




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
    from .image_encoder import PaintByExampleImageEncoder
    from .pipeline_paint_by_example import PaintByExamplePipeline












import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
    from .tokenization_deberta_va import DebertaVaTokenizer
else:
    lowerCamelCase_  						=       None

lowerCamelCase_  						=       logging.get_logger(__name__)

lowerCamelCase_  						=       {"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase_  						=       {
    "vocab_file": {
        "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"
        ),
        "microsoft/deberta-v2-xxlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"
        ),
    }
}

lowerCamelCase_  						=       {
    "microsoft/deberta-v2-xlarge": 512,
    "microsoft/deberta-v2-xxlarge": 512,
    "microsoft/deberta-v2-xlarge-mnli": 512,
    "microsoft/deberta-v2-xxlarge-mnli": 512,
}

lowerCamelCase_  						=       {
    "microsoft/deberta-v2-xlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False},
}
class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   str     =  VOCAB_FILES_NAMES
     _A    :   Union[str, Any]     =  PRETRAINED_VOCAB_FILES_MAP
     _A    :   Optional[int]     =  PRETRAINED_INIT_CONFIGURATION
     _A    :   Any     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A    :   Optional[int]     =  DebertaVaTokenizer




     def __init__(     self      :			List[Any]    ,_UpperCamelCase      :			Tuple=None    ,_UpperCamelCase      :			str=None    ,_UpperCamelCase      :			Any=False    ,_UpperCamelCase      :			List[Any]=False    ,_UpperCamelCase      :			Any="[CLS]"    ,_UpperCamelCase      :			Optional[int]="[SEP]"    ,_UpperCamelCase      :			int="[UNK]"    ,_UpperCamelCase      :			int="[SEP]"    ,_UpperCamelCase      :			Tuple="[PAD]"    ,_UpperCamelCase      :			Optional[Any]="[CLS]"    ,_UpperCamelCase      :			Optional[int]="[MASK]"    ,**_UpperCamelCase      :			int    ,)		->       None:


         '''simple docstring'''





         super().__init__(
             _UpperCamelCase    ,tokenizer_file=_UpperCamelCase    ,do_lower_case=_UpperCamelCase    ,bos_token=_UpperCamelCase    ,eos_token=_UpperCamelCase    ,unk_token=_UpperCamelCase    ,sep_token=_UpperCamelCase    ,pad_token=_UpperCamelCase    ,cls_token=_UpperCamelCase    ,mask_token=_UpperCamelCase    ,split_by_punct=_UpperCamelCase    ,**_UpperCamelCase    ,)

         SCREAMING_SNAKE_CASE__       =do_lower_case
         SCREAMING_SNAKE_CASE__       =split_by_punct
         SCREAMING_SNAKE_CASE__       =vocab_file
         SCREAMING_SNAKE_CASE__       =False if not self.vocab_file else True




     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			int    ,_UpperCamelCase      :			str=None						)		->       Dict:


         '''simple docstring'''





         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         SCREAMING_SNAKE_CASE__       =[self.cls_token_id]
         SCREAMING_SNAKE_CASE__       =[self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep




     def   __A    (     self      :			List[str]    ,_UpperCamelCase      :			Any    ,_UpperCamelCase      :			List[Any]=None    ,_UpperCamelCase      :			Union[str, Any]=False						)		->       str:


         '''simple docstring'''





         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_UpperCamelCase    ,token_ids_a=_UpperCamelCase    ,already_has_special_tokens=_UpperCamelCase						)

         if token_ids_a is not None:
             return [1] + ([0] * len(_UpperCamelCase						)) + [1] + ([0] * len(_UpperCamelCase						)) + [1]
         return [1] + ([0] * len(_UpperCamelCase						)) + [1]




     def   __A    (     self      :			Union[str, Any]    ,_UpperCamelCase      :			List[str]    ,_UpperCamelCase      :			str=None						)		->       int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =[self.sep_token_id]
         SCREAMING_SNAKE_CASE__       =[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   __A    (     self      :			int    ,_UpperCamelCase      :			str    ,_UpperCamelCase      :			Optional[str] = None						)		->       Tuple[str]:


         '''simple docstring'''





         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                 """tokenizer."""						)

         if not os.path.isdir(_UpperCamelCase						):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
             return
         SCREAMING_SNAKE_CASE__       =os.path.join(
             _UpperCamelCase    ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

         if os.path.abspath(self.vocab_file						) != os.path.abspath(_UpperCamelCase						):
             copyfile(self.vocab_file    ,_UpperCamelCase						)

         return (out_vocab_file,)






