
import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
				import ctypes
				import msvcrt  # noqa






				class 				SCREAMING_SNAKE_CASE__			(							ctypes.Structure			):

											"""simple docstring"""






											_snake_case   						=     [('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]




def 		A_       (							)					->			Dict:



						'''simple docstring'''

						if os.name == "nt":
												__UpperCamelCase        =	CursorInfo()
												__UpperCamelCase        =	ctypes.windll.kernelaa.GetStdHandle(-11       )
												ctypes.windll.kernelaa.GetConsoleCursorInfo(__A       ,					ctypes.byref(__A       )       )
												__UpperCamelCase        =	False
												ctypes.windll.kernelaa.SetConsoleCursorInfo(__A       ,					ctypes.byref(__A       )       )
						elif os.name == "posix":
												sys.stdout.write('''\033[?25l'''       )
												sys.stdout.flush()




def 		A_       (							)					->			Any:



						'''simple docstring'''

						if os.name == "nt":
												__UpperCamelCase        =	CursorInfo()
												__UpperCamelCase        =	ctypes.windll.kernelaa.GetStdHandle(-11       )
												ctypes.windll.kernelaa.GetConsoleCursorInfo(__A       ,					ctypes.byref(__A       )       )
												__UpperCamelCase        =	True
												ctypes.windll.kernelaa.SetConsoleCursorInfo(__A       ,					ctypes.byref(__A       )       )
						elif os.name == "posix":
												sys.stdout.write('''\033[?25h'''       )
												sys.stdout.flush()




@contextmanager
def 		A_       (							)					->			Dict:



						'''simple docstring'''

						try:
												hide_cursor()
												yield
						finally:
												show_cursor()










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_ : int    		=       {
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : int    		=       ["DebertaTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[str]    		=       [
        "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DebertaForMaskedLM",
        "DebertaForQuestionAnswering",
        "DebertaForSequenceClassification",
        "DebertaForTokenClassification",
        "DebertaModel",
        "DebertaPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Any    		=       [
        "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDebertaForMaskedLM",
        "TFDebertaForQuestionAnswering",
        "TFDebertaForSequenceClassification",
        "TFDebertaForTokenClassification",
        "TFDebertaModel",
        "TFDebertaPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
    from .tokenization_deberta import DebertaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_deberta_fast import DebertaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deberta import (
            DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            DebertaForMaskedLM,
            DebertaForQuestionAnswering,
            DebertaForSequenceClassification,
            DebertaForTokenClassification,
            DebertaModel,
            DebertaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deberta import (
            TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDebertaForMaskedLM,
            TFDebertaForQuestionAnswering,
            TFDebertaForSequenceClassification,
            TFDebertaForTokenClassification,
            TFDebertaModel,
            TFDebertaPreTrainedModel,
        )


else:
    import sys

    snake_case_ : Dict    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






