import os
import json
import regex as re
from typing import Dict, List, Optional, Tuple, Union

from transformers import PreTrainedTokenizer
from transformers.utils import logging

logger = logging.get_logger(__name__)

class AksharaTokenizer(PreTrainedTokenizer):
   """
   Akshara tokenizer for processing Indic language text.
   This tokenizer handles characters at the akshara (syllable) level.
   """
   
   vocab_files_names = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
   model_input_names = ["input_ids", "attention_mask"]
   
   def __init__(
       self,
       vocab_file,
       merges_file=None,
       unk_token="<unk>",
       bos_token="<s>",
       eos_token="</s>",
       pad_token="<pad>",
       mask_token="<mask>",
       add_prefix_space=False,
       **kwargs
   ):
       super().__init__(
           unk_token=unk_token,
           bos_token=bos_token,
           eos_token=eos_token,
           pad_token=pad_token,
           mask_token=mask_token,
           add_prefix_space=add_prefix_space,
           **kwargs,
       )
       
       # Load vocabulary
       with open(vocab_file, encoding="utf-8") as vocab_handle:
           self.encoder = json.load(vocab_handle)
       self.decoder = {v: k for k, v in self.encoder.items()}
       
       # Load merges if available
       self.merges = {}
       if merges_file is not None and os.path.isfile(merges_file):
           with open(merges_file, encoding="utf-8") as merges_handle:
               merges = merges_handle.read().split("\n")
               self.merges = {tuple(merge.split()): i for i, merge in enumerate(merges) if merge}
       
       # Special token handling
       self.add_prefix_space = add_prefix_space
       
       # Pre-compile regex patterns for tokenization
       self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
   
   @property
   def vocab_size(self):
       return len(self.encoder)
   
   def get_vocab(self):
       return dict(self.encoder, **self.added_tokens_encoder)
   
   def _tokenize(self, text):
       """Tokenize text into akshara units."""
       if self.add_prefix_space and not text.startswith(" "):
           text = " " + text
       tokens = re.findall(self.pat, text)
       return tokens
   
   def _convert_token_to_id(self, token):
       """Convert a token to its ID in the vocabulary."""
       return self.encoder.get(token, self.encoder.get(self.unk_token))
   
   def _convert_id_to_token(self, index):
       """Convert an ID to its token in the vocabulary."""
       return self.decoder.get(index, self.unk_token)
   
   def convert_tokens_to_string(self, tokens):
       """Convert a sequence of tokens to a single string."""
       text = "".join(tokens)
       text = text.replace(" ", "").replace("▁", " ").strip()
       return text
   
   def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
       """Build model inputs from a sequence by appending eos_token_id."""
       if token_ids_1 is None:
           return [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
       return [self.bos_token_id] + token_ids_0 + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
   
   def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
       """Get list where entries are [1] if a token is special and [0] otherwise."""
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
           )
       
       if token_ids_1 is None:
           return [1] + ([0] * len(token_ids_0)) + [1]
       return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
   
   def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
       """Create a mask from the two sequences for sequence classification tasks."""
       eos = [self.eos_token_id]
       bos = [self.bos_token_id]
       
       if token_ids_1 is None:
           return len(bos + token_ids_0 + eos) * [0]
       return len(bos + token_ids_0 + eos) * [0] + len(token_ids_1 + eos) * [1]
   
   def save_vocabulary(self, save_directory, filename_prefix=None):
       """Save the vocabulary and merges files to a directory."""
       if not os.path.isdir(save_directory):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory")
           return
       
       vocab_file = os.path.join(
           save_directory, (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
       )
       
       with open(vocab_file, "w", encoding="utf-8") as f:
           f.write(json.dumps(self.encoder, ensure_ascii=False))
           
       return (vocab_file,)


# Register the tokenizer with the AutoTokenizer class
from transformers import AutoTokenizer
AutoTokenizer.register("akshara", AksharaTokenizer)

# Register the model configuration if needed
from transformers.models.auto.configuration_auto import CONFIG_MAPPING
if "akshara" not in CONFIG_MAPPING:
   from transformers import PretrainedConfig
   
   class AksharaConfig(PretrainedConfig):
       model_type = "akshara"
   
   CONFIG_MAPPING.register("akshara", AksharaConfig)