




import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
    from .testing_utils import (
        floats_tensor,
        load_hf_numpy,
        load_image,
        load_numpy,
        load_pt,
        nightly,
        parse_flag_from_env,
        print_tensor_test,
        require_torch_a,
        require_torch_gpu,
        skip_mps,
        slow,
        torch_all_close,
        torch_device,
    )
    from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


lowerCamelCase_  						=       get_logger(__name__)



def      UpperCAmelCase_    (     __UpperCamelCase     ):







    if version.parse(__UpperCamelCase     ) < version.parse(__UpperCamelCase     ):
        if "dev" in min_version:
            SCREAMING_SNAKE_CASE__       =(
                """This example requires a source install from HuggingFace diffusers (see """
                """`https://huggingface.co/docs/diffusers/installation#install-from-source`),"""
            )
        else:
            SCREAMING_SNAKE_CASE__       =f"""This example requires a minimum version of {min_version},"""
        error_message += f""" but the version found is {__version__}.\n"""
        raise ImportError(__UpperCamelCase     )












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,)






