import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


UpperCAmelCase							   =      logging.get_logger(__name__)

UpperCAmelCase							   =      {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase							   =      {
    """vocab_file""": {
        """microsoft/layoutlm-base-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt"""
        ),
        """microsoft/layoutlm-large-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """microsoft/layoutlm-base-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json"""
        ),
        """microsoft/layoutlm-large-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCAmelCase							   =      {
    """microsoft/layoutlm-base-uncased""": 512,
    """microsoft/layoutlm-large-uncased""": 512,
}

UpperCAmelCase							   =      {
    """microsoft/layoutlm-base-uncased""": {"""do_lower_case""": True},
    """microsoft/layoutlm-large-uncased""": {"""do_lower_case""": True},
}






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						Tuple 			=   VOCAB_FILES_NAMES
   UpperCamelCase_				:						Dict 			=   PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase_				:						str 			=   PRETRAINED_INIT_CONFIGURATION
   UpperCamelCase_				:						int 			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase_				:						Any 			=   LayoutLMTokenizer
   def __init__(    self							,     lowerCAmelCase_=None							,     lowerCAmelCase_=None							,     lowerCAmelCase_=True							,     lowerCAmelCase_="[UNK]"							,     lowerCAmelCase_="[SEP]"							,     lowerCAmelCase_="[PAD]"							,     lowerCAmelCase_="[CLS]"							,     lowerCAmelCase_="[MASK]"							,     lowerCAmelCase_=True							,     lowerCAmelCase_=None							,     **lowerCAmelCase_							,     )	->Dict:
        super().__init__(
            lowerCAmelCase_							,     tokenizer_file=lowerCAmelCase_							,     do_lower_case=lowerCAmelCase_							,     unk_token=lowerCAmelCase_							,     sep_token=lowerCAmelCase_							,     pad_token=lowerCAmelCase_							,     cls_token=lowerCAmelCase_							,     mask_token=lowerCAmelCase_							,     tokenize_chinese_chars=lowerCAmelCase_							,     strip_accents=lowerCAmelCase_							,     **lowerCAmelCase_							,     )

        a  :    str				=						json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
        if (
            normalizer_state.get('''lowercase'''							,     lowerCAmelCase_    ) != do_lower_case
            or normalizer_state.get('''strip_accents'''							,     lowerCAmelCase_    ) != strip_accents
            or normalizer_state.get('''handle_chinese_chars'''							,     lowerCAmelCase_    ) != tokenize_chinese_chars
        ):
             a  :    Optional[int]				=						getattr(lowerCAmelCase_							,     normalizer_state.pop('''type'''    )    )
             a  :    List[Any]				=						do_lower_case
             a  :    List[Any]				=						strip_accents
             a  :    Dict				=						tokenize_chinese_chars
             a  :    List[Any]				=						normalizer_class(**lowerCAmelCase_    )

        a  :    str				=						do_lower_case
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_=None    )	->Optional[int]:
        a  :    Dict				=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
             output += token_ids_a + [self.sep_token_id]

        return output
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = None    )	->str:
        a  :    int				=						[self.sep_token_id]
        a  :    int				=						[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    ) * [0] + len(token_ids_a + sep    ) * [1]


   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = None    )	->List[Any]:
        a  :    Union[str, Any]				=						self._tokenizer.model.save(lowerCAmelCase_							,     name=lowerCAmelCase_    )
        return tuple(lowerCAmelCase_    )
# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    """--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"""
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/