'''simple docstring'''

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

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


lowerCAmelCase_			:			Optional[int]    	=   logging.get_logger(__name__)

lowerCAmelCase_			:			Union[str, Any]    	=   {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

lowerCAmelCase_			:			List[str]    	=   {
    "vocab_file": {
        "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json",
    },
    "merges_file": {"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"},
}

lowerCAmelCase_			:			Tuple    	=   {
    "microsoft/biogpt": 1_024,
}


def __a			(						__lowerCamelCase      :    Any					)						->				Optional[int]:
      '''simple docstring'''


      lowercase_     			=   set()
      lowercase_     			=   word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)					)
            lowercase_     			=   char
      return pairs






class 				lowercase    (    a__			):
   lowerCamelCase_            =VOCAB_FILES_NAMES
   lowerCamelCase_            =PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_            =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_            =['input_ids', 'attention_mask']


   def __init__( self					:						int ,		__lowerCAmelCase					:						Any ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						Dict="<unk>" ,		__lowerCAmelCase					:						Any="<s>" ,		__lowerCAmelCase					:						Dict="</s>" ,		__lowerCAmelCase					:						List[str]="</s>" ,		__lowerCAmelCase					:						int="<pad>" ,		**__lowerCAmelCase					:						List[str] ,		)   ->       Tuple:
         super().__init__(
             bos_token=__UpperCAmelCase ,		eos_token=__UpperCAmelCase ,		sep_token=__UpperCAmelCase ,		unk_token=__UpperCAmelCase ,		pad_token=__UpperCAmelCase ,		**__UpperCAmelCase ,		)

         try:
               import sacremoses
         except ImportError:
               raise ImportError(
                   "You need to install sacremoses to use BioGptTokenizer. "
                   "See https://pypi.org/project/sacremoses/ for installation.")

         lowercase_     			=   "en"
         lowercase_     			=   sacremoses
         # cache of sm.MosesTokenizer instance
         lowercase_     			=   {}
         lowercase_     			=   {}
         with open(__UpperCAmelCase ,		encoding="utf-8") as vocab_handle:
               lowercase_     			=   json.load(__UpperCAmelCase)
         lowercase_     			=   {v: k for k, v in self.encoder.items()}
         with open(__UpperCAmelCase ,		encoding="utf-8") as merges_handle:
               lowercase_     			=   merges_handle.read().split("\n")[:-1]
         lowercase_     			=   [tuple(merge.split()[:2]) for merge in merges]
         lowercase_     			=   dict(zip(__UpperCAmelCase ,		range(len(__UpperCAmelCase))))
         lowercase_     			=   {}


   @property
   def __UpperCAmelCase							( self					:						int)   ->       Optional[Any]:
         return len(self.encoder)


   def __UpperCAmelCase							( self					:						Optional[int])   ->       Dict:
         return dict(self.encoder ,		**self.added_tokens_encoder)


   def __UpperCAmelCase							( self					:						Tuple ,		__lowerCAmelCase					:						Tuple ,		__lowerCAmelCase					:						Optional[int])   ->       Optional[Any]:
         if lang not in self.cache_moses_tokenizer:
               lowercase_     			=   self.sm.MosesTokenizer(lang=__UpperCAmelCase)
               lowercase_     			=   moses_tokenizer
         return self.cache_moses_tokenizer[lang].tokenize(
             __UpperCAmelCase ,		aggressive_dash_splits=__UpperCAmelCase ,		return_str=__UpperCAmelCase ,		escape=__UpperCAmelCase)


   def __UpperCAmelCase							( self					:						Tuple ,		__lowerCAmelCase					:						Dict ,		__lowerCAmelCase					:						List[str])   ->       Any:
         if lang not in self.cache_moses_detokenizer:
               lowercase_     			=   self.sm.MosesDetokenizer(lang=__UpperCAmelCase)
               lowercase_     			=   moses_detokenizer
         return self.cache_moses_detokenizer[lang].detokenize(__UpperCAmelCase)


   def __UpperCAmelCase							( self					:						List[Any] ,		__lowerCAmelCase					:						int)   ->       Union[str, Any]:
         lowercase_     			=   tuple(token[:-1]) + (token[-1] + "</w>",)
         if token in self.cache:
               return self.cache[token]
         lowercase_     			=   get_pairs(__UpperCAmelCase)

         if not pairs:
               return token + "</w>"

         while True:
               lowercase_     			=   min(__UpperCAmelCase ,		key=lambda __lowerCAmelCase: self.bpe_ranks.get(__UpperCAmelCase ,		float("inf")))
               if bigram not in self.bpe_ranks:
                     break
               lowercase_			,				lowercase_     			=   bigram
               lowercase_     			=   []
               lowercase_     			=   0
               while i < len(__UpperCAmelCase):
                     try:
                           lowercase_     			=   word.index(__UpperCAmelCase ,		__UpperCAmelCase)
                     except ValueError:
                           new_word.extend(word[i:])
                           break
                     else:
                           new_word.extend(word[i:j])
                           lowercase_     			=   j

                     if word[i] == first and i < len(__UpperCAmelCase) - 1 and word[i + 1] == second:
                           new_word.append(first + second)
                           i += 2
                     else:
                           new_word.append(word[i])
                           i += 1
               lowercase_     			=   tuple(__UpperCAmelCase)
               lowercase_     			=   new_word
               if len(__UpperCAmelCase) == 1:
                     break
               else:
                     lowercase_     			=   get_pairs(__UpperCAmelCase)
         lowercase_     			=   " ".join(__UpperCAmelCase)
         if word == "\n  </w>":
               lowercase_     			=   "\n</w>"
         lowercase_     			=   word
         return word


   def __UpperCAmelCase							( self					:						List[str] ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						Union[str, Any]=False)   ->       Any:
         if bypass_tokenizer:
               lowercase_     			=   text.split()
         else:
               lowercase_     			=   self.moses_tokenize(__UpperCAmelCase ,		self.lang)

         lowercase_     			=   []
         for token in text:
               if token:
                     split_tokens.extend(list(self.bpe(__UpperCAmelCase).split(" ")))

         return split_tokens


   def __UpperCAmelCase							( self					:						Dict ,		__lowerCAmelCase					:						int)   ->       Dict:
         return self.encoder.get(__UpperCAmelCase ,		self.encoder.get(self.unk_token))


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


   def __UpperCAmelCase							( self					:						int ,		__lowerCAmelCase					:						Any)   ->       Tuple:
         lowercase_     			=   [t.replace(" " ,		"").replace("</w>" ,		" ") for t in tokens]
         lowercase_     			=   "".join(__UpperCAmelCase).split()
         # detokenize
         lowercase_     			=   self.moses_detokenize(__UpperCAmelCase ,		self.lang)
         return text


   def __UpperCAmelCase							( self					:						List[str] ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						int = None)   ->       List[int]:
         if token_ids_a is None:
               return [self.sep_token_id] + token_ids_a
         lowercase_     			=   [self.sep_token_id]
         return sep + token_ids_a + sep + token_ids_a


   def __UpperCAmelCase							( self					:						Any ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						Optional[int] = None ,		__lowerCAmelCase					:						Optional[int] = False)   ->       List[int]:
         if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__UpperCAmelCase ,		token_ids_a=__UpperCAmelCase ,		already_has_special_tokens=__UpperCAmelCase)
         # no bos used in fairseq
         if token_ids_a is not None:
               return [1] + ([0] * len(__UpperCAmelCase)) + [1] + ([0] * len(__UpperCAmelCase))
         return [1] + ([0] * len(__UpperCAmelCase))


   def __UpperCAmelCase							( self					:						Optional[Any] ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						List[str] = None)   ->       List[int]:
         lowercase_     			=   [self.sep_token_id]

         # no bos used in fairseq
         if token_ids_a is None:
               return len(token_ids_a + sep) * [0]
         return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]


   def __UpperCAmelCase							( self					:						List[str] ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						int = None)   ->       Tuple[str]:
         if not os.path.isdir(__UpperCAmelCase):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
         lowercase_     			=   os.path.join(
             __UpperCAmelCase ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
         lowercase_     			=   os.path.join(
             __UpperCAmelCase ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"])

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

         lowercase_     			=   0
         with open(__UpperCAmelCase ,		"w" ,		encoding="utf-8") as writer:
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,		key=lambda __lowerCAmelCase: kv[1]):
                     if index != token_index:
                           logger.warning(
                               F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                               " Please check that the tokenizer is not corrupted!")
                           lowercase_     			=   token_index
                     writer.write(" ".join(__UpperCAmelCase) + "\n")
                     index += 1

         return vocab_file, merge_file


   def __getstate__( self					:						Tuple)   ->       List[str]:
         lowercase_     			=   self.__dict__.copy()
         lowercase_     			=   None
         return state


   def __setstate__( self					:						Optional[int] ,		__lowerCAmelCase					:						Union[str, Any])   ->       Any:
         lowercase_     			=   d

         try:
               import sacremoses
         except ImportError:
               raise ImportError(
                   "You need to install sacremoses to use XLMTokenizer. "
                   "See https://pypi.org/project/sacremoses/ for installation.")

         lowercase_     			=   sacremoses










"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    A_ ( _lowerCAmelCase  :       Dict,  _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     _a     						=					RoFormerConfig.from_json_file(_lowerCAmelCase							)
     print(f'Building PyTorch model from configuration: {config}'							)
     _a     						=					RoFormerForMaskedLM(_lowerCAmelCase							)

     # Load weights from tf checkpoint
     load_tf_weights_in_roformer(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     torch.save(model.state_dict(),  _lowerCAmelCase,  _use_new_zipfile_serialization=_lowerCAmelCase							)


if __name__ == "__main__":
   __snake_case												=							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--bert_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained BERT model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   __snake_case												=							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)