lowercase__   :       Optional[int]				=	'\n' + BPE_TOKEN_MERGES

         if word.endswith(a):
              lowercase__   :       Dict				=	word.replace(a						,       '')

         lowercase__   :       int				=	word.replace(' '						,       a)
         lowercase__   :       List[str]				=	word
         return word





    def 				snake_case_    (				self						,       a):

         if self.bpe_ranks is None:
              raise ValueError(
                  'This tokenizer was instantiated without a `merges.txt` file, so'
                  ' that it can only be used for decoding, not for encoding.'
                  'Make sure to provide `merges.txt` file at instantiation to enable '
                  'encoding.')

         if self.do_lower_case:
              lowercase__   :       int				=	text.lower()

         lowercase__   :       Optional[int]				=	text.split()

         lowercase__   :       Optional[int]				=	[]
         for token in text:
              if token:
                   split_tokens.extend(list(self.bpe(a).split(' ')))

         return split_tokens





    def 				snake_case_    (				self						,       a):
         return self.encoder.get(a						,       self.encoder.get(self.unk_token))





    def 				snake_case_    (				self						,       a):
         lowercase__   :       Union[str, Any]				=	self.decoder.get(a						,       self.unk_token)
         return result





    def 				snake_case_    (				self						,       a):
         lowercase__   :       Union[str, Any]				=	' '.join(a)

         # make sure @@ tokens are concatenated
         lowercase__   :       Optional[int]				=	''.join(string.split(a))

         return string





    def 				snake_case_    (				self						,       a						,       a = None):
         if not os.path.isdir(a):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
              return
         lowercase__   :       Optional[Any]				=	os.path.join(
             a						,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
         lowercase__   :       List[Any]				=	os.path.join(
             a						,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

         with open(a						,       'w'						,       encoding='utf-8') as f:
              f.write(json.dumps(self.encoder						,       indent=2						,       sort_keys=a						,       ensure_ascii=a) + '\n')

         lowercase__   :       Optional[Any]				=	0
         if self.bpe_ranks is None:
              return (vocab_file,)

         with open(a						,       'w'						,       encoding='utf-8') as writer:
              for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,       key=lambda a: kv[1]):
                   if index != token_index:
                        logger.warning(
                            f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
                            ' Please check that the tokenizer is not corrupted!')
                        lowercase__   :       Dict				=	token_index
                   writer.write(' '.join(a) + '\n')
                   index += 1

         return (vocab_file, merges_file)

