


import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase			=							logging.get_logger(__name__)




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    Any:
       lowercase__:       Dict        =       os.path.abspath(snake_case					)
       logger.info(f'Converting TensorFlow checkpoint from {tf_path}'					)
       # Load weights from TF model
       lowercase__:       Optional[Any]        =       tf.train.list_variables(snake_case					)
       lowercase__:       List[Any]        =       []
       lowercase__:       Tuple        =       []
       lowercase__:       int        =       []
       for full_name, shape in init_vars:
              # logger.info(f"Loading TF weight {name} with shape {shape}")
              lowercase__:       Union[str, Any]        =       full_name.split('/'					)
              if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
                     logger.info(f'Skipping non-model layer {full_name}'					)
                     continue
              if "optimizer" in full_name:
                     logger.info(f'Skipping optimization layer {full_name}'					)
                     continue
              if name[0] == "model":
                     # ignore initial 'model'
                     lowercase__:       str        =       name[1:]
              # figure out how many levels deep the name is
              lowercase__:       Optional[Any]        =       0
              for _name in name:
                     if _name.startswith('layer_with_weights'					):
                            depth += 1
                     else:
                            break
              layer_depth.append(snake_case					)
              # read data
              lowercase__:       Optional[Any]        =       tf.train.load_variable(snake_case      ,							snake_case					)
              names.append('/'.join(snake_case					)					)
              arrays.append(snake_case					)
       logger.info(f'Read a total of {len(snake_case					):,} layers'					)

       # Sanity check
       if len(set(snake_case					)					) != 1:
              raise ValueError(f'Found layer names with different depths (layer depth {list(set(snake_case					)					)})'					)
       lowercase__:       Any        =       list(set(snake_case					)					)[0]
       if layer_depth != 1:
              raise ValueError(
                  'The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'
                  ' heads.'					)

       # convert layers
       logger.info('Converting weights...'					)
       for full_name, array in zip(snake_case      ,							snake_case					):
              lowercase__:       Optional[int]        =       full_name.split('/'					)
              lowercase__:       List[Any]        =       model
              lowercase__:       Any        =       []
              for i, m_name in enumerate(snake_case					):
                     if m_name == ".ATTRIBUTES":
                            # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                            break
                     if m_name.startswith('layer_with_weights'					):
                            lowercase__:       Any        =       int(m_name.split('-'					)[-1]					)
                            if layer_num <= 2:
                                   # embedding layers
                                   # layer_num 0: word_embeddings
                                   # layer_num 1: position_embeddings
                                   # layer_num 2: token_type_embeddings
                                   continue
                            elif layer_num == 3:
                                   # embedding LayerNorm
                                   trace.extend(['embeddings', 'LayerNorm']					)
                                   lowercase__:       str        =       getattr(snake_case      ,							'embeddings'					)
                                   lowercase__:       int        =       getattr(snake_case      ,							'LayerNorm'					)
                            elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                                   # encoder layers
                                   trace.extend(['encoder', 'layer', str(layer_num - 4					)]					)
                                   lowercase__:       int        =       getattr(snake_case      ,							'encoder'					)
                                   lowercase__:       List[str]        =       getattr(snake_case      ,							'layer'					)
                                   lowercase__:       Union[str, Any]        =       pointer[layer_num - 4]
                            elif layer_num == config.num_hidden_layers + 4:
                                   # pooler layer
                                   trace.extend(['pooler', 'dense']					)
                                   lowercase__:       Tuple        =       getattr(snake_case      ,							'pooler'					)
                                   lowercase__:       Tuple        =       getattr(snake_case      ,							'dense'					)
                     elif m_name == "embeddings":
                            trace.append('embeddings'					)
                            lowercase__:       Union[str, Any]        =       getattr(snake_case      ,							'embeddings'					)
                            if layer_num == 0:
                                   trace.append('word_embeddings'					)
                                   lowercase__:       Union[str, Any]        =       getattr(snake_case      ,							'word_embeddings'					)
                            elif layer_num == 1:
                                   trace.append('position_embeddings'					)
                                   lowercase__:       Dict        =       getattr(snake_case      ,							'position_embeddings'					)
                            elif layer_num == 2:
                                   trace.append('token_type_embeddings'					)
                                   lowercase__:       Optional[Any]        =       getattr(snake_case      ,							'token_type_embeddings'					)
                            else:
                                   raise ValueError(f'Unknown embedding layer with name {full_name}'					)
                            trace.append('weight'					)
                            lowercase__:       List[str]        =       getattr(snake_case      ,							'weight'					)
                     elif m_name == "_attention_layer":
                            # self-attention layer
                            trace.extend(['attention', 'self']					)
                            lowercase__:       int        =       getattr(snake_case      ,							'attention'					)
                            lowercase__:       Union[str, Any]        =       getattr(snake_case      ,							'self'					)
                     elif m_name == "_attention_layer_norm":
                            # output attention norm
                            trace.extend(['attention', 'output', 'LayerNorm']					)
                            lowercase__:       str        =       getattr(snake_case      ,							'attention'					)
                            lowercase__:       Optional[Any]        =       getattr(snake_case      ,							'output'					)
                            lowercase__:       List[Any]        =       getattr(snake_case      ,							'LayerNorm'					)
                     elif m_name == "_attention_output_dense":
                            # output attention dense
                            trace.extend(['attention', 'output', 'dense']					)
                            lowercase__:       Optional[Any]        =       getattr(snake_case      ,							'attention'					)
                            lowercase__:       Optional[int]        =       getattr(snake_case      ,							'output'					)
                            lowercase__:       Optional[Any]        =       getattr(snake_case      ,							'dense'					)
                     elif m_name == "_output_dense":
                            # output dense
                            trace.extend(['output', 'dense']					)
                            lowercase__:       Union[str, Any]        =       getattr(snake_case      ,							'output'					)
                            lowercase__:       List[Any]        =       getattr(snake_case      ,							'dense'					)
                     elif m_name == "_output_layer_norm":
                            # output dense
                            trace.extend(['output', 'LayerNorm']					)
                            lowercase__:       Any        =       getattr(snake_case      ,							'output'					)
                            lowercase__:       str        =       getattr(snake_case      ,							'LayerNorm'					)
                     elif m_name == "_key_dense":
                            # attention key
                            trace.append('key'					)
                            lowercase__:       Tuple        =       getattr(snake_case      ,							'key'					)
                     elif m_name == "_query_dense":
                            # attention query
                            trace.append('query'					)
                            lowercase__:       List[str]        =       getattr(snake_case      ,							'query'					)
                     elif m_name == "_value_dense":
                            # attention value
                            trace.append('value'					)
                            lowercase__:       Optional[int]        =       getattr(snake_case      ,							'value'					)
                     elif m_name == "_intermediate_dense":
                            # attention intermediate dense
                            trace.extend(['intermediate', 'dense']					)
                            lowercase__:       Any        =       getattr(snake_case      ,							'intermediate'					)
                            lowercase__:       str        =       getattr(snake_case      ,							'dense'					)
                     elif m_name == "_output_layer_norm":
                            # output layer norm
                            trace.append('output'					)
                            lowercase__:       Union[str, Any]        =       getattr(snake_case      ,							'output'					)
                     # weights & biases
                     elif m_name in ["bias", "beta"]:
                            trace.append('bias'					)
                            lowercase__:       str        =       getattr(snake_case      ,							'bias'					)
                     elif m_name in ["kernel", "gamma"]:
                            trace.append('weight'					)
                            lowercase__:       Tuple        =       getattr(snake_case      ,							'weight'					)
                     else:
                            logger.warning(f'Ignored {m_name}'					)
        # for certain layers reshape is necessary
              lowercase__:       Any        =       '.'.join(snake_case					)
              if re.match(R'(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'      ,							snake_case					) or re.match(
                  R'(\S+)\.attention\.output\.dense\.weight'      ,							snake_case					):
                     lowercase__:       Union[str, Any]        =       array.reshape(pointer.data.shape					)
              if "kernel" in full_name:
                     lowercase__:       str        =       array.transpose()
              if pointer.shape == array.shape:
                     lowercase__:       Union[str, Any]        =       torch.from_numpy(snake_case					)
              else:
                     raise ValueError(
                         f'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
                         f' {array.shape}'					)
              logger.info(f'Successfully set variable {full_name} to PyTorch layer {trace}'					)
       return model




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    Any:
       # Instantiate model
       logger.info(f'Loading model based on config from {config_path}...'					)
       lowercase__:       int        =       BertConfig.from_json_file(snake_case					)
       lowercase__:       Tuple        =       BertModel(snake_case					)

       # Load weights from checkpoint
       logger.info(f'Loading weights from checkpoint {tf_checkpoint_path}...'					)
       load_tfa_weights_in_bert(snake_case      ,							snake_case      ,							snake_case					)

       # Save pytorch-model
       logger.info(f'Saving PyTorch model to {pytorch_dump_path}...'					)
       torch.save(model.state_dict()      ,							snake_case					)


if __name__ == "__main__":
 __lowerCAmelCase			=							argparse.ArgumentParser()
 parser.add_argument(
     '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow 2.x checkpoint path.'''
 )
 parser.add_argument(
     '''--bert_config_file''',
     type=str,
     required=True,
     help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''',
     type=str,
     required=True,
     help='''Path to the output PyTorch model (must include filename).''',
 )
 __lowerCAmelCase			=							parser.parse_args()
 convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


__lowerCAmelCase			=							logging.get_logger(__name__)

__lowerCAmelCase			=							'''▁'''

__lowerCAmelCase			=							{'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

__lowerCAmelCase			=							{
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

__lowerCAmelCase			=							{'''vinai/bartpho-syllable''': 10_24}





class   __a      (   __UpperCamelCase				):
 __lowercase			:							int       					=			VOCAB_FILES_NAMES
 __lowercase			:							str       					=			PRETRAINED_VOCAB_FILES_MAP
 __lowercase			:							str       					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase			:							Optional[int]       					=			['input_ids', 'attention_mask']
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__="<s>"    ,			lowerCAmelCase__="</s>"    ,			lowerCAmelCase__="</s>"    ,			lowerCAmelCase__="<s>"    ,			lowerCAmelCase__="<unk>"    ,			lowerCAmelCase__="<pad>"    ,			lowerCAmelCase__="<mask>"    ,			lowerCAmelCase__ = None    ,			**lowerCAmelCase__    ,			) ->    None:




        '''simple docstring'''
        # Mask token behave like a normal word, i.e. include the space before it
        lowercase__:       List[str]        =       AddedToken(lowerCAmelCase__    ,			lstrip=lowerCAmelCase__    ,			rstrip=lowerCAmelCase__			) if isinstance(lowerCAmelCase__    ,			lowerCAmelCase__			) else mask_token

        lowercase__:       Any        =       {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=lowerCAmelCase__    ,			eos_token=lowerCAmelCase__    ,			unk_token=lowerCAmelCase__    ,			sep_token=lowerCAmelCase__    ,			cls_token=lowerCAmelCase__    ,			pad_token=lowerCAmelCase__    ,			mask_token=lowerCAmelCase__    ,			sp_model_kwargs=self.sp_model_kwargs    ,			**lowerCAmelCase__    ,			)

        lowercase__:       Dict        =       vocab_file
        lowercase__:       str        =       monolingual_vocab_file
        lowercase__:       int        =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(str(lowerCAmelCase__			)			)

        # Load the reduced vocab

        # Keep order of special tokens for backward compatibility
        lowercase__:       List[Any]        =       {}
        lowercase__:       Optional[int]        =       0
        for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
               if str(lowerCAmelCase__			) not in self.fairseq_tokens_to_ids:
                      lowercase__:       str        =       cnt
                      cnt += 1
        with open(lowerCAmelCase__    ,			'r'    ,			encoding='utf-8'			) as f:
               for line in f.readlines():
                      lowercase__:       Optional[Any]        =       line.strip().split()[0]
                      lowercase__:       Optional[Any]        =       len(self.fairseq_tokens_to_ids			)
        if str(lowerCAmelCase__			) not in self.fairseq_tokens_to_ids:
               lowercase__:       Optional[int]        =       len(self.fairseq_tokens_to_ids			)

        lowercase__:       Optional[Any]        =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def __getstate__(       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Tuple        =       self.__dict__.copy()
        lowercase__:       Tuple        =       None
        lowercase__:       Optional[Any]        =       self.sp_model.serialized_model_proto()
        return state
 def __setstate__(       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       d

        # for backward compatibility
        if not hasattr(self    ,			'sp_model_kwargs'			):
               lowercase__:       Union[str, Any]        =       {}

        lowercase__:       List[Any]        =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = 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[int]        =       [self.cls_token_id]
        lowercase__:       Union[str, Any]        =       [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			) ->    List[int]:




        '''simple docstring'''

        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			) ->    List[int]:




        '''simple docstring'''
        lowercase__:       Dict        =       [self.sep_token_id]
        lowercase__:       Dict        =       [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			) ->    Tuple:




        '''simple docstring'''
        return len(self.fairseq_ids_to_tokens			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       {self.convert_ids_to_tokens(lowerCAmelCase__			): i for i in range(self.vocab_size			)}
        vocab.update(self.added_tokens_encoder			)
        return vocab
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    List[str]:




        '''simple docstring'''
        return self.sp_model.encode(lowerCAmelCase__    ,			out_type=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
        else:
               return self.unk_token_id
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        return self.fairseq_ids_to_tokens[index]
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       ''.join(lowerCAmelCase__			).replace(lowerCAmelCase__    ,			' '			).strip()
        return out_string
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    Tuple[str]:




        '''simple docstring'''
        if not os.path.isdir(lowerCAmelCase__			):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
               return
        lowercase__:       int        =       os.path.join(
            lowerCAmelCase__    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
        lowercase__:       List[str]        =       os.path.join(
            lowerCAmelCase__    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file']    ,			)

        if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCAmelCase__			) and os.path.isfile(self.vocab_file			):
               copyfile(self.vocab_file    ,			lowerCAmelCase__			)
        elif not os.path.isfile(self.vocab_file			):
               with open(lowerCAmelCase__    ,			'wb'			) as fi:
                      lowercase__:       Optional[Any]        =       self.sp_model.serialized_model_proto()
                      fi.write(lowerCAmelCase__			)

        if os.path.abspath(self.monolingual_vocab_file			) != os.path.abspath(
            lowerCAmelCase__			) and os.path.isfile(self.monolingual_vocab_file			):
               copyfile(self.monolingual_vocab_file    ,			lowerCAmelCase__			)
        elif not os.path.isfile(self.monolingual_vocab_file			):
               with open(lowerCAmelCase__    ,			'w'    ,			encoding='utf-8'			) as fp:
                      for token in self.fairseq_tokens_to_ids:
                             if token not in self.all_special_tokens:
                                    fp.write(F'{str(lowerCAmelCase__			)} \n'			)

        return out_vocab_file, out_monolingual_vocab_file


